orefice antonella tesi

orefice antonella tesi
University of Bologna “Alma Mater Studiorum”, Naples “Federico II” and “Roma III”,
in convention with the “Istituto Nazionale di Geofisica e Vulcanologia”
Ph.D Thesis in Geophysics
XXIV Cycle
04/04 - GEO/10
Year 2012
Refined Estimation of
Earthquake Source Parameters:
Methods, Applications and Scaling Relationships
Ph.D. Thesis of: Antonella Orefice
Coordinator
Prof. Michele Dragoni
Tutor
Prof. Aldo Zollo
2
Table of contents
Acknowledgments ............................................................................................................... 7
Introduction.......................................................................................................................... 8
Section A Seismic source theory ................................................................................. 10
1.
The representation theorem ............................................................................... 10
2.
Kinematic description of seismic source ........................................................... 11
2.1
Seismic spectrum to the high frequency ............................................................ 13
Section B Frequency domain ....................................................................................... 15
Chapter 1 Modeling of displacement spectra .................................................................... 15
1.1
Spectral model ................................................................................................... 15
1.1.1
Source spectrum S0(ω) ....................................................................................... 15
1.1.2
Path attenuation model Q(ω) ............................................................................. 17
1.1.3
Site transfer function R(ω)................................................................................. 18
1.1.4
Instrumental response curve I(ω)....................................................................... 20
1.2
From spectral to seismic parameters.................................................................. 24
Chapter 2 Multi-step inversion of displacement spectra ................................................... 26
2.1
Inversion strategy: iterative, multi-step approach ............................................. 26
2.1.1
Influence of noise .............................................................................................. 28
2.2
Introduction to inverse theory............................................................................ 31
2.3
Non linear inverse problem ............................................................................... 34
2.4
The Levenberg-Marquardt algorithm ................................................................ 37
Chapter 3 Applications ...................................................................................................... 42
3.1
Resolution test: Laviano sequence..................................................................... 42
3.2
Scaling laws ....................................................................................................... 50
3
3.2.1
Data collecting and processing .......................................................................... 50
3.2.2
P- and S- path attenuation .................................................................................. 54
3.2.3
P- and S- site transfer functions ......................................................................... 57
3.2.4
Seismic moment, source radius and static stress drop ....................................... 59
3.2.5
Moment and local magnitude ............................................................................ 63
3.3
Conclusion ......................................................................................................... 64
Section C Time domain ............................................................................................... 65
Chapter 4 Empirical Green Function's (EGF) Approach ................................................... 65
4.1
Introduction........................................................................................................ 65
4.2
Theory of EGF analysis ..................................................................................... 66
4.3
Projected Landweber method ............................................................................ 67
4.4
Conditions of applicability EGF method's ......................................................... 69
4.4.1
Difference in magnitude between master event and EGF: synthetic test .......... 70
4.4.2
Processing of near-source data .......................................................................... 74
Chapter 5 Source parameters from RSTFs ........................................................................ 77
5.1
Introduction........................................................................................................ 77
5.2
Effects of directivity: rupture duration and seismic moment ............................ 77
5.3
Corner frequency and rupture velocity .............................................................. 80
Chapter 6 Applications ...................................................................................................... 84
6.1
L’Aquila sequence ............................................................................................. 84
6.1.1
Mw 6.3 L’Aquila mainshock .............................................................................. 88
6.1.2
Cluster of events with Mw ≥ 3.0 ......................................................................... 94
6.2
Mw 2.9 Laviano mainshock .............................................................................. 100
6.3
Conclusions...................................................................................................... 103
4
Summary .................................................................................................................... 104
References........................................................................................................................ 106
5
6
Acknowledgments
This work would not have been possible without the help and encouragement of many
people and I would like to take this opportunity to express my thankfulness to them.
First of all I would like to thank my tutor Prof. Aldo Zollo for having followed me in
these years and for his valuable teachings. Thanks to him I learned to love research in
seismology and I understand how to work for and with a research group. So I take all the
researchers, post-docs and students
of “Laboratorio di RIcerca in Sismologia
Sperimentale e Computazionale” (RISSC-Lab) for their constructive comments, valuable
discussions and support. In particular, I thank Emanuela Matrullo, Simona Colombelli
and Claudio Martino who made this experience unforgettable.
I wish to express my profound gratitude to my family, and in particular my parents,
Annunziata and Alfonso, for all they have given me in life and for encouraging me to
pursue my education.
Finally, I would like to thank my boyfriend (soon my husband) Gianni for encouraging
me during our life together.
Thanks to everyone!
7
Introduction
The large majority of microearthquake source parameter estimations now available in
literature have been obtained from measurements performed in the frequency domain.
The seismic moment, for example, is derived from the low frequency level of
displacement spectra and the rupture length (radius of circular ruptures) is obtained from
the spectral corner frequency, according to kinematic and dynamic source models (Brune,
1970; Madariaga, 1976). The analysis of the source parameters is often complicated by
their spectral properties at high frequencies, where path and site effects are not easily
distinguished from the source characteristics. One way to overcame this problem is to use
the Empirical Green Functions (EGFs) that allow to represent the contribution of
propagation and site effects to signal without using approximate velocity models (e.g.,
Mori and Frankel, 1990; Hough, 1997; Ide et al., 2003; Abercrombie and Rice, 2005).
The method requires two earthquakes having a similar hypocenter and focal mechanism
but different size. The smaller earthquake, preferably 1 to 2 magnitudes smaller than the
other, will act as a medium transfer function. Assuming that the path, site, and instrument
effects are the same for both earthquakes, the time deconvolution of the two earthquakes
will give the relative source time functions (RSTFs) of the larger earthquake at each
considered station. The durations of each RSTF are then examined to retrieve some
interesting properties regarding the extent and the rupture velocity of the event. They are
essential to obtain accurate estimates of the source size and therefore of corner frequency.
Thus, in the frequency domain, where the seismic moment is estimated by low-frequency
level of displacement spectra, one major issue to be afforded is the adequate correction of
observed ground motion for path attenuation and site response effects. Different
approaches can be used which can be classified as parametric when both seismic source
and attenuation models are selected a-priori (e.g., de Lorenzo et al., 2010; Edwards et al.,
2008) or as non-parameteric when data are analyzed to infer the source properties and
attenuation models (e.g., Prieto et al., 2004; Bindi et al., 2006).
In this study we estimate the source parameters using a parametric approach,
based on a physical description of the different effects which modify the signal radiated
by the seismic sources.We use observations in time and/or frequency domain in order to
8
estimate the model parameters accounting for source, path attenuation and site effects.
Moreover we apply the deconvolution method of Vallèe (2004) to calculate the RSTFs
and to get accurate estimates source size and rupture velocity.
This thesis is divided in three sections:
Section A. We will give a brief review of the theory of seismic source, starting
from the representation integral. This theorem is the basis of the argoments explained in
the next sections: in section B it will be used in the frequency domain, while in section C
we will discuss its applicability in the time domain.
Section B. In this section the spectral model used to estimate the source
parameters in the frequency domain will be explained, as well as the multi-step, nonlinear inversion strategy. To test the iterative multi-step procedure the resolution test on
Laviano sequence (Southern Italy) is applied. Finally we will discuss about the scaling
relationships of source parameters which have been estimated for a database with local
magnitude [0.1:3.7] both from P- and S-wave signals.
Section C. In the third section we will focus on the time domain deconvolution
method of EGFs. We will first outline the physical constraints on the RSTFs. Then, we
will describe in detail how to compute the deconvolution. Moreover we will discuss the
advantage of using RSFs to obtaine accurate estimates of source parameters and
information on rupture process. In conclusion the applications of EGFs method are
shown for large, moderate and small events.
9
Section A Seismic source theory
1.
The representation theorem
The representation theorem is a formula for the ground displacement, at general point in
space and time, in terms of the quantities that originated the motion: these are body
forces and/or applied tractions over surface of the elastic body.
In a kinematic description of the seismic source, the theory that links the source and
propagation is
the representation
theorem:
the
displacement u
generated by
a
discontinuity across a internal surface Σ has components:
[
]
+∞
r
r
u i ( x , t ) = ∫ dτ ∫∫ u j (ξ , t ) C jkpq Gip ,q ( x , t ; ξ ,τ )v k dΣ(ξ )
−∞
Σ
(1.1)
where Cjkpq is a tensor of an elastic constant, Gip,q are the Green’s functions and vk is the
r
normal to the surface Σ. The function Gip ,q ( x , t ; ξ ,τ ) represents the effect of the
propagation of elastic waves through the medium.
The only case in which the Green's function can be written explicitly is when the medium
is homogeneous, isotropic and unlimited, and its expression is given by:
Gip (ξ ,τ ; x, t ) =
1
4πρ
(3γ γ
i
p
− δ ip )
1 rβ
1
1 
r
t ' δ (t − τ − t ') +
γ γ δ t −τ − 
3 ∫r α
2 i p
r 
α
r
4πρα
(γ i γ p − δ ip ) 1 δ  t − τ − r
−
2
r 
β
4πρβ
1



(1.2)
where γ is the unit vector pointing from the source ξ to the receiver x and r = |x - ξ|. α and
β are the P- and S-wave velocity, respectively, ρ is the medium density. The term that is
attenuated with distance as 1/r3 is said near field while the terms that are attenuated as 1/r
are called as far field.
From the representation theorem and the expression of the Green’s function in a
homogeneous, isotropic and unlimited medium, some theoretical tools used in the course
10
of work will be discussed.
2.
Kinematic description of seismic source
The far field condition is equivalent to the Fraunhofer condition for linear optics (L <<
λ). If the distance between source and station is much greater than the linear dimension
of the fault Σ, we can assume that both distance r and the direction cosines γi do not
depend on the coordinates on the fault plane. Substituting the terms of equation (1.2) in
the far field representation theorem we obtain the expressions for the displacement
associated with P and S phase:
u iP =
γi
r

C jkpq γ p γ q v k n j ∫∫Σ ∆u& ξ , t − dΣ
3
α
4πρα r0

δ − γ iγ n

r
u = in
C jkpq γ q v k n j ∫∫Σ ∆u&  ξ , t − dΣ
3
β
4πρβ r0

(1.3)
S
i
where
u j (ξ , t ) = n j ⋅ ∆u (ξ , t )
(1.4)
The scalar function ∆u (ξ , t ) is the source function. Since in general ∆u& (ξ , t ) can change
quickly in time and space, the delay in the integrands of (1.2) must include variations of r
with ξ. The factor r0-1 is based on the distance of the receiver from each point r0 of the
fault. Equation (1.3) allows to obtain in a very simple way the characteristics of motion
of seismic waves in the far field condition. From relationships γiγi = 1 and γi(δi - γiγp) = 0
it is clear that the motion of particles hit by a P wave (particle motion) is perpendicular to
the fault plane (parallel to the normal γ), while it is parallel for those hit by a wave S. The
amplitude of the wave is attenuated as the inverse of the distance and it is inversely
proportional to the cube of wave velocity. Then the amplitude of S-waves is a factor
(α/β)3 greater than amplitude of P-waves. The factor C jkpq γ p γ q v k n j represents the
radiation pattern of P-waves, determined by the orientation of the plane (vk), the direction
of the discontinuity of displacement (nj) and the direction of the station relative to the
fault (γp). Similarly if we consider the vectors γ’ and γ’’ orthogonal to surface
11
perpendicular to γ, the amplitude of the radiation of issued S waves is equal to
C jkpq γ 'p γ q v k n j in the direction γ ‘ and C jkpq γ 'p' γ q v k n j in the direction γ '' . The shape of
the displacement for the P and S waves is described by a term that expresses the time
dependence; this has the form:

x −ξ
Ω(x , t ) = ∫∫ ∆u&  ξ , t −
c
Σ


dΣ(ξ )


(1.5)
where c is the velocity of propagating wave. Developing in Taylor series the expression
of distance between the receiver located in x and source dΣ x − ξ and neglecting the
terms of order higher than 1 we get:
r ≈ r0 − (ξ ⋅ γ )
(1.6)
Replacing (1.6) in (1.5) we obtain:
r −(ξ ⋅ γ ) 

Ω(γ , t ) = ∫∫ ∆u& ξ , t − 0
 dΣ
c
Σ


(1.7)
whose Fourier transform is:
Ω(γ , t )e
−
iωr0
c
= ∫∫ ∆u& (ξ , ω )e −iω (ξ −γ c ) dΣ
(1.8)
Σ
The right side has the form of a double Fourier transform in space, expressed by:
∫∫ ∆u& (ξ , ω )e
Σ
− i (ξ ⋅k )
dΣ = f (k )
(1.9)
If the transform was known for all k in the space of wave numbers, it would be possible
to reverse the double integral and determine ∆u& (ξ , ω ) as a function of ξ completely, from
12
far-field observations. Unfortunately the Fourier transform is not known for all wave
numbers k but only for the projection of ωγ/c on Σ. Then the range of unsearchable wave
numbers is restricted to k parallel to Σ and |k ≤ ω/c|. It follows that it is not possible to
study the details of seismic source at length scales smaller than the shortest observed
wavelength.
2.1
Seismic spectrum to the high frequency
When the frequency ω is close to zero, the Fourier spectrum Ω(x,ω) of the far field
displacement tends to a constant value:
Ω(x, ω → 0 ) = ∫∫ ∆u& (ξ , t → 0 )dΣ
(1.10)
Σ
because
u& (ξ , ω → 0) = ∫ ∆u& (ξ , t ) exp(iωt )dt
(1.11)
∆u& (ξ , ω → 0) = ∫ ∆u& (ξ , t )dt = ∆u (ξ , t → ∞ )
(1.12)
Ω(x, ω → 0 ) = ∫∫ ∆u (ξ , ω → ∞ )dΣ
(1.13)
and moreover
So we obtain that:
Σ
Then Ω(x,ω → 0) tends to the integral of the final slip on the fault plane. In other words,
the far-field displacement spectrum at low frequencies tends to a constant value
proportional to the seismic moment which is defined as:
M 0 = µu A = µ × average dislocation × Area of the fault
13
This result is true for any function of the dislocation on fault plane and asserts that the
spectral trend at low frequencies is independent of the details of the process that led to
the final value of dislocation. If the area of the fault surface is infinitesimal and the
dislocation varies as a step in time, the far-field waveform is a Dirac delta function and
then the spectrum is flat in the whole frequency range.
14
Section B Frequency domain
Chapter 1 Modeling of displacement spectra
1.1
Spectral model
The earthquake displacement spectrum can be described by the relationship:
U (ω ) = S 0 (ω )Q(ω )R(ω )I (ω )
(1.14)
where U (ω ) is the observed ground motion displacement spectrum, S 0 (ω ) is the source
spectrum, Q(ω ) is the path attenuation model, R(ω ) is the site transfer function, and
I (ω ) is the instrumental response.
Now we will see in details these functions and their characteristics.
1.1.1
Source spectrum S0(ω)
S0 is the source spectrum, which includes the frequency-independent radiation pattern
and the geometrical spreading factors:
S 0 (ω ) = C S
Ω0
ω
1 + 
 ωc



γ
(1.15)
Ω0 is low-frequency spectral level (ω << ωc) (related to seismic moment M0), ωc is the
corner frequency (related to source radius, r) (Fig. 1.1) and
CS =
c
Rϑϕ
FS
4πρc 3 R
(1.16)
15
where R is the hypocentral distance, c is the S- or P-wave velocity, ρ is the medium
c
density, Rϑϕ
is the radiation pattern coefficient, and FS is the free-surface coefficient
(=2). The above equations assume that the propagation medium can be described by a
uniform velocity model. γ is a constant that control the shape of the spectrum curvature
around the corner frequency.
In order to account for direct P- and S-wave amplification due to a vertically varying
velocity structure, we replaced the constant in (1.16) with the more general expression
(Aki and Richards,1980):
C =
'
S
c
Rϑϕ
FS
(1.17)
4πρ h1 2 c h5 2 c10 2 R '
where the sub-scripts h and o are for density and velocity values at the hypocenter and
receiver depths, respectively. The geometrical spreading R’ is estimated for a linear
variation of velocity with depth (Ben-Menhaem and Singh, 1981):
R' =
ρ 0 c0
R
ρ h ch
(1.18)
Moment magnitude
Log | u (ω)|
ωc
Log ω
Frequency (Hz)
Figure 1.1: Left: theoretical variation of spectral amplitude in homogeneous medium as function of
frequency. Right: example of observed displacement spectrum. The dashed line represents the theoretical
spectrum f1(x).
16
1.1.2
Path attenuation model Q(ω)
Q(ω) is the function which accounts for the anelastic body-wave attenuation along the
travel path:
Q(ω ) = e −ωt
∗
(1.19)
with t*=T/Q is the attenuation parameter, depending on the travel time and the quality
factor, which can be constant or frequency dependent.
The quality factor Q is defined by the relation
1
∆E
=−
Q(ω )
2π E
(1.20)
where the second member is the fraction of energy (energy variation / total energy)
dissipated in a cycle by a wave that propagates in a anelastic medium. Under this
definition, highly attenuating media are characterized by small values of Q, and
conversely, high values of Q correspond to weakly attenuating media.
In the most general formulation of the anelastic attenuation model, the coefficient t* in
equation (1.19) is frequency-dependent, thus it can be written as:
t * (ω ) =
T
Q0ω n
(1.21)
where n is a positive real number and Qo is the quality factor evaluated at a reference
frequency, often fixed to 1 Hz (e.g. Morozov, 2008).
It has been shown that the quality factor Q has to depend on the frequency in
order to satisfy the causality requirements (Aki-Richards, 1980). However, the same
authors state that the attenuation law can be chosen to make Q effectively constant over
the seismic frequency range. This is also the result of Azimi et al. (1968) which proposed
a Q model which depends on frequency but is constant in the seismic frequency range.
This result is more or less equivalent to that inferred by Kjartansson (1979), even if its
17
mathematical development could be partially questioned (in fact he was unable to derive
the time domain expression of the impulse response).
Based on these theoretical developments, many studies have assumed that Q does
not depend on the frequency in the typical frequency range of recorded waveforms. It is
worth note that an important reason for the difficulty of assuming a frequency dependent
Q model is that if Q is frequency dependent, for instance through a power law, then a
strong dependence of body wave velocities on frequency should be inferred from the
analysis of seismic data, whereas only exceptionally a dispersion relationship for P waves
has been inferred.
The data selection is critical in terms of physical quantities (acceleration, velocity
or displacement) and in terms of analyzed seismic phase, that is, P-wave, S-wave,
surface wave or coda waves. In fact, due to the different frequency content, each of the
listed phases can lead to a different result on the behavior of Q. However, particularly for
body waves, the inability of accurately separate direct waves from secondary
contributions, can lead to controversial results.
Another problem to be faced in estimating the anelastic attenution properties of a
study area concern its intrinsic correlation with source paramters and, in particular, the
corner frequency and high frequency spectral fall-off γ. In fact γ is responsible of decay
of spectrum to high frequency as well as the parameter t* correlated with corner
frequency and seismic moment. To high values of t* (small values of Q) corrispond small
values of γ. This means that a robust strategy has to be adopted to reduce the correlation
between t*/Q and (M0, fc).
1.1.3
Site transfer function R(ω)
Site response functions represent the station-specific effect on the record.
The term R(ω) in equation (1.14) is the site transfer function that is a generally unknown
function and, in the present study, has been determined by an iterative procedure. In fact,
as detailed in the next chapter, the site transfer functions for P- and S-waves are
determined through an iterative procedure based on the computation of displacement
spectra residuals and stack at each receiver site.
There are several definitions of site term in literature, for example:
18
Edwards et al. (2008) define the site transfer function for station j as
(
T j ( f ) = A j a j ( f ) exp − π f k j f
a
),
where Aj is a frequency-independent correction
factor, κj is a constant site-related attenuation operator (e.g., Anderson and Hough,
1984), fa determines the frequency dependence of Q and aj(f) is the frequency-dependent
site amplification function that takes into account resonant frequencies due to the
layered, fractured subsurface (e.g., Steidl et al., 1996). In figure 1.2 the flowchart of
method used by Edwards et al. (2008) is shown.
The site transfer function Rj(f) defined by de Lorenzo et al. (2010) is given by
product of near site attenuation (described in terms of the kj attenuation factor)
K j ( f ) = exp(− π f k j ) and local site amplification Aj(f). Aj(f) is not described by a
particular mathematical relationship and depends on the elastic and geometrical
properties of the rocks near the recording site (e.g. Tsumura et al., 1996). Considering the
residual Res ij ( f ) = U ijobs ( f ) − U ijteo ( f ) , where U ijobs and U ijteo are the observed and
theoretical spectrum respectively, the site response Rj(f) at station j is obtained
minimizing, at each station, the quantity ∑ Res ij ( f ) − ln R j ( f ) . Ni is the number of
Ni
i =1
spectra available for the event i.
In Prieto et al. (2004), the source contribution is isolated by receiver contributions
to the spectra following the method described by Warren and Sheare (2002). This method
assume that the observed spectrum Dij(f) from each source Si and receiver Rj (denoted Si
for the ith earthquake and Rj for the jth station) is a product of source effects and pathsite effects. They iteratively stack all log spectra from each station, after removing the
source term Si, to obtain the path-station term Rj.
So attenuation and site responses are crucial parameters to obtain accurate estimates of
source parameters. Therefore, it is necessary to adopt a multi-step inversion method to
separate source, attenuation and site terms.
19
Figure 1.2: (from Edwards et al., 2008) A flowchart of the method used. From top to bottom: (1) the initial
spectral inversion, (2) t* estimates are then used to construct a Q model using a tomographic method, (3)
theoretical t* values are computed for each spectrum using the new Q model, (4) the spectral inversion is
repeated, this time fixing the theoretical t* value, and (5) finally, the signal moment is decomposed into
seismic moment, a site amplification term, and a geometrical decay value. Parameters in bold diamonds
indicate the final values of each parameter.
1.1.4
Instrumental response curve I(ω)
The function I(ω) is the response curve of the specific instrument recording the
earthquakes analyzed. For our analysis we consider the data recorded by Irpinia Seismic
Network (ISNet) (Weber et al., 2007), network developed with the aim of permanently
monitoring the Irpinia faults system in Southern Italy (Fig. 1.3).
Southern Apeninnes (Italy) are among the regions with highest seismic potential
in the Mediterranean area. They have been interested by large earthquakes with
magnitude up to M 7 generated as a consequence of a rather complex geodynamic which
produces an anticlockwise motion of the Italian Peninsula (Scandone et al., 1979). The
observed stress regime is mainly extensional (e.g., Montone et al., 2004) and, as a
consequence, the dominant fault mechanism is normal, although there have been some
20
(e.g., May 5, 1990, Potenza, M 5.4; October 31–November 1, 2002, Molise, M 5.4)
strike-slip earthquakes, the origin of which are still debated (Fracassi and Valensise,
2003; Valensise et al., 2003). The last destructive seismic event occurred in the area was
the November 23,1980 M 6.9 Irpinia earthquake which caused severe damage and about
3,000 deaths. The November 23, 1980 M 6.9 Irpinia earthquake was characterized by a
complex normal fault mechanism involving three fault segments which ruptured in three
distinct episodes 20 seconds apart (0s, 20s and 40s) with a total seismic moment of
18x1018 Nm (Bernard and Zollo, 1989).
ISNet network is composed by 30 stations covering an area of about 100 × 70
km2. It is organized in “sub-nets”, each of them composed by a maximum of seven
seismic stations and managed by a data concentrator (LCC, Local Control Center). All of
the stations are equipped with a strong-motion accelerometer (Guralp CMG-5T) and a
three-component velocimeter (Geotech S-13J), with a natural period of one second, thus
ensuring a high dynamic recording range. Moreover, five stations host broad-band 40 s
velocimeter for a better recording of regional and teleseismic events (Nanometrics
Trillium 40S). The full recording dynamic range is ±1g, and the sensitivity is sufficient to
record Mw 1.5 events at a distance of more than 40 km and down to Mw 0.2 at smaller
distances.
Figure 1.3: Green squares indicate seismic stations. Yellow lines symbolize wireless radio links between
each seismic station and its nearest Local Control Center (LCC, blue circles). Gray lines represent higher
bandwidth, wireless connections among LCCs and the Network Control Center (red star). The latter
transmission system is conceived as a redundant double ring.
21
Data acquisition at the seismic stations is performed by an innovative data-logger
produced by Agecodagis, the Osiris-6 model (http://www.agecodagis.com).
In figure 1.4 the overall (sensor + data logger) instrumental response curve I(ω) for
accelerometer Guralp CMG-5T, velocimeter Geotech S-13J and velocimeter Trillium
40S. We can observe that all curves present a cut-off high frequency of about 50 Hz. So
we cannot obtain in frequency domain estimates of corner frequency fc ≥ 50 Hz.
The data are carefully corrected for instrument response such that I(ω)=1.
22
Figure 1.4: Overall (sensor + data logger) instrument frequency bandwidth for accelerometer Guralp CMG5T, velocimeter Geotech S-13J and velocimeter Trillium 40S.
23
1.2
From spectral to seismic parameters
In this paragraph we will see the parameters that can be calculated by estimation of
spectral parameters, that is spectral amplitude and corner frequency.
Given the estimation of spectral parameter Ω0 and ωc from the inversion of
displacement spectra, the source parameters seismic moment M0 and source radius r can
be estimated through the formulae:
M 0 = C S' Ω 0
r = kc ⋅
c
fc
(1.22)
(1.23)
where c is the P- or S-wave velocity and fc is the corner frequency (fc = ωc/2π ). kc is a
coefficient which depends on the adopted circular rupture model and wave type, e.g.,
assuming the Madariaga (1976)’s model kP = 0.32 for P-waves and kS=0.21 for S-waves,
while according to the Brune (1970)’s model kS = 0.37.
The seismic moment and radius of a circular fault rupture from equations (1.22) and
(1.23) are used to estimate the static stress drop (Keilis-Borok, 1959):
∆σ = µ
7π
7 M0
u=
16
16 r 3
(1.24)
where µ is the rigidity and u is the average earthquake slip.
Since the tern r3 in equation (1.24) is high, small errors associated with it will produce
large errors in the determination of the stress drop. If we replace the area of the fault (S =
π r2) in equation (1.24), we obtain:
M0 =
16∆σ 3 2
S
7π 3 2
(1.25)
or, considering the logarithm,
24
log M 0 =
3
 16∆σ 
log S + log 3 2 
2
 7π 
(1.26)
From this equation it follows that, if the stress drop is constant for all earthquakes, then
logS is proportional to ⅔ logM0. It has been shown empirically that this assumption is
valid for a wide range of magnitude (Kanamori and Anderson, 1975). For moderate and
large earthquakes (M> 5), ∆σ takes values in the range [1:10] MPa and an average value
of 6 MPa (Fig. 1.5).
From estimates of seismic moment M0 the moment magnitude is calculate through the
relationship (Hanks and Kanamori, 1979):
Mw =
2
(LogM 0 − 9.1)
3
(1.27)
where M0 is expressed in N·m.
The advantage of the Mw scale is that it is clearly related to a physical property of the
source and it does not saturate for even the largest earthquakes.
Figure 1.5: The relation between the fault area S and the seismic moment M0 with lines of constant stress
drop (Udias, 1999).
25
Chapter 2 Multi-step inversion of displacement spectra
This chapter provides a description of the inversion procedure used in this study and the
principles of the theory of inversion. First a parametric modeling approach combined
with a multi-step, non-linear inversion strategy will be described. It is based on the
physical description of the different source, path attenuation and site effects which
modify the signal radiated by seismic sources. Then a description of inverse theory is
made with a distinction between global and local search methods. Finally, it will be given
a more detailed description of the inversion method used in the thesis to estimate
the source
parameters, that
is the
method
of
Levenberg - Marquardt. This is
a
linearized inversion method combining the Hessian and gradient descent.
2.1
Inversion strategy: iterative, multi-step approach
We have adopted an iterative, multi-step approach for the inversion of P- and Sdisplacement spectra; in this approach source, attenuation and site response models are
determined by applying progressive corrections for attenuation and site effects to the
source spectral function.
Using the theoretical model in (1.14) we first estimated the attenuation parameter tij∗ and
the constant γ and after the spectral parameters Ω ij0 and ω cij (i and j are indexes of the
event and station respectively) by a non-linear best-fitting method applied to the
observed displacement spectra. Specifically, we applied the non-linear LevenbergMarquardt least-square algorithm (Marquardt, 1963), implemented in the software
package GNUPLOT (Janert, 2009), for curve fitting and parameters estimation.
Assuming that γ and t ij* follow a unimodal distribution with mean values equal to <γ
> and < t*> respectively, we applied the iterative procedure shown in the flowchart of
figure 1.2 , i.e.:
1.
assuming <γ>=2 as initial guess, the spectral parameters Ωij, ω cij , t*ij are
estimated;
2.
fixing the values of Ωij and ω cij at the event-average estimates, the parameter t*ij
is estimated. In this step we obtain new values of t*ij ≡ t*ijNEW. In order to be consistent
26
with the relationship between seismic moment and moment magnitude (Mw∝LogMo)
(
(Hanks and Kanamori, 1979), for Ω ij0 the geometric mean Ω i0 = Π Mj=i1Ω ij0
)
1
Mi
has been
Mi
computed while for ω cij the arithmetic mean ω ci = ∑ ω cij M i has been computed (where
j =1
Mi is the number of stations that have recorded the event i).
3.
fixing the value of t*ij at the mean value <t*ijNEW> the displacement spectra are
inverted for Ωij, ω cij , γij. So, new values of γij for each displacement spectra are estimated
and its mean value is denoted as <γNEW>. Assuming <γ>=<γNEW>, the procedure is
iterated until no changes in mean values of t*ij and γ are observed.
The iterative multi-step procedure should converge to a stable value of the average
quantities, since t* and γ are related only to that part of displacement spectrum
with frequencies greater then corner frequency ω cij ; furthermore t* is independent of γ
for frequencies less than ω cij .
By fixing the best values of γij and tij∗ for each pair station-event obtained from the
previous procedure, the site transfer functions R(ω) for P- and S-waves are determinated
through an iterative procedure. For each station j the site transfer function Rj(ω) is
obtained from the average of the transfer functions inferred from each event i recorded at
the station j :
R j (ω ) =
U ij (ω )
1 j
∑
N j i =1 S oij (ω )Qij (ω )
(2.1)
where Nj is the number of earthquakes recorded by the j-th station and U ij (ω ) is the
observed P- or S-wave displacement spectrum for the event i. The same equation is
applied for P- and S-wave displacement spectra, so that two site transfer functions
specific for the analyzed seismic phase are retrievable. It is worth to note that the site
transfer functions obtained from equation (2.31) account for both the constant and
frequency dependent site amplification/attenuation effect and for the differences in the
instrumental response between accelerometers and velocimeters.
27
The source spectrum S 0 j (ω ) for the event i, is obtained from equation (1.15), using the
event average values Ω i0 and ω ci defined above and the values of γij for each pair stationevent. The attenuation spectrum Qij (ω ) is obtained from equation (1.29) using the
parameter tij∗ obtained from the second step of final iteration as described above.
In order get more refined estimations of the source spectrum, the observed P- and S-wave
displacement spectra are then corrected for the estimated site response and attenuation
functions :
U ijSC (ω ) =
U ij (ω )
Qij (ω )R j (ω )
(2.2)
where U ijSC (ω ) is the site and attenuation corrected displacement spectrum. By fixing the
mean value of t* (obtained from the iterative procedure described above), the
displacement spectra U ijSC (ω ) are therefore inverted for γij, Ω ij0 and ω cij to get new
estimations of the spectral parameters Ω ij0 and ω cij .
The procedure to estimate the site response function can be iterated by recomputing the
site functions Rj(ω) with equation (2.1), using the updated event source models. The
iterative procedure is stopped when 1) the overall spectral misfit does not change
significantly, 2) the retrieved average source parameters do not change significantly
2.1.1
Influence of noise
As we can see from the first part of the flowchart (Fig. 2.1), after the correction of
displacement spectra for the overall instrument curve, the signal to noise S/N ratio is
calculated in the whole range of frequency. The noise N is calculated in a 2.56-second
time window before the arrival of P-wave. The signal is calculated in a 2.56-second time
window around the manual P/S pick starting 0.25 s before the pick P/S.
The S/N ratio is used as a weighting factor in the inversion procedure.
In fact, the noise in the data introduces high‐frequency oscillations in the displacement
spectra, masking the corner frequency and the low frequency spectral amplitudes. To
overcome this problem we "weigh" the amplitudes in the displacement spectra rather
28
than selecting a band to the inversion in which the signal‐to‐noise ratio is less than a
priori fixed threshold value. In figure 2.2a the displacement spectrum of an event with
moment magnitude Mw=0.5 is shown. We can observe that for frequency less of 6 Hz the
S/N assumes low values, so this means the signal is strongly contaminated by noise. The
noise is responsible of artificial low frequency plateau that give us an incorrect estimate
of spectral amplitude and therefore of seismic moment. To avoid this problem, for events
with local magnitude ML ≤ 1.5 the minimum frequency in the inversion is set equal to 6
Hz (Fig. 2.2b), while for events with ML > 1.5 it is set equal to 0.5 Hz. In figure 2.2c an
example of fit between the observed and calculated displacement spectrum of event with
Mw = 3.5 is shown, together with the signal to noise ratio.
Moreover in the inversion procedure only the records with mean value of S/N less of 2
are selected to calculate the displacement spectra. In this way we impose that the
spectrum of noise and of signal are dissimilar.
29
Figure 2.1: Flow chart of iterative multi-step approach.
30
(a)
(b)
(c)
Figure 2.2: Fit between observed (black line) and calculated (dashed line) displacement spectra for event
with Mw=0.5 in the range of frequency [0.5-50] Hz (a) and [6-50] Hz (b). The same is shown for an event
with Mw=3.5 (c). In all graphics, the signal to noise ratio is also shown (grey line).
2.2
Introduction to inverse theory
Inverse theory is an organized set of mathematical techniques used for reducing data to
obtain useful information about the physical world on the basis of inferences drawn from
observation. Observations of
physical
quantities
(measures)
are
the data. It
is
assumed that there is a specific method, usually a mathematical theory or model, that
relates the model parameters to the data. Inverse theory addresses the reserve problem:
starting with data and a general principle or model, it determines estimates of the model
parameters (Fig. 2.3).
31
FORWARD PROBLEM
MODEL
PARMETERS
MODEL
PREDICTION
OF DATA
MODEL
ESTIMATES OF
MODEL
PARAMETERS
INVERS PROBLEM
DATA
Figure 2.3: Outline of the forward and inverse problems.
Note that the role of inverse theory is to provide information about unknown numerical
parameters to be used into the model, not to provide the model itself.
The starting point in the definition of inverse problems is the description of the data. In
general, the data are a set of numerical values and, therefore, a vector provides a
convenient way for their representation. If N measurements are made in a particular
r
experiment, you can consider them as the elements of a vector d of dimension N:
r
T
d = [d1 , d 2 , d 3 ,..., d N ]
(2.3)
where T denotes the transpose.
r
Similarly, the model parameters can be represented as the elements of a vector m ,
whoselength is M:
r
T
m = [m1 , m2 , m3 ,..., mM ]
(2.4)
In general, the relationship between data and model is represented by one or
more implicit equations of the type:
f1 (d , m) = 0
f 2 (d , m) = 0
(2.5)
M
f L (d , m) = 0
32
where L is the number of equations.
So we can write:
r r r
f ( d , m) = 0
(2.6)
These equations summarize what is known about the relationship between measured data
and model parameters (unknowns).
The purpose of inverse theory is therefore to solve, or “invert”, these equations to
derive the model parameters from the data available.
There are three types of inverse problems: linear, non linear and linearized
inverse problems.
The linear inverse problems are problems where it is possible to separate the data
from the model parameters and to obtain linear equations with respect to the data, for
which (2.6) can be written as:
r r r
r
r
f ( d , m) = 0 = d − G ⋅ m
(2.7)
Thus:
r r
G⋅m = d
(2.8)
where G is a M x N matrix.
r
If we denote by e the vector whose elements are the errors on the data, then
equation (2.8) becomes:
r
r r
d = G⋅m + e
Indicating
with
r
m esr
the
vector whose components are
(2.9)
the
estimates
of
parameters obtained by inversion, we can write:
r
r
m est = G − g ⋅ d
(2.10)
33
where G-g is the matrix called generalized inverse.
By introducing equation (2.9) in (2.10) we get:
r
r
r
m est = G − g Gm + G − g e
(2.11)
The matrix G-gG ≡ R is called the resolution matrix.
Equation (2.11) can be written as:
(
)
r
r
r
m est = m + G − g G − I + G − g e
(2.12)
r
r
If the estimated model is equal to the true model ( m esr = m ), each parameter will
(
be estimated independently. In equation (2.12) the operator G − g G - I
component of
the estimated parameter
different components
of
the
matrix resolution coincides
of
vector is
a
linear
vector of true parameters
with the
identity
matrix I,
)
means that any
combination of
r
m.
If
the
all
parameters are
well resolved. The last term in equation (2.12) describes the effect of measurement
errors on parameter
estimation.
These errors are
determined from data
errors. In
fact, when data are uncorrelated and all have variance σ d J , the standard deviation of
the estimated parameter σ mi , resulting from the propagation of data errors, is given by:
(
σ m2 = ∑ Gij− g σ d
j
)
(2.13)
j
In
the linearized problem we
assume
that, locally, around a
trial
solution,
the
relation data-parameters is approximated by a linear relationship.
In the non-linear inverse problems the data and parameters of the model are linked by
non-linear relationships. As will be seen in the next section, to solve these problems we
can proceed following a linearization approach or by using optimization techniques.
2.3
Non linear inverse problem
The nonlinear problems are solved by a direct exploration of the cost function, defined
as a measure of the difference between observed and predicted data. The search of
34
the absolute minimum of the cost function E(m) is made difficult by the presence
of secondary minimum (Fig. 2.4) (Menke, 1989).
These non-linear inversion’s methods can be divided into two main categories:
global search methods that investigate the whole parameter space (e.g., genetic
algorithm, simulated annealing);
local search methods looking for the minimum of the cost function in around of a
trial solution (e.g., hill climbing methods, downhill simplex).
E(m)
(a)
maximum
local
minimum
global minimum
mest
E(m)
mtrue
Model parameter m
maximum
(b)
local minimum
global minimum
mest
mtrue
Model parameter m
Figure 2.4: If the trial solution is too far from the global minimum, the method may converge to a local
minimum (a) or to a maximum (b).
The Simulated Annealing is based on the analogy between the way in which a
metal cools and freezes at a minimum energy of the crystal structure (annealing process)
and the search for a minimum in a more general system (Davis, 1987).
The algorithm uses a random search which accepts not only those changes that lead to a
35
decrease of the function E, but also some changes that will lead to an increment of E. The
implementation of simulated annealing is relatively simple. It is necessary to give the
following “ingredients”: a representation of the possible solutions, a generator of random
variations of solutions, a method to evaluate the function E of the problem and
an annealing schedule, i.e. the
initial
temperature and
rules for E
decrease to
the
progression of research.
Regarding the genetic
algorithm, the
solution
to
the optimization
problem is obtained on the basis of an evolutionary process, based on the principle
of natural
selection developed
in
the Darwinian
theory
(Goldberg, 1989).
This
principle asserts that individuals with more adaptability to the environment leave on
average more numerous progeny. The basic properties necessary to carry out the
evolutionary process are: heredity (each individual carries the genetic characteristics that
have made it more suitable for the parent) and variability (different individuals must coexist in a different manner suitable to the environment, so that the natural selection can
act). Taking advantage of the terminology of genetics, chromosome is defined as a string
of
parameters
chosen to
chromosomes. Fitness, which
describe the
model and
expresses the
the
individual's
population is
a set
of
ability to adapt
to
its
surroundings, is connected to the value of the cost function: the search for the absolute
minimum of the cost function consists in the choice of the chromosome that, within
a given population, is characterized by the highest fitness.
The Hill Climbing search algorithm is an iterative algorithm that starts with an
arbitrary solution to a problem, then attempts to find a better solution by incrementally
changing a single element of the solution. If the change produces a better solution, an
incremental change is made to the new solution, repeating until no further improvements
can be found.
The Downhill Simplex, proposed by Nelder and Mead (1965), is a technique that
requires only to evaluate the cost function and its derivatives. A simplex is a geometrical
figure consisting, in N dimensions, N +1 points (or vertices) and by interconnect all
segments, polygonal faces, etc.. For example, in 2D the simplex is a triangle, a
tetrahedron in 3D, and so on. This method optimizes the cost function E making a series
of purely geometrical operations (reflections, expansions, contractions).
The optimization procedure is stopped if the vector distance covered in a cycle is a
36
fraction of a tolerance tol established a priori. Alternatively, it is possible to request that
the improvement of the minimum value of the function in the stopping step is a
fraction of a certain tolerance ftol established a priori.
2.4
The Levenberg-Marquardt algorithm
Let
us
consider N
points for a
model
parameters k, k = 1.2, ...., M. The prediction
characterized
by a
2
error or misfit (χ )
set of
is
M unknown
defined
as
the
difference between the observed data (d obs) and predicted data (d pre):
N  obs
r
d − d ipre 
χ (a ) = ∑  i


σ di
i =1 

2
2
(2.14)
where each measurement is weighted by the reciprocal of its variance. This function give
more weight to more accurate data. To minimize χ2 and estimate the parameters of bestfit it is possible to use an iterative process.
Selected some trial values for the
parameters, we evaluate the trial solution and the procedure is repeated until the variation
of χ2 is no longer significant. Assuming for χ2 a quadratic form, expanding in a Taylor
series around the minimum and stopping the expansion at second order we get:
r
χ 2 (a ) = χ 2 (a k ) + ∑
i
∂χ 2
1
∂2χ 2
ai + ∑
a i a j + .....
∂ai
2 i , j ∂a i ∂a j
r r 1r
r
≈ γ − d ⋅a + a ⋅D⋅a
2
(2.15)
where
γ ≡ χ 2 (a k )
r
d ≡ −∇χ 2
ak
[D] i, j =
∂2χ 2
∂a i ∂a j
ak
(2.16)
r
d is a M-vector and D is a M x M matrix.
If the approximation is a good one, we know how to jump from the current trial
r
r
parameters a cur to the minimizing ones a min in a single leap, namely:
37
[
r
r
r
a min = a cur + D −1 ⋅ − ∇χ 2 (a cur )
]
(2.17)
On the other hand, (2.15) might be a poor local approximation to the shape of the
r
function that we are trying to minimize at a cur . In that case, a new step is considered with
r
a new trial parameter a next
r
r
r
a next = a cur − costante ⋅ ∇χ 2 ( a cur )
(2.18)
To use equation (2.17) or (2.18) we must be able to calculate the gradient of the
function χ2 at any set of parameters. In particular, using equation (2.17), we need the
matrix D, which is the matrix of second derivatives of the error of prediction (Hessian
r
matrix) for each a . The matrix D is known because the form of χ2 is precisely
known. This allows us to use both relationships. The equation (2.18) is only used when
the equation (2.17) does not minimize the prediction error.
Let us see how to calculate the gradient and the Hessian of χ2.
Suppose that we have N points (xi, yi), for i = 1, ..., N. If the fitting model is
r r
y = y( x ; a )
(2.19)
the misfit function will be:
r 2
N
 y i − y ( xi ; a ) 
r

χ (a ) = ∑ 
σi
i =1 

2
(2.20)
r
where σi is the standard deviation associated with each point, and yi and y( xi ; a ) are
the observed and predicted model, respectively.
r
The gradient of χ2 compared to vector a is zero in correspondence of minimum and has
components:
r
r
N
[
y i − y ( xi ; a )] ∂y ( xi ; a )
∂χ 2
= −2∑
∂a k
∂a k
σ i2
i =1
k = 1,2,….,M
(2.21)
38
The mixed partial derivative is given by:
r
r
r
N
r ∂ 2 y ( xi ; a ) 
∂2χ 2
1  ∂y ( xi ; a ) ∂y ( xi ; a )
= 2∑ 2 
− [ y i − y ( x i ; a )]

∂a k ∂al
∂a k
∂al
∂al ∂a k 
i =1 σ i 
(2.22)
Then we put
1 ∂χ 2
βk ≡ −
2 ∂a k
1 ∂2χ 2
α kl ≡
2 ∂a k ∂a l
(2.23)
In this way [α] = ½ D and the equation (2.17) can be rewritten as the set of linear
equations:
M −1
∑α
l =0
kl
δal = β k
(2.24)
This set is solved for the increments δa l that, added to the current approximation, give
the next approximation.
The equation (2.18), the gradient descent formula, translates to:
δal = costante × β l
(2.25)
Note that the components of the Hessian matrix (Eq. 2.22) depend both on the first
derivative and on the second derivatives of the function with respect to their parameters.
Some treatments proceed to ignore the second derivative whose multiplicative term in
r
equation (2.22) is [yi – y(xi; a )]. For a good model, this term represents the random
error on the measurement of each point, and that error could be either positive or
negative.
It may
be generally
unrelated
to the
model
and then
the second
derivative term tends to zero when the sum of i is considerate.
The inclusion of the second derivative term can in fact be destabilizing if the model
reproduces the data badly, or if it is contaminated by outliers, then α kl can be defined
39
through the formula:
r
r
1  ∂y ( xi ; a ) ∂y ( xi ; a ) 
α kl = 2∑ 2 

∂a k
∂al 
i =1 σ i 
N
(2.26)
The condition of minimum for χ2, i.e. β k = 0 , is independent by matrix [α].
A
combination of
the Hessian method (Eq. 2.23) and
the
method
of
descent gradient (Eq. 2.25) is the method of Levenberg - Marquardt. It is based on
two elementary, but important observations. Let us consider the “constant” in the
equation (2.25). The
first
observation is
that
the
components of
the
Hessian
matrix provide information on the order of magnitude of the problem.
Equation (2.20) shows that the quantity χ2 is dimensionless. β k has the dimensions of
1/ak, whose unit of measure is cm-1, or kW, or any other measure. In fact, according to
the problem you are solving, each component of β k can have different dimensions. The
constant of proportionality between β k and δa k must therefore have a size of a k2 and the
only quantity that has this size is the reciprocal of the diagonal element, i.e. 1/αkk. So the
scale of constant must be assigned: so that this constant is not too large, it is divided by a
dimensional factor λ, with the the possibility to assign λ >> 1 to stop the step. So the
equation (2.25) is replaced by:
δal =
1
λα ll
βl
or
λα ll δal = β l
(2.27)
Is also necessary that all is positive, but this is guaranteed by the definition (2.26): this is
another reason for adopting this equation.
The second observation is that the equations (2.24) and (2.27) can be combined if a new
matrix α ' is defined:
α 'jj ≡ α jj ( 1 + λ)
α 'jj ≡ α jk
(j ≠ k)
(2.28)
40
and then replace both equations (2.24) and (2.27) with
M −1
'
∑ α kl δa l = β k
l =0
(2.29)
When λ is very large, the matrix α ' is reduced to diagonal element,
so
equation (2.29) becomes identical to equation (2.27). If λ instead tends to zero,
equation (2.29) becomes identical to equation (2.24).
From an operational viewpoint, given an initial set of parameters, the Marquardt
algorithm is based on the following steps:
a.
b.
c.
d.
compute χ2;
pick a modest value for λ, say λ = 0.001;
r
r r
solve the linear equation (2.29) for δa and evaluate χ2( a + δa );
r r
r
if χ2( a + δa ) ≥ χ2( a ), increase λ by a factor of 10 (or another substantial
factor) and go back to step c;
r r
r
e.
if χ2( a + δa ) ≤ χ2( a ), decrease λ by a factor of 10, update the trial solution
r
r
r
a ← a + δa and go back to step c.
41
Chapter 3 Applications
3.1
Resolution test: Laviano sequence
Here we propose a resolution test aimed at estimating the minimum moment magnitude
value above which source parameters can be effectively estimated. For this test we
consider a microearthquake sequence started on May 25, 2008 in Irpinia region, nearby
the village of Laviano, at about 800 m distance from the 1980 epicenter (Fig. 3.1). The
moment magnitude Mw and local magnitude ML of the events ranged from 0.8 to 2.9 and
from 0.3 to 2.7, respectively, with the largest magnitude earthquake occurring at the
middle of the sequence (Stabile et al., submitted to Scientific Reports).
We chose that sequence for two principal reasons. 1) The striking waveform similarity
and the coherence of the P-wave first motion polarity at different stations indicate that
events are co-located and share the same focal mechanism and site effects. As an
example of this strong likeness across all the events, band-pass filtered (1-20 Hz),
amplitude-normalized waveforms of the velocimetric vertical component records at
COL3, SNR3, and VDS3 stations for all microearthquakes in the sequence are shown in
figure 3.2. 2) The range of moment magnitude of these events covers the range of Mw of
the whole dataset.
The iterative multi-step approach described in Chapter 2 was applied to Laviano
sequence for S- waves. Five iterations were considered and for each iteration the
hypotheses of unimodal distributions for the high-frequency falloff rate γ and attenuation
parameter t ij* were tested. In fact in figure 3.3 the histograms of γ and t ij* obtained from
inversion of S- displacement spectra are shown; the inverse triangles represents the mean
values of γ and t ij* . The trade-off between attenuation parameter t ij* and corner frequency
ω cij is solved through the two inversions of displacement spectrum, that is, step1/ the
displacement spectra are inverted to estimate Ωij ω cij and t ij* ; step2/ fixing the values of
Ωij and ω cij at the event-average estimates in the step1, the displacement spectra are
inverted to estimate t ij* (flow chart 2.1). We can observe in figure 3.4 that in each
iteration at step2 the attenuation parameter and corner frequency are not correlate.
42
Moreover we have verified that any correlation between γ and ω cij / t ij* is introduced, as
shown in figure 3.5.
The iterative multi-step procedure converges to a stable value of the average quantities
< γ> and < t ij* > after the third iteration (Fig. 3.6). In table 1 the average of γ and t ij*
obtained from each iteration are listed.
In this test the site response functions Rij(ω) are not calculated, thus the
displacement spectra are not corrected for Rij(ω). But, ss mentioned above, the events are
co-located and share the same site effects. Figure 3.7 shows a log-log representation of
the corner frequency vs seismic moment for S-waves along with the associated
uncertainties. These parameters were obtained by step3 of fourth iteration (flow chart
2.1), where the average of γ and t ij* become stable and any correlation between γ / t ij* and
ωcij is observed. The constant stress drop lines at values 0.1 to 100 MPa are shown
(Kanamori and Anderson, 1975) in figure 3.7, where stress drop is estimated using the
Madariaga’s model for the earthquake rupture radius. A clear deviation from a selfsimilar scaling of the corner frequency is observed for seismic moments M0 smaller than
about 1011 Nm (Mw ≈ 1), above which the static stress drop ∆σ remains constant with a
value of (3.9 ± 2.2) MPa (red line). We verified that the events belong to the same
distribution with constant ∆σ by the χ2 statistical hypothesis test. The grey line indicates
the minimum values of seismic moment (and therefore moment magnitude) above which
we can obtain reliable estimates of source parameters, that is corner frequency and
seismic moment. The vertical arrows indicate corner frequencies greater than our
maximum resolution threshold, while the horizontal arrows indicate that the seismic
moments of these events are indeterminate. Then for S-wave the source parameters can
be effectively estimated from M0 ≥ 1011 Nm. By assuming that the estimates of seismic
moment obtained from P- and S- waves are the same, we can conclude that the resolution
threshold M0 ≥ 1011 Nm is also valid for P-waves.
43
Table 1: Average of high-frequency falloff rate γ and attenuation parameter t*ij
obtained from each iteration
Iteration
<γ>
< tij* >
1
1.76 ± 0.61
0.020 ± 0.010
2
1.56 ± 0.68
0.023 ± 0.010
3
1.52 ± 0.68
0.024 ± 0.011
4
1.51 ± 0.68
0.024 ± 0.012
5
1.51 ± 0.68
0.024 ± 0.012
Figure 3.1: Map of the May 25-28, 2008 microearthquake sequence located nearby the village of Laviano
(Southern Italy). The events of the sequence are extremely concentrated in a volume less than 300 m per
side and the swarm is about 800 m distance from the 1980 Irpinia earthquake epicenter. The fault plane
solution of the mainshock is consistent with the 1980 Irpinia earthquake fault plane. The dimension of
circles is the Madariaga’ circular rupture area of events while the color represents the event depth.
Horizontal location errors are also reported in the figure for each event. (Stabile et al., submitted to
Scientific Reports).
44
Figure 3.2: Vertical-component velocity records of the seismic sequence at (a) COL3, (b) SNR3, and (c)
VDS3 stations. The waveforms are band-pass filtered from 1 Hz to 20 Hz and are amplitude-normalized.
Events are ordered in time from below to above and the event number increases with the event origin-time.
Waveforms are aligned respect to the first P-wave arrival (Stabile et al., submitted to Scientific Reports).
45
γ
γ
γ
γ
γ
Figure 3.3: Distributions of high-frequency falloff rate γ and attenuation parameter
t * obtained from each
iteration. The inverse triangles represent the average of γ and t*.
46
Figure 3.4: Correlation between attenuation parameter t* and corner frequencies at step1 and step2 of
iterative multi-step procedure.
47
γ
γ
γ
γ
γ
γ
γ
γ
γ
γ
Figure 3.5: Correlation between high-frequency falloff rate γ and corner frequencies/attenuation parameter
t* at each iteration.
48
γ
Figure 3.6: Variation of average of parameters γ and t* estimated by each iteration.
49
Figure 3.7: Measured seismic moments and corner frequencies for S-wave and associated uncertainties.
Dashed lines refer to constant stess-drop values expressed in MPa. The red line indicates the mean value of
static stress drop, while the grey line indicates the minimum value of seismic moment above which we can
obtain reliable estimates of source parameters. Estimates of corner frequencies and seismic moment below
the resolution threshold are plotted with arrows.
3.2
Scaling laws
In this paragraph the relationships between the source parameters estimated through the
multi-step inversion procedure are shown.
3.2.1
Data collecting and processing
For the analysis proposed in the present study, the dataset collected by ISNet is further
extended and integrated by the inclusion of the closest stations of the Italian Seismic
Network, managed by Istituto Nazionale di Geofisica e Vulcanologia (INGV). The total
number of available three-component records is 25436, relative to 689 microearthquakes
with local magnitude ranging between 0.2 and 3.7 and located inside ISNet network (Fig.
3.8). These earthquakes were located using the code NLLoc (Lomax et al., 2000) and 1D
velocity model of Matrullo et al. (in preparation).
Among all the available recordings only those with accurate pick P/S are selected. In
50
fact, after associating a weight w at pick P/S based on the uncertainties on onset time
(Table 2), the following procedure is applied (Fig. 3.9):
a)
If there is manual pick P on vertical component with w ≤ 2, the relative horizontal
and vertical records are selected for the analysis, otherwise these components are
removed from the database.
b)
If there is manual pick S on horizontal component with w < 2, then this
component is considered for the calculation of displacement spectra.
c)
If the pick S does not exist or its weight is ≥ 2, the error on hypocentral distance
R is evaluated as:
σR = σRh 2 + σRx 2
(3.1)
where Rh and Rx are the errors along horizontal and vertical direction, respectively,
obtained from the location procedure.
If σR < 2 km, the theoretical arrival time Ts of S- phase is calculated as:
Ts = Tp +
R
Vp
 Vp

⋅ 
− 1
 Vs

(3.2)
where Tp is the manual pick P that satisfies the condition a), Vp and Vs are the velocity of
P and S waves, respectively. The ratio Vp/Vs is set equal to 1.85 and Vs=3 km/s (Matrullo
et al., in preparation); so Vp is equal to 5.6 km/s.
So in this case the horizontal component is used in the analysis.
If σR ≥ 2 km, the theoretical S-pick is not calculated and the analysis is done only for the
P-waves.
From this procedure an optimal database with accurate P and S manual picks is obtained.
This dataset is used to calculate the displacement spectra on which the multi-step
51
inversion procedure is applied. The signal is calculated in a 2.56-second time window
around the manual P/S pick starting 0.25 s before the pick P/S (Fig. 3.10). In order to
reduce distortions due to the windowing of the signals, a cosine taper function with a
fraction of tapering equal to 10% is applied to the P- and S-wave time series before
computing the amplitude spectrum. Next, an average moving window with a half width
of 3 points is used to smooth the spectra. Finally, the P- and S-displacement spectra from
velocity/acceleration time series are computed from integration/double-integration of
velocity/acceleration in frequency domain. The chosen frequency band for the spectral
analysis is 0.5-50 Hz and 6-50 Hz for events with local magnitude ML ≤ 1.5 and ML >
1.5, respectively, constrained by the data-logger, the overall (sensor + data logger)
instrumental response curve and by the relatively small signal-to-noise amplitude at low
frequencies.
Table 2: Weight associated to onset time P/S
Weight
Uncertainties on onset time (s)
0
<= 0.05
1
0.05 – 0.1
2
0.1 – 0.2
3
0.2 – 0.5
4
> 0.5
52
Figure 3.8: Map showing the microearthquakes analyzed in this study (grey circles) and stations of ISNet
and INGV networks (solid triangles). Events are plotted with a symbol whose size is proportional to the
magnitude. The three main rupture segment of the MS 6.9, 1980 Irpinia earthquake are also drawn as from
Bernard and Zollo, (1989).
Figure 3.9: Flow chart of waveforms processing to select the best manual pick P and S.
53
P
S
P
S
Figure 3.10: In the upper panel, yellow and red shaded areas identify the used time-windows for P- and Swave, respectively. A zoom of these area is shown in the bottom panels.
3.2.2
P- and S- path attenuation
In the present study, it is assumed that in the analyzed frequency band (0.5-50 Hz), the
parameter Q is not frequency-dependent. This hypothesis has been first verified through
two analyses (Zollo et al., 2011):
1.
Qualitative analysis. In particular, the displacement spectra of smallest events (ML
< 1.2) contained in the available dataset, at frequencies larger than the theoretical
corner frequency (> 25Hz), have been considered. With this assumption the source
spectrum can be assumed as constant and the recorded spectrum, in a log-lin
representation is reduced to a linear function of frequency. As an example, figure 3.11
shows displacement spectra, corresponding to three waveforms recorded at three
different stations, and five attenuation functions t*(ω) characterized by different
54
values of n (cfr equation (1.21)). From a qualitative analysis, it can be noted that the
case n=0, that is, the Q-constant model provides a better fit of the spectra with respect
to the other models.
2.
Statistical test. In order to obtain a more robust comparison, the differences in the
fit between the attenuation models have been statistically tested. In practice, assuming
the omega-square model, two different inversions have been carried out. In the first
one, the displacement spectra were inverted for determining the three parameters Mo,
fc and t* assuming a Q-constant model (which corresponds to assuming n=0 in
equation 1.21). In the second one, a frequency-dependent Q model has been assumed
for estimating the four parameters Mo, fc, t* and n. To establish which model best
reproduces the data, we estimated the variance of the residuals (E), that is a measure
of the discrepancy between the observed and theoretical displacement spectra. Finally,
the best-fit model has been discriminated by using the Akaike Information Criterion
(AIC) (Akaike, 1974). The criterion states that, among the best-fit models described
by a different number of parameters, the one that minimizes the following function
has to be selected: AIC=2Np + N[ln(2πE) + 1]. In this equation, Np is the number of
parameters used for modelling the displacement spectrum and the attenuation model
while N=Nt·Ns·Nc is the number of data. Specifically, Nt is the number of frequency
samples in each spectrum, Ns is the number of analyzed displacement spectra, and Nc
is the number of components (=1). As for the qualitative analysis, the results of the
statistical test indicated that the Q-costant model results in the minimum of the AIC
and has therefore to be considered as the best compromise between model simplicity
and adherence to data (Akaike, 1974).
After the study on the frequency dependence of quality factor, the iterative multistep procedure shown in figure 2.1 has been applied. We obtain for P- and S-wave that
< t ij* P > = (0.022 ± 0.014) s and < tij*S > = (0.026 ± 0.017) s, respectively. Figure 3.12
shows the distributions of values of t ij* .
55
Figure 3.11: Log-lin representation of scaled displacement spectra corresponding to S waveforms recorded
at 7 different stations (grey lines). Black lines correspond to five attenuation functions t*(ω) characterized
by different values of n (cfr equation (1.21)) used to test the frequency dependence of Q model against the
Q-constant model corresponding to n=0 (black continuous line). Note that the comparison has to be done in
the frequency range lower that 26 hz (black arrow) where, in the adopted representation the source spectra
is constant and the attenuation model dominates (Zollo et al., 2011).
Assuming an uniform anelastic attenuation model for the upper crust in the investigated
region of southern Apennines, the estimated average values for the P- and S-wave quality
factors are QP = 266 ± 254 and QS = 361 ± 287. Then the crustal QS is higher than QP.
According to laboratory measurements, a larger P- than S-wave attenuation,
corresponding to QS / QP > 1, is a marker for a partially fluid-saturated crust, while the
inverse (e.g. QS / QP < 1) is expected for dry or full-saturated rock layers, (Winkler and
Nur, 1979; Toksoz et al.,1979). This seismic attenuation behavior is analogous to that of
shear to compressional velocity ratio, with values of Vp/Vs ratio around 1.8 or slightly
larger, for partially fluid-saturated materials (Ito et al., 1979). For the analyzed area the
value of ratio Vp/Vs is included in the range 1.8-1.9 (Maggi et al., 2008; Matrullo et al.,
2011). Based on the mentioned results of laboratory measurements, we suggest that the
observations of relatively large values of the Vp/Vs and QS/QP ratios in the analyzed
region of southern Apennines, are the evidence for a highly fractured, partially fluidsaturated medium embedding the Irpinia fault zone, down to crustal depths of 15-20 km.
56
Figure 3.12: Distribution of parameter t* for P-wave (grey) and S-wave (black). The mean values of t* is
plotted as inverse triangle (black for S-wave and grey for P-wave).
3.2.3
P- and S- site transfer functions
Given the used recursive procedure for site and attenuation correction of displacement
spectra, the P and S site transfer functions account for all the effects which systematically
modify the spectral shape at a given receiver, including the instrument response, local
site geology, ambient noise and signal processing artifacts due to filtering or inadequate
base-line corrections. For this reason we expect that the P- and S- transfer functions
could be different at the same site given the different frequency content and signal-tonoise level. Figure 3.13 shows the S-site transfer functions at 8 stations of ISNet
network, obtained analyzing the signals recorded by velocity and accelerometer sensors.
Except at very low frequencies (f<1 Hz), the transfer functions obtained from
accelerometers and velocimeters look very similar. In each panel, the continuous lines
refer to the average transfer function obtained from all the earthquakes recorded at that
station while the dashed lines delimitate the 1-σ standard error.
57
Figure 3.13: S-wave site transfer functions at 8 stations of ISNet network, obtained analyzing the signals
recorded by both velocity (black lines) and accelerometer (grey lines) sensors. In each panel, the
continuous lines refer to the average transfer function obtained from all the earthquakes recorded at a given
station while the dashed lines delimitate the 1-σ standard error.
Almost all the sites show a constant level of amplification or attenuation with exception
of stations CLT3 and CMP3 showing at least one clear peak at frequencies around 5 Hz
with amplifications larger than 1.5. The presence of characteristic resonance peaks at
ISNet stations due to local site amplification effects have been also pointed by Cantore et
al., (2010) using H/V spectral ratio technique. P-wave transfer functions (not reported in
figure) show similar resonance peaks of S waves but, due to the higher frequency
content, a number of secondary peaks are also observed.
In order to verify the effect of having properly accounted for the attenuation model and
site transfer functions, we compared the displacement spectra corrected and uncorrected
for the attenuation and site functions. As an example, figure 3.14 shows the S-wave
displacement spectra of 4 earthquakes recorded at two stations of ISNet (CMP3 and
TE03) before and after the correction for path attenuation and site functions. It can be
noted that the correction affects spectra both at low and high frequencies. An average
variation of 0.25 unit in the moment magnitude can be noted at the two selected stations
together with a shift in the corner frequency values which is more effective for the
smallest earthquakes.
58
Figure 3.14: S-wave displacement spectra of 4 earthquakes recorded at two stations of the ISNet network.
Upper panel refers to the station TEO3 and lower panel refers to the station CMP3. For both the two
stations left panel refer to scaled spectra before the correction for the attenuation and site effect obtained
from the iterative procedure. Right panels refer to the same spectra corrected for the attenuation and site
effect and superimposed grey dots indentify the corner frequencies.
3.2.4
Seismic moment, source radius and static stress drop
Once we have obtained the spectral parameters low-frequency spectral level and corner
frequency, we can calculate the seismic moment, the source dimension and the stress
released by faulting.
In order to account for the results of resolution test, the scaling laws are shown only for
events with seismic moment M0 ≥ 1011 Nm. Moreover, only events recorded at a
59
minimum of 5 stations have been considered. Figure 3.15 shows a log-log representation
of the corner frequency vs seismic moment for S-waves (top panel) and P-waves (bottom
panel). The grey dots indicate the estimations of source parameters obtained for each
analyzed event, while the black/white circles with the associated uncertainties are
obtained by averaging the data grouped in a 0.3 logarithm of seismic moment bin. This
value of bin has been chosen by considering the average of uncertainty associated with
the logarithm of seismic moment. The constant stress drop lines at values 0.1 to 100 MPa
are shown in the same figure, where stress drop is estimated using the Madariaga’s model
for the earthquake rupture radius. We observe the self-similar scaling of the corner
frequency in the whole range of seismic moment for both P- and S-waves.
Figure 3.15: Log of corner frequency versus log of seismic moment for S-waves (top panel) and P-waves
(bottom panel). The black/white circles with the associated uncertainties are obtained by averaging the data
(grey dots) grouped in a 0.3 logarithm of seismic moment bin.
60
The earthquake source radius is then determined by the arithmetic mean of all the
available corner frequency estimates (ref. relation 1.23). Source radii decrease with
decreasing moment, confirming the self-similarity (Fig. 3.16). P- and S-wave estimates
of source parameters are very consistent, which is a further confirmation of the
robustness of such estimates.
We estimate the static stress drop ∆σ from the seismic moment and source radius
using the relation (1.24). Stress drops appears to be invariant with earthquake moment
(Fig. 3.17), with a value of (8.9 ± 2.0) MPa which corresponds to an average Brune’s
static stress drop value of (1.6 ± 0.4) MPa. A self-similar scaling of static stress drop has
been found in southern California by Abercombrie et al. (1995) from recording at depth
of 2.5 km Cajon Pass and Prieto et al. (2004) from Earth surface recordings of
microearthquakes by the Anza seismic network. In Central Apennines, Italy, in a
dominant normal faulting tectonic environment, several studies analyzed the source
parameter scaling relationships from the aftershock recordings of the 1997 UmbriaMarche seismic sequence. Using different modeling approaches and sub-sets of the same
data archive, Bindi et al., (2001) found a self-similar scaling of static stress drop (Brune’s
stress drop 2.6 MPa).
For a limited number of events, we compared P and S corner frequencies as
shown in figure 3.18. Here the black circles with the associated uncertainties are obtained
by averaging the data grouped in a 2 Hz S-corner frequencies bin. We find that the P
corner frequencies are systematically higher than those estimated for S waves from the
same earthquakes. The ratio fcP/fcS is about 2.0 ± 0.5, consistent within the error with the
model of Madariaga (1976). In fact, Molnar et al. (1973) and Madariaga (1976) present
source model with 1.5 < fcP/fcS < 1.73, whereas Savage (1974) and other argue that such a
sift is incompatible with Haskell-type source models and must result from attenuation.
Accounting for attenuation, in this study fcP > fcS and we can assert that the corner
frequency shift observed here is principally a source effect, as proposed by Hanks (1981).
61
Figure 3.16: Log of source radius versus log of seismic moment. Dashed lines show the constant stressdrop values expressed in MPa. The black/white circles with the associated uncertainties are obtained by
averaging the data (grey dots) grouped in a 0.3 logarithm of seismic moment bin.
Figure 3.17: Log of static stress drop versus log of seismic moment Dashed lines show the constant stressdrop values expressed in MPa. The black/white circles with the associated uncertainties are obtained by
averaging the data (grey dots) grouped in a 0.3 logarithm of seismic moment bin.
62
Figure 3.18: P-wave and S-wave measured corner frequencies for a limited portion of the analyzed dataset.
Dashed lines show different P/S corner frequencies ratio.
3.2.5
Moment and local magnitude
In figure 3.19 the values of moment magnitude Mw (calculated for S-wave) as a function
of local magnitude ML are shown. The values of local magnitude for earthquakes
recorded by the network ISNet were obtained from Bobbio et al. (2009). The data (grey
dots) are grouped in a 0.3 local magnitude bin (black circles). The black line represent
the line of best fit, while the dotted line corresponds to MW = ML. The resulting
relationship between moment and local magnitude is:
Mw = 0.63 (± 0.04) ML + 0.95 (± 0.09)
(3.3)
We observe a systematic underestimation of moment magnitude by local magnitude.
In theory, Mw and ML should provide the same value which means that
Mw =
2
(LogM 0 − 9.1) = M L
3
(3.4)
According to Deichmann (2006), due to inappropriate correction of instrumental and
63
attenuation effects, the local magnitude causes the underestimation of moment
magnitude.
Figure 3.19: Moment magnitude (MW) versus local magnitude (ML) relationship obtained from a best fit
analysis. The black circles with the associated uncertainties are obtained by averaging the data (grey dots)
grouped in a 0.3 ML bin. The dotted line corresponds to MW = ML.
3.3
Conclusion
Important findings can be summarized as follows:
1.
By the resolution’s test we obtained the minimum values of seismic above which we
can obtain reliable estimates of source parameters, that is M0 ≥ 1e11 Nm (Mw ≈ 1)
2.
Frequency-independent attenuation model: through statistical test we verified that
the constant-Q model has to be preferred to frequency dependent Q-models
3.
Earthquake self-similarity: we observed a constant stress-drop scaling of source
parameters. The average Madariaga’s static stress drop is about 8.9 MPa, which
corresponds to Brune’s stress drop of about 1.6 MPa. The ratio between P- and S- corner
frequencies is comparable with the theoretical value.
4.
We observed the discrepancy between local and moment magnitude: ML causes the
underestimation of Mw.
64
Section C Time domain
Chapter 4 Empirical Green Function's (EGF) Approach
4.1
Introduction
Knowledge of the seismic source requires modeling the propagation between the source
and the receiver (Green’s functions). Under the hypothesis of linear wave propagation,
the Green’s functions may also be replaced by the records of small earthquakes occurring
on the same fault with the same focal mechanism and the same stress drop, commonly
referred to as Empirical Green’s functions (EGFs).
As seen in the previous section, the analysis of the source parameters is often
complicated by their spectral properties at high frequencies, where path and site effects
are not easily distinguished from the source characteristics. One way to overcame this
problem is to determine the EGFs that consent to represent the contribution of
propagation and site effects to signal avoiding the use of approximate velocity models. In
fact, the displacement spectra of small events are characterized by high corner frequency
below than the source can be assimilated to spatial and temporal function delta of Dirac.
Then, in this range of frequency, the signal is the response of the medium to impulses in
the source region.
The use of small events as EGFs was first proposed by Hartzell (1978). It was
subsequently used and developed by Mueller (1985), Fukuyama and Irikura (1986), Mori
and Frankel (1990), Ammon et al. (1993), Velasco et al. (1994), Courboulex et al.
(1997a), and Ihmlè (1996). The idea is to deconvolve the mainshock from the smaller
event (EGF) to obtain a relative source time function (RSTF) at each considered station.
The durations of each RSTF are then examined to retrieve some interesting properties
regarding the extent and rupture velocity of the event.
In this chapter the deconvolution method (Vallée, 2004) is explained. It takes into
account various physical constraints of the RSTF to stabilize the deconvolution. The
method is based on the projected Landweber method, introduced in seismology by
Bertero et al. (1997), to which we have added an important constraint: the area of the
65
RSTF, which represents the scalar moment of the earthquake, has to remain the same at
all stations.
4.2
Theory of EGF analysis
By starting from the representation theorem, for a large earthquake of moment M1, we
can write:
(
) ( )
r r
r
r
r r
U i1 ( x , ω ) = − M pq Μ 1ik q Gip x , ξ 0 , ω ∫∫ f ξ , ω e − ik ⋅(ξ −ξ 0 )d 2ξ
(4.1)
S
where Gip denotes the spatial derivative of the Green function. Here we assume that 1)
the Green function Gip is the same for all the points of the fault except for a phase shift
r r r
k ⋅ ξ − ξ 0 due to the varying distance between source and receiver (far-field
(
)
approximation), 2) the earthquake has a constant mechanism. M is a unit tensor
r
r
r
independent of ξ and ω, f ξ , t , the inverse Fourier transform of f ξ , ω , is a causal,
( )
( )
positive scalar function, monotonically increasing over [0,D], where D is the unknown
duration of the source, and constant elsewhere (for more details see Vallée 2004).
For a smaller earthquake of scalar moment M0, with same location and similar focal
r
mechanism of large earthquake, f ξ , ω can be approximated by
( )
( ) (
(
)
r
r
δ ξ − ξ0
f ξ , ω = δ ξ − ξ 0 TF (H (t )) =
iω
r
r
r
)
(4.2)
where TF(H(t)) is the Fourier transform of the Heaviside function, which leads to
(
Μ
r
r r
U i0 ( x , ω ) = − M pq 0 ik q Gip x , ξ 0 , ω
iω
)
(4.3)
Therefore, by deconvolving equation (4.1) from equation (4.3), we obtain the RSTF,
defined as Fθ in the equations:
66
Fθ (ω ) =
( )
r r
r
Μ1
iω ∫∫ f ξ , ω e −ik ⋅(ξ −ξ 0 )d 2ξ =
Μ0 S
r
u
− iω ⋅ ⋅(ξ −ξ 0 )
r
Μ
v
= 1 iω ∫∫ f ξ , ω e φ
d 2ξ
Μ0 S
( )
r
(4.4)
r
where vΦ, the phase velocity and u the wave propagation direction, are assumed
constant. This assumption compels us to study separately each wave type in the EGF
analysis. The RSTF is a positive, bounded-support function and its duration will also
depend on the position of the station, the phase, and the rupture velocity but it will of
course remain bounded. Another important property of the RSTF is that its integral value
is independent of the stations or the wave type used in the deconvolutions and is equal to
the relative moment between the mainshock and the EGF.
4.3
Projected Landweber method
The deconvolution method of Vallée (2004) is based on the approach of Bertero et al.
(1997), who developed a simple method to include positivity and temporal constraints on
the RSTFs, based on the Landweber method. It was shown by Bertero et al. (1995) that
the latter method was slower but more accurate than conjugate gradient methods.
Called U1 and U0 the mainshock and EGF waveform, respectively, the problem is
to identify the RSTF Fθ verifying
||U0 * Fθ || - U1 = minimum
(4.5)
or equivalently
U0* * U0 * Fθ = U0* * U1
(4.6)
67
(e.g., Bertero, 1989), where U0* is the adjoint operator of U0. Through mathematical
manipulations, equation (4.6) can thus be written as
Fθ = Fθ +U0(-t) * (U1 - U0 * Fθ )
(4.7)
In an iterative scheme, the last equation becomes
Fθ(n+1) = Fθ n +τU0(-t) * (U1 - U0 * Fθ n )
(4.8)
where τ is the relaxation parameter which must satisfy the condition 0 < τ ≤
2/(supω|U0(ω)|)2 and is classically chosen equal to 1/(supx|U0(ω)|)2.
Let us suppose that we know that the RSTF belongs to some closed and convex set C.
Then equation (4.8) can be modified as follows:
Fθ(n+1) =Pc( Fθ n +τU0(-t) * (U1 - U0 * Fθ n ))
(4.9)
where Pc denotes the metric projection on C. In the absence of noise, Fn is shown to
converge, but only weakly, toward the expected solution of
||U0 * Fθ || - U1 = minimum, Fθ ∈ C
(4.10)
Bertero et al. (1997) defined C as the set of nonnegative causal functions that are zero for
t > D. However, we can be even more restrictive and let C be the set of nonnegative
causal functions that are zero for t > D and for which the integral over [0D] is equal to
M1/M0. It can be immediately verified that the newly defined set that we call Cm is
closed and convex. We now must define the projection PCm itself in order to compute
equation (4.9). Given a function h, it can be shown that PCm(h) can be naturally
68
computed, that is, we essentially add a proper, additive constant to h to derive PCm(h)
from h. it is shown that PCm is approximated by:


M ' − M1 M 0 

 if t ∈ [0 D ]
 P +  h(t ) + k
PCm h(t ) = 
α
D


0
elsewhere

(4.11)
where k is a positive real number.
Given PCm, the computation procedure is again completely as the one of Bertero
et al. (1997): we start from Fθ 0 = 0, compute equation (4.8) in the frequency domain, and
come back to the time domain to use PCm as defined by equations (4.9) and (4.11). We
then obtain Fθ
1
and repeat the operation, transforming into the frequency domain to
compute again equation (4.8) and so on. The scheme (4.9) is semiconvergent, that is, it
approaches the solution before diverging again. However, the minimum seems very flat,
and good results are obtained after a few hundred iterations.
4.4
Conditions of applicability EGF method's
As mentioned above, an empirical Green’s function is a recorded three-component set of
time-histories of a small earthquake whose source mechanism and propagation path are
similar to those of the master event. This definition requires that:
1.
we must find a smaller earthquake than the mainshock so that equation (4.2) is
verified. In reality, the small-event source time function has a finite duration, and
therefore a high-frequency-limited spectrum. This high-frequency limit is represented by
the corner frequency of the small event and corresponds to the maximum resolution that
we can obtain on the large-event rupture process.
To establish how much is the difference in magnitude between the EGF and master event,
a synthetic test was performed. It has been shown that the EGF optimal magnitude is
about 1 units smaller than the mainshock.
2.
The mechanism and location must be similar — in case of difference between both
events, it is possible to correct for these effects (Ihmlè, 1996), but it adds some
69
complexity to the procedure. Consequently, waves that radiate from the nucleation points
of the two events should cross exactly the same medium. In reality the two events are
slightly shifted in space, and a heterogeneity in the source region can be detected by only
one of the events. This is a restriction of the EGF method, but the resulting error is
smaller than the one that would result from using a calculated Green’s function.
3.
The mainshock must have a constant mechanism so that the Green’s function may be
assumed to be consistent over the whole source zone.
The conditions listed above are very important in particular for near source data. In fact,
for regional and/or telesismic data the differences in focal mechanism and location are
attenuated by large distances between seismic source and receiver, and so these
conditions can be considered negligible. As we will see in this chapter, to identify
potential
EGF
the near-source data processing provides several steps before
the deconvolution, i.e. 1) localization using the NLLoc code (Lomax et al., 2000), 2) the
calculation of focal mechanisms with FPFIT code (Reasenberg et al., 1985) and 3) the
study of the stability of the polarization for the optimal choice of range frequency to be
used in the deconvolution process.
4.4.1
Difference in magnitude between master event and EGF: synthetic test
To determine the difference in magnitude between the mainshock (main) and EGF,
synthetic seismograms were generated with a moment magnitude ranging between 0.5
and 3. We used the AXITRA code (Coutant, 1989) based on the discrete wavenumber
method (Bouchon, 1981) to generate the synthetic seismograms.
For each moment magnitude a different discretization of the fault plane was chosen
(Table 3), keeping fixed the distance D between the elementary sources to 9 m and with
strike, dip and rake respectively equal to 285°, 40° and -110°. It is assumed a unilateral
rupture with uniform velocity rupture vr=0.9·β and the velocity model used is model of
Amato and Selvaggi (1993). The hypocentral coordinates have been set equal to
40.7720°N, 15.3135°E and 17.25 km for all events generated by using the actual
geometry of the Irpinia Seismic Network (ISNet) (Fig. 4.1).
The deconvolution code of Vallèe (2004) was applied to the synthetic
70
seismograms. In particular, the following pairs main-EGF were considered:
Case 1. main of Mw=1 and EGF of Mw=0.5
Case 2. main of Mw=3 and EGF of Mw=2
Case 3. main of Mw=3 and EGF of Mw=1
Case 4. main of Mw=3 and EGF of Mw=0.5
For each pairs main-EGF the RSTF was determined for 21 stations in the S- and P- wave
time window and the misfit between the real mainshocks and the reconstitued mainshock
is evaluated. It is obtained by reconvolution of the RSTF with the EGF, as a function of
the allowed duration of the RSTF. This misfit is a good indicator of the quality of the
obtained deconvolution. The time at which the function becomes flat gives the simplest
(i.e., shortest) RSTF able to well describe the seismic source (Fig. 4.2).
As it can be seen from figure 4.3, since in case 1 the difference in magnitude is 0.5, we
are not able to distinguish the two events and therefore the misfit function is zero. In case
2 and 3, however, the misfit function becomes flat at τ = 0.1 s. This value represents the
optimal duration of RSTF. Increasing the difference in magnitude between the main
and the EGF (case 4), the accuracy in the estimation of the optimal duration increases. In
the latter case, τ is equal to 0.15 s.
So we can conclude that the minimum difference in magnitude between mainshock and
EGF is equal to 1. Figure 4.4 shows the RSTFs of the main for each station obtained in
the case 3. Therefore for Mw=3.0 we observe that the optimal duration is τ = 0.1 s. It is
essential to obtain accurate estimates of the source size.
Table 3: Discretization of fault for generated event with different moment magnitude
Mw
N° sources
0.5
2
1
3
2
10
3
21
71
Figure 4.1: Map showing the location of generated event (star) with different magnitude and the stations of
ISNet network.
Figure 4.2: Illustration of the deconvolution technique for S-wave at station AVG3 of ISNet network.
72
Case 1 – S phase
Case 2 – S phase
Case 1 – P phase
Case 2 – P phase
Case 3 – S phase
Case 3 – P phase
Case 4 – S phase
Case 4 – P phase
Figure 4.3: For each case the misfit between the real mainshocks and the reconstituted mainshock obtained
from reconvolution of the RSTF with the EGF, is shown as a function of the allowed duration of the RSTF.
Red dots represent the mean values of misfit curve obtained for each station.
73
Figure 4.4: Map of RSTFs for each station.
4.4.2
Processing of near-source data
As mentioned in previous paragraphs the EGF approach suffers from certain limitations
related to the selection of valuable Empirical Green Function, especially for small events.
To select the best EGF, the data processing includes the estimation of the event location
and the determination of the focal mechanism.
Considering only P-wave arrival time, first the NLLoc code (Lomax et al., 2000) has
been applied to each pair master event – EGF, previously selected according to the
difference in magnitude. After, the focal mechanisms were calculated using code FPFIT
(Reasenberg et al., 1985). Only the pair master event – EGF with similar location and
focal mechanism have been chosen. Finally, the study of stability of polarization is done
74
to chose the optimal range of frequency to use in the deconvolution process.
To set the low-frequency fmin it is necessary to know the corner frequency of
events pair. In fact, the low-frequency limit is represented by the corner frequency of
master event fcmain, i.e fmin << fcmain (Fig. 4.5). In this way also the condition (4.2) is
verified and so the source of EGF can be assimilated to spatial and temporal function
delta of Dirac. Then, in this frequency range, the signal of EGF represents the response
of the medium to impulses in the source region.
To set the high-frequency fmax we study the polarization. By applying different
range of frequency, the stability of polarization is observed and the fmax at which it
becomes stable in time is chosen as high-frequency limit. The polarization direction of
the wave velocity is determined directly from the diagrams since it coincides with the
azimuth of the motion associated with the first arriving wave. Figure 4.6 shows an
example of the diagram of polarization obtained by horizontal components sisE and sisN
for fmax = 10 Hz. This components are aligned according to the theoretical arrival time of
S-wave (red lines). In this case it is clear that until about 3.8 s the polarization is stable
indicating that 10 Hz is the optimal high-frequency limit.
This study also provides information on the time window to be selected from the
S phase in the deconvolution process. In fact, as we can see in figure 4.6, the angle of
polarization changes sharply after about 3.8 s indicating the presence of secondary phase.
To obtain reliable RSTFs it is necessary not to introduce several phase in the time
window. So in the case shown in figure 4.6 the optimal duration of time window to be
selected from S-phase is about 3 s.
Selected the range of frequency, the deconvolution method is applied to each pair
master event – EGF to obtain the RSTFs.
75
Moment magnitude
fcMASTER
fcEGF
Frequency (Hz)
Figure 4.5: Scheme to understand the choice of low-frequency limit fmin. The displacement spectra of
master event (red) and EGF (blue) are shown. The relative corner frequency are indicated. fmin must be
smaller than fminMASTER where the two signal are equal (blue area).
Figure 4.6: Example of stability of polarization. sisN and sisE are the horizontal components, while anaEN
represents the angle of polarization as function of time. The pink area indicates the time window in which
the polarization is stable.
76
Chapter 5 Source parameters from RSTFs
5.1
Introduction
As seen in the previous chapter, from the RSTFs it is possible to obtain the knowledge of
the seismic source without modeling the propagation between the source and the
receiver. The observables of RSTFs are:
Duration
Area
Shape
These give us accurate estimations of source radius, and so corner frequency, seismic
moment and rupture velocity. Moreover, the inversion of RSTFs allowed to constrain the
fault plane and provided us with the estimation of the slip distribution, the rupture
direction and the average velocity rupture. From estimates of source radius and seismic
moment the static stress drop can be calculated with equation (1.24) and the scaling laws
can be investigated.
Therefore, in this chapter the properties of RSTFs are illustrated and the relation
between the duration and corner frequency are reported.
5.2
Effects of directivity: rupture duration and seismic moment
The first-order effect we expect to see on a deconvolved source time function at a
particular station is directivity.
The source time function can be defined as the shape of the body-wave pulses
which are caused by the earthquake rupture. At distances beyond a few fault lengths, the
near-field effects are dominated by far-field effects, and so only these far-field terms are
considered in this case.
For a small earthquake, the fault is considered to be a single point source. As a
simple approximation, displacement on this fault can be considered to occur as a ramp
function. The source time function arising from a ramp time history on a single point
source is a box-car of length τr, which is the rise time of the ramp function. For finite
77
length faults, the rupture plane can be approximated as the summation of a number of
earthquake point sources that rupture with the appropriate time delays considering the
progressive rupture of the fault (Fig. 5.1). This simple line source is the Haskell Fault
Model.
∆x
epicenter
w
rupture front
L
Figure 5.1: Simplified fault geometry for fault of width w and length L, with unilateral slip. Rupture plane
is divided into sub-event slices of length ∆x.
Figure 5.2 shows a fault of length L, rupturing from left to right. If the distance to the
recording station is r (r >> L), then the arrival time of a ray from the beginning of the
faults is t=r/c, where c is the velocity of the wave type. The arrival time of waves from a
faulting segment at point x on the fault is given by:
tx =
x r − x cos θ
+
vr
c
(5.1)
Thus the difference in time between energy arriving from the end of the fault, at position
L and that arriving from the beginning of the fault can be used to define the time τc, the
duration of rupture for this unilateral case, as observed at the station at (r,θ):
78
L
τc =  +
 vr
r − L cos θ  r L L cos θ
− = −
c
c
 c vr
(5.2)
Then the rupture time depends on the viewing azimuth. This azimuth dependence due to
fault propagation is called directivity. If a station is located along the direction of rupture
propagation, θ = 0° and τc is short, especially in the case of the shear wave speed (c = β),
as vr is typically ≈ 0.8β. A station behind the rupture propagation (θ = 180°) has a long τc
and small amplitude. Stations located perpendicular to the rupture (θ = 90°) are not
affected by the directivity.
Figure 5.2: (from Clinton, 2004) Azimuthal dependency of arrival times, for fault plane rupturing from left
to right.
A scheme showing how source time functions are affected by a unilaterally rupturing
strike-slip fault is in figure 5.3.
The area under the time function is directly proportional to the seismic moment, which
must be independent of azimuth. In fact, the area of the RSTFs is equal to the moment
ratio between the mainshock and EGF and it must remain the same at all stations.
79
Figure 5.3: (from Clinton, 2004) Simplified azimuthal variations for source time functions in a unilaterally
strike-slip fault rupture. Note that the area under the source time function (proportional to the seismic
moment, M0) is constant, but the time, and the amplitudes, vary widely.
The simple Haskell line source representation that we have considered involves
unilateral rupture, or rupture in only one direction. For some earthquakes, unilateral
rupture is a sufficient model of the faulting process, but many earthquakes nucleate in the
center of a fault segment and spread in both directions. This known as bilateral rupture.
The source time function for bilateral rupture varies much less with azimuth, and it is
often impossible to distinguish bilateral rupture from a point source. Some faults appear
to expand radially, as circular rupture. This model was introduced by several authors
including Savage (1966), Brune (1970), and Keilis-Borok (1959) to quantify a simple
source model that was mechanically acceptable and to relate slip on a fault to stress
changes. Dislocation models such as Haskel’s model produce nonintegrable stress
changes due to the violation of material continuity at the edges of the fault. A natural
approach to model earthquakes is to assume that the earthquake fault is circular from the
beginning, with rupture starting from a point and then propagating self-similarly, until it
finally stops at a certain source radius.
5.3
Corner frequency and rupture velocity
For a circular rupture the duration of rupture τc is given by:
80
r
vr
τc =
 v sin θ 
⋅ 1 + r
c 

(5.3)
r
where θ is the angle between the normal n to the fracture plane and the direction of the
ray (Fig. 5.4). By integrating (5.3) on the fault plane we get:
 v sin θ 
⋅ 1 + r
dθ =
c 
π

r 2 v
r  2v 
= + ⋅ r = ⋅ 1 + r 
vr π c vr  π c 
∆τ c =
2
π 2
∫0
r
vr
(5.4)
As seen in the chapter 1, the source radius is related to corner frequency fc through the
relation (1.23), that is:
r = kc ⋅
c
fc
(5.5)
r
n
θ
r
Figure 5.4: Circular fault plane with finite radius r. θ is the angle between the normal to the fault plane and
direction of ray.
As mentioned above, kc is a coefficient which depends on the adopted circular rupture
model and wave type. By replacing (5.5) in (5.4) we obtain the relationship between the
corner frequency and rupture duration:
81
f c = kc
c
vr
 2v  1
⋅ 1 + r  ⋅
 π c  ∆τ c
(5.6)
Let us consider three circular source model:
Sato and Hirasawa’s model (1973), where the center of the expanding circular
front coincides with the center of the circular fault. In Sato and Hirasawa’s (1973)
model, the stopping of slip occurs simultaneously over the entire surface of the crack
when the rupture front stumbles on the edge of the fault. This model predicts higher
corner frequency for P-waves than for S-waves, in accordance with observations. Their
corner frequencies averaged over all directions are:
f cP = k PSH ⋅
α
;
r
f cS = k SSH ⋅
β
r
(5.7)
where α and β are P- and S- waves velocity, respectively. k PSH and k SSH are the
coefficients for P- and S- waves equal to 1.85/2π and 1.53/2π.
Madariaga (1976). In this model, the slip does not stop simultaneously on the
fault. Once the rupture front stops, a healing phase propagates inward from the edge of
the fault causing the arrest of slippage. For Madariaga’s model:
f cP = k PMAD ⋅
β
r
;
f cS = k SMAD ⋅
β
r
(5.8)
where k PMAD = 0.32 and k SMAD = 0.21 . In this equation the velocity of S-waves is
considered also for P-waves.
In Brune’s model (1970) the stress pulse is applied instantaneously on the whole
fault area. For this reason, there is no fracture propagation. The shear pulse generates a
shear wave that propagates perpendicularly to the fault plane. Brune’s model is
commonly used to obtain fault dimensions from spectra of S waves, so the corner
frequency is given by:
82
f cS = k SBRU ⋅
β
r
(5.9)
where k SBRU = 0.37 .
In this models, all the coefficients kS were obtained by assuming the ratio between
rupture velocity and S-waves velocity vr/β equal to 0.9.
By rewriting equation (5.4) for P- and S- waves we obtain the following relations:
1 1  2 vr  1
⋅
= ⋅ 1+
r vr  π β  ∆τ S
1 1  2 vr  1
⋅
= ⋅ 1+
r vr  π α  ∆τ P
(5.10)
The formula (5.10) can be used to obtain the estimate of rupture velocity vr, comparing
the ratio ∆τ S ∆τ P :
∆τ S
=
∆τ P
1+
2 vr
π 3β
1+
2 vr
(5.11)
πβ
by assuming α = 3β .
So from source time functions calculated by P- and S-phases the rupture durations are
obtained and by applying relation (5.11) the rupture velocity can be estimated.
83
Chapter 6 Applications
In this chapter we will see the results obtained by applying the deconvolution technique
(Vallée, 2004) to the Mw=6.3 L’Aquila mainshock and cluster of aftershock and
foreshock of L’Aquila sequence with moment magnitude ranging between 3.5 and 5.6.
Finally the results obtained to Mw=2.9 Laviano mainshock are shown.
6.1
L’Aquila sequence
The 2009 L’Aquila earthquake (Mw 6.3) occurred in the Central Apennines (Italy) on
April 6th at 01:32 UTC. The hypocenter is located at 42.35 N, 13.38 E at a depth of 9.5
km (http://portale.ingv.it). The earthquake caused nearly 300 casualties and heavy
damages in the L’Aquila town and in several villages nearby. The mainshock was
preceded by a seismic sequence starting a few months before and culminating with an ML
4.1 event on March 30th 2009, followed by a ML 3.9 and a ML 3.5 foreshocks on April
5th 2009 – a few hours before the mainshock. The earthquake ruptured a northwest southeast active segment of the normal fault system embedded in the mountain front of
the central Apennines (Cirella et al., 2009; Walters et al., 2009).
The central Apennines (Italy), that belongs to the Lazio-Abruzzi Mesozoic
carbonate platform domain, is dominated by the roll-back of the Adriatic subduction
toward the east (Doglioni et al., 1998). This region shows an arc-like belt of seismicity in
the upper crust that follows the mountain range and is characterized by normal faults
directed along pre-existing compressive tectonic structures (Bigi et al., 2002). NorthWest striking segments are present and the largest seismic events are mainly related to
normal faulting mechanisms (Fig. 6.1), consistent with the regional NE–SW trending
extension (Selvaggi, 1998; Montone et al., 1999; Serpelloni et al., 2005; Devoti et al.,
2008; D’Agostino et al., 2009) and likely controlled by deep crustal-scale decollements
(Bigi et al., 2002).
One of goals of this work was the creation of an accelerometric waveform archive
of 605 earthquakes recorded between 30 March 2009 and 30 April 2009 by DPC-RAN
(National Accelerometric Network) (35 stations) and by INGV (29 stations) permanent
and temporary seismic networks. All of the stations are equipped with Kinemetrics
84
Episensor FBA ES-T sensors with high dynamic range, from 108 to 130 dB. Several of
the new stations installed after the L’Aquila mainshock, are equipped with the new
instrumentation recently acquired by DPC: a three-component Syscom Instruments Force
Balance Accelerometer, model MS2007.
Figure 6.1: Sketch map of main tectonic features of Italy simplified from Bigi et al. (1990). CMTs for great
earthquakes that occurred between 1976 and 1998 are shown. (a) thrust fault (pre-middle Pliocene); (b)
thrust fault (middle Pliocene-Recent); (c) normal fault; (d) strike-slip fault; (e) undetermined fault
(Montone et al., 1999).
The total number of three-component records is 32275 for events with local
magnitude ranging between 2.5 and 5.9, and recorded by 3 to 41 stations. This dataset
provides with a unique aftershock strong motion data bank covering a wide moment
magnitude [2.6-6.3] and epicentral distance ranging from near-source (≤ 20 km) to farfield (100 km) (Fig. 6.2). For this reason these data can be very useful to determine
refined ground motion prediction equations and models of the rupture processes.
85
Figure 6.2: Map showing the stations of DPC-RAN and INGV networks (triangles). The hypocenters of the
earthquakes considered in this study (size of circle is proportional to the local magnitude) are also plotted.
The creation of the database consists of three phases:
1.
processing of binary files in ASCII files
2.
converting ASCII files into SAC files format (Seismic Analysis Code, from
Lawrence Livermore National Laboratory)
3.
association of the waveform (in sac format) at each event.
In the last step the header of each event waveform is filled of location and local
magnitude reported by catalog Italian Seismic Instrumental and parametric Data-basE
(Iside, http://iside.rm.ingv.it).
A data quality parameter (Elia et al., 2009) is assigned to each waveform, automatically
computed by evaluating the signal to noise ratio S/N of the signal level S of the recorded
earthquake compared to the noise level N before the event. In figure 6.3 and 6.4 an
example of waveforms with S/N ≥ 50 and waveforms of a small earthquake are shown,
respectively.
86
Figure 6.3: Examples of waveforms recording by RAN network. The seismogram relative to station MTR
presents S/N ≈ 51, while for station GSA it is equal to about 85.
Figure 6.4: Waveforms of a small earthquake recorded up to a distance of about 30 km from epicenter.
Date: 2009-04-30; Origin time T0 = 16:41:47 ; Latitude (°) = 42.35 ; Longitude (°) = 13.342 ; Depth (km)
= 8.6 ; Local magnitude ML = 2.5.
87
6.1.1
Mw 6.3 L’Aquila mainshock
In this paragraph the RSTFs of Mw=6.3 L’Aquila mainshock are shown. As we will see,
from RSTFs the fracture properties are analyzed.
Regional data
To
determine
the RSTFs the
Mw=4.9
aftershock occurred
on
2009-04-09
at
09:26:29 UTC was used as EGF. To get more information at different azimuths and to
overcome the limitations of similar location and focal mechanism between master event
and EGF, the waveforms of 39 broad band stations have been recovered by the following
networks:
MedNet
(MN), ISNet (IN), INGV (IV), GEOFON (GE),
French Broadband Seismological Network (FR), Austrian
Aristotle
University
of
Thessaloniki
Seismic
Seismological
Network (OE),
AUTH (HT),
Slovenia (SL), BayernNetz, Germany (BW), Hungarian Seismological Network (HU).
These stations are located at a distance greater than about 200 km and less than about 80
km (Fig. 6.5).
In case of a large earthquake, the waves considered in the deconvolution process are
surface waves. They constitute the best choice, since they are sensitive to long periods
and they do not suffer from the wave mixing of body waves. Data has been windowed in
the Love and Rayleigh waves and filtered between 0.05 Hz and 1 Hz (Fig. 6.6).
88
Figure 6.5: Map of seismic stations used in this study. The location of Mw=6.3 mainshock (red star) and
EGF (white circle) are also shown. The information on location and focal mechanism are taken by
catalogue Iside and INGV, respectively.
MAINSHOCK
EGF
Figure 6.6: Example of time window [T0, T1] on Love wave for the station OBKA.
89
Estimate of fracture properties: modeling of RSTFs
Figure 6.7 shows the RSTFs obtained for all the considered stations with increasing
azimuth from left top to right bottom.
It is clear the presence of two bumps in the RSTFs as well as the effects of directivity. In
fact, the RSTFs of station located along the direction of rupture propagation, i.e. stations
of ISNet network (AND3, CLT3, etc.) show short duration and big amplitude, while the
RSTFs of station behind the rupture propagation, i.e. stations of Austrian
seismic network (DAVA, RETA), have long duration and small amplitude.
In other words, proceeding from stations behind the rupture propagation (station ROBS)
to stations along the direction of rupture propagation (station COL3) we observe in
RSTFs that the distance between the two bumps becomes increasingly smaller and their
amplitude increasingly small.
The two bumps may mark off an anomaly zone due to the presence of eventual
fluid. To confirm this hypothesis, a careful study of the geology of the area should be
done.
Since the two bump are well localized, we considered the difference ∆τ between
the arrival times of first and second bumps to obtain accurate estimates of rupture
velocity Vr and length L of fault plane. In this case the estimated length is the segment of
faults
between
the
two
bumps.
For the
event
in
study we
assumed
the
following unilaterally propagating rupture model
∆τ =
L  VR

1 − cos α 
Vr 
c

(6.1)
where c is the velocity of wave used in the deconvolution process and α is the directivity
angle between the rupture direction and the Love-wave ray leaving the source. For this
analysis we used only the RSTFs calculated by Love waves. So, the Love - waves phase
velocity is set equal to 4.5 km/s. From fit between the observed data ∆τ and theoretical
model (6.1) we obtain that Vr = 1.85 ± 0.09 km/s and L = 5.6 ± 0.3 km (Fig. 6.8).
This value of Vr corresponds to about 60-70% of the velocity of shear waves to the depth
of the source and it is consistent with a slow rupture propagation as well as inferred
90
from kinematic inversion models obtained by the combined inversion of teleseismic,
accelerometer and GPS data (Balestra et al., 2010, Yano et al., 2009). In fact figure 6.9a
shows the observed RSTFs (filled curves) together with the RSTFs computed (red curve)
from slip model of Balestra et al. (2010) inverting strong motion, broadband telseismic,
GPS, and InSAR data (Fig. 6.9b). We can observe from this model that the rupture
propagates in two directions, updip and toward the SE, exhibiting two or three asperities.
Balestra et al. (2010) estimate a value of rupture velocity equal about 1.9 km/s and the
total rupture length is between 14 and 16 km. So, the estimate L=5.6 km obtained in our
study represents the distance between the two patch of highest values of slip in the slip
model. The good agreement
between observed and computed RSTFs is a strong
indicator of the realness of our results.
91
Figure 6.7: RSTFs obtained by stabilized deconvolution of Mw=6.3 L’Aquila mainshock.
Figure 6.8: Fit between the observed data (black circles) and theoretical model (red curve). The observed
data are the difference between the arrival times of first and second bumps.
92
(a)
(b)
Figure 6.9: Comparison between observed and computed RSTFs (a). Observed RSTFs (filled curves) are
obtained by deconvolution approach. Computed RSTFs (red curves) are computed from rupture process
model (b) of Balestra et al. (2010).
93
6.1.2
Cluster of events with Mw ≥ 3.0
After the study of fracture properties of Mw=6.3 L’Aquila mainshock, the events of
L’Aquila sequence with moment magnitude 3<Mw<5.6 are analyzed to retrieve
information on kinematic parameters of fractures, in particular, on rupture velocity and
its relationships with corner frequency.
Near source data
From the data set described in paragraph 1 a cluster of 32 events with Mw >= 3 has been
selected. The accelerometric waveforms of this events have been integrated with
velocimeter data recorded by INGV network.
As seen in paragraph 4.4.2, the near-source data processing requires several steps before
the deconvolution. In the first and second steps the 32 events are located using the
code NLLoc (Lomax et
al, 2000)
and the
focal
mechanisms are
calculated
with FPFIT (Reasenberg et al, 1985). The focal mechanisms are prevalently of normal
type, consistent with the extensional tectonics active in the central Apennines since the
Pliocene (Walters et al., 2009) (Fig. 6.10).
From this cluster of events, 15 pairs master event-EGF with similar location and focal
mechanism have been selected. Finally the study of the stability of the polarization is
performed to the optimal choice of the frequency range to be used in the deconvolution
process.
94
Figure 6.10: Hypocenter (dots) and focal mechanisms of custer of 32 events with moment magnitude
ranging between 3 and 5.6.
Rupture duration, corner frequency and rupture velocity
For each selected pair master event-EGF, the RSTFs are calculated for P- and S- phase
using the deconvolution method (Vallèe, 2004). Figure 6.11 shows the RSTFs of Mw=4.0
foreshock occurred in 2009-03-30 at 13:38 (UTC). Also the misfit between the real
mainshock and the reconstituted mainshock for any stations is shown as a function of the
allowed duration of the RSTF. As EGF the Mw=3.0 11 April 2009 at 13:57 UTC was
used. The range of frequency used in the deconvolution process is [0.05-4] Hz.
Once the RSTFs are known, the relationship between the corner frequencies fc
estimated through the inversion of the displacement spectra of S-waves (Orefice and
Zollo, 2010) and the inverse of duration τc-1 has been investigated. For each master event
the mean value of inverse duration ∆τc-1 is calculated as arithmetic mean of τc-1 observed
at each station.
In figure 6.12 the estimate of fc with their uncertainties are plotted as function of ∆τP-1
and ∆τS-1, that is the arithmetic mean of τc-1 obtained by P- and S- phases, respectively. In
order to estimate the best value for the fc -to- ∆τP-1/ ∆τS-1 ratio, a non-linear best-fitting
95
procedure which allows to account for the uncertainties on both the two variables has
been applied (Reed 1989), assuming the linear model Log(fc) = Log(∆τc-1) + Log(a). The
fitting problem is reduced to an optimization problem for the intercept Log(a) by setting
the slope to one. The estimated value for S-phase is Logas ± σas = -3.32e-02 ± 2.30e-02
which yields as ± σas = 0.93 ± 0.05. For P-phase we obtain Logap ± σap = -7.41e-02 ±
3.89e-02 which yields ap ± σap = 0.84 ± 0.08. Therefore, the observed duration of RSTFs
is in inverse proportion to corner frequency.
S phase
P phase
AQU
FAGN
FIAM
GIUL
Figure 6.11: RSTFs of an event with moment magnitude Mw=4 obtained by S- and P- phase. Any example
of misfit between real mainshock and the reconstituted mainshock are reported.
As seen in the paragraph 5.3, the theoretical coefficient a is given by the quantity
∆τ c ⋅ f c = a = k c
c
vr
 2v 
⋅ 1 + r 
 π c
(6.2)
where kc·c depends on the adopted circular rupture model and wave type.
In this study we considered three circular models, that is Madariaga’s (1976), Brune’s
96
(1970) and Sato & Hirasawa’s (1973) model. In Table 4 the theoretical coefficients a for
this models are reported for both P- and S- waves. We want remark that in these models,
all the coefficients kc and so the coefficients a are obtained by assuming the ratio between
rupture velocity and S-waves velocity vr/β equal to 0.9.
In figure 6.12 the theoretical lines obtained by using Madariaga’s (red line), Brune’s
(green) and Sato and Hirasawa’s (blue) model are also plotted. We can observe that none
of the models in literature (circle rupture) explains the relationship fc vs ∆τP-1/ ∆τS-1.This
could be due to the fact that the vr/β ratio is not equal to 0.9, as these models assume. In
fact, using the measurements ∆τP and ∆τS we can estimate the rupture velocity through
relation (5.11) independently from the adopted rupture model. To estimate vr we applied
the same non-linear best fitting procedure implemented for the estimation of coefficient
a. The best fitting line Log(∆τP-1) = Log(∆τS-1) + Log(b) results b ± σb = 1.16 ± 0.08
(Fig. 6.13). Assuming different values of vr/β in equation (5.11), with β = 3374 m/s (Bagh
et al, 2007), we obtain that b ± σb = 1.16 ± 0.08 corresponds to ratio vr/β ranging
between 0.7 and 0.8. Therefore, the most probable value of vr/β ratio is less than 0.9.
So, as obtained for the Mw = 6.3 L’Aquila mainshock, also the smallest events have low
value
of
rupture
velocity,
property of rocks where fractures are
indicating
developed,
that vr is
regardless
of
a mechanical
the geometry
of
the fracture planes and the initial conditions of stress .
Table 4: Theoretical coefficient a for the circular model used in this study
Model
atheo
S wave
atheo
P wave
Madariaga
(1976)
0.367
0.437
Brune
(1970)
0.647
-
Sato and
Hirasawa
(1973)
0.627
0.515
97
ap ± σap = 0.84 ± 0.08
Log( fc )
as ± σas = 0.93 ± 0.05
Log(∆τS-1)
Log(∆τP-1)
Figure 6.12: Corner frequency versus inverse of duration obtained by S- (on the left) and P- phases (on the
right). The black lines are the best fitting lines, while the dashed lines correspond to plus/minus one
standard deviation. The theoretical lines obtained by Madariaga’s (red line), Brune’s (green) and Sato and
Hirasawa’s (blue) model are also plotted.
Log(∆τS-1)
b ± σb = 1.16 ± 0.08
0.5
0.6
0.7
0.8
0.9
Log(∆τS-1)
Figure 6.13: Inverse of duration obtained by S- phases versus inverse of duration obtained P- phases. The
black lines are the best fitting lines, while the dashed lines correspond to plus/minus one standard
deviation. The red lines are the theoretical lines obtained by assuming different values of vr/β in equation
(3.30).
98
Scaling laws
As seen in the previous paragraphs, from the area and durations of RSTFs the seismic
moment and source radius of events in study can be calculated, respectively. From
estimates of source radius and seismic moment the static stress drop can be calculated
with equation (1.24) and the scaling laws can be investigated.
As evidenced by previous analysis, the vr/β ratio varies between 0.7 and 0.8. Exactly the
value 1.16 of coefficient b corresponds to vr/β = 0.77. So, by using this value the source
radius is calculated by relation (5.10) for each events.
Figure 6.14a,b shows the log-log representation of source radius and static stress drop vs
seismic moment, respectively, for P- wave (empty circles) and S- waves (solid circles),
with the associated uncertainties. The constant stress drop lines at values 0.1 to 100 MPa
are shown in the same figure. We observe the self-similar scaling of source radius with
seismic moment. The mean value of static stress drop for P- and S- waves is (2.7 ± 1.2)
MPa and (3.0 ± 1.7) MPa, respectively.
Thus, reliable source parameters can be estimated through RSTF without need to know
velocity and attenuation model, and no assumptions are done on the shape of adopted
spectral model.
(a)
(b)
Figure 6.14: Log-log representation of source radius (a) and static stress drop (b) vs seismic moment,
respectively, for P- wave (empty circles) and S- waves (solid circles), with the associated uncertainties. The
constant stress drop lines at values 0.1 to 100 MPa are shown.
99
6.2
Mw 2.9 Laviano mainshock
We studied the rupture process of the largest magnitude event of Laviano sequence by
performing a kinematic rupture modeling through the deconvolution by an empirical
Green’s function. The event of magnitude Mw = 2.9 is considered as the mainshock of the
sequence since its seismic moment (2.6·1013 N·m), is about 4-5 times the cumulative
seismic moment of all foreshocks and aftershocks (6.5·1012 N·m corresponding to Mw =
2.5). The main contribution to this latter value is due to the aftershocks which cumulated
a seismic moment of 5.4·1012 N·m (Mw = 2.4) while the one associated with the
foreshocks was 1.1·1012 N·m (Mw = 2.0).
Near source data
To retrieve the source time functions we applied the stabilized deconvolution technique
of Vallée (2004) to Mw=2.9 Laviano mainshock recorded by ISNet network. In
particular, the Mw=1.9 aftershock occurred on 2008-5-27 at 17:25 UTC was used as
EGF. We estimated the RSTFs at 12 of recording stations (Fig. 6.15) in the S-wave time
window: the duration ranges from about 0.07 s to about 0.11 s, evidencing a directivity
effect.
Figure 6.15: Apparent source time functions of the main event obtained at different stations of the ISNet
network. Apparent source time functions are ordered from above to below respect to the station azimuth.
100
Inversion of RSTFs
We performed a kinematic rupture inversion of RSTFs by the use of isochrones back
projection technique (Festa et al., 2006). The inversion of RSTFs allowed to constrain the
fault plane and provided us with the estimation of the slip distribution, the rupture
direction and the average velocity rupture.
For the main event we measured 26 P-wave first motion polarities. From these
polarities we obtained by means of the FPFIT code the fault plane solutions of the main
event. The two nodal planes have strike 290˚, dip 40˚, rake -100˚, and strike 123˚, dip
51˚, rake -82˚, respectively. The resulting focal mechanism is reported on figure 6.16a
and indicates an almost pure normal faulting event. We investigated which of the two
nodal planes is the more likely for the rupture of the main event by the use of the backprojection technique. For each plane and a fixed constant rupture velocity, we retrieved
the best solution for the slip by minimization of the L1 distance between the observed
RSTFs and their synthetic estimations, the choice of the cost function trying to reproduce
both the amplitude and the shape of the RSTFs. Results were plotted in figure 6.16b. The
minimum of the cost function was obtained for a rupture with a velocity of 2.3 km/s
along the nodal plane having strike 290˚, and dip 40˚. To check the sensitivity of the
solution to the nodal plane and the rupture velocity, the vertical axis of the figure shows
the normalized variation of the cost function with respect to the minimum value. By
inspection of the curves, we found that the nodal plane generating the rupture is well
constrained while the variation of the cost function with the rupture velocity is very
small, indicating a large uncertainty on this parameter. The slip distribution (Fig. 6.16c)
shows that the main event was principally a circular crack having a predominant updip
direction of the rupture with an average velocity of 2.3 km/s, and a high slip patch of
about 3.0 cm on the positive direction of the strike. The estimated average slip in this
area is equal to 2.2 cm. In addition, we found that the other events mostly occurred on the
left side (i.e. negative direction of the strike) of the main event, with average slips that
range from 0.2 cm to 0.7 cm (Fig. 6.16c). These values are small compared to the
average slip obtained for the main event.
The presence of structural or rheological discontinuities may be responsible of the strong
heterogeneous slip distribution estimated for the main event and the asymmetrical
101
location of foreshocks and aftershocks. For this reason, an accurate spatiotemporal study
of the crackling noise (in particular repeated earthquakes and swarms) in the Irpinia
region should help to better understand the state of health of the fault and to possibly
monitor also time variations. In particular, the accurate knowledge of the P- and S-wave
arrival times with a large number of records, and refined location of events, allows to
study Vp/Vs variations in space and time along the Irpinia fault to monitor fluid
injections, which could play a key role in the preparatory phase of a large event (Chiodini
et al., 2004; Lucente et al., 2010).
Figure 6.16: (a) Fault plane solutions of the main event. P and T denote the P- and T-axes positions. Open
circles and crosses indicate dilatations and compressions, respectively. (b) Percentage variation of the
normalized cost function against rupture velocity for both the nodal planes. The absolute minimum is
obtained for the nodal plane with strike 290˚, and dip 40˚, and for a rupture velocity of 2.3 km/s. (c) Slip
map of the main event and superimposed distribution of all microearthquakes in the swarm along the
strike-dip plane. The dimension of circles is the Madariaga’s circular rupture area of events inferred from
corner frequencies, while the color of circles indicates the computed average slip ∆u. Dotted circle at the
center is the rupture area of the main event estimated by the RSTFs durations. Horizontal and vertical
location errors are also displayed (Stabile et al., submitted to Scientific Reports).
102
6.3
Conclusions
From the RSTFs it is possible to get knowledge of the seismic source without modeling
the propagation between the source and the receiver. In fact, from the observables of
RSTFs, that is duration, area and shape, we have obtained:
1.
presence of two bumps in the RSTFs of mainshock Mw 6.3;
2.
low velocity rupture (vr ~ 1.9 km/s) for Mw=6.3 L’Aquila mainshock, as well as, low
value of vr for smallest events of L’Aquila sequence;
3.
the observed duration of RSTFs is in inverse proportion to corner frequency;
4.
a constant stress-drop and apparent stress scaling of source parameters is observed.
The average static stress drop for P- and S- waves is (2.7 ± 1.2) MPa and (3.0 ± 1.7)
MPa, respectively. The apparent stress is equal to (2.7 ± 1.2) MPa and (1.7 ± 0.7) MPa
for P- and S- waves, respectively.
5.
For Mw = 2.9 Laviano mainshock the RSTFs were inverted to obtain maps of slip
and velocity rupture: the slip distribution shows that the mainshock was principally a
circular crack with a slip concentration in the updip and west directions, evidencing a
possible directivity effect toward those directions.
103
Summary
The objective of this work of thesis is the refined estimations of source parameters. To
such a purpose we used two different approaches, one in the frequency domain and the
other in the time domain.
In frequency domain, we analyzed the P- and S-wave displacement spectra to
estimate spectral parameters, that is corner frequencies and low frequency spectral
amplitudes. We used a parametric modeling approach which is combined with a multistep, non-linear inversion strategy and includes the correction for attenuation and site
effects.
First of all a resolution test was applied in order to estimate the minimum moment
magnitude value above which source parameters can be effectively derived. For this test
we consider a microearthquake sequence started on May 25th 2008 in Irpinia region,
nearby the village of Laviano (Southern Italy). By the resolution test we have obtained
the minimum values of seismic above which we can obtain reliable estimates of source
parameters, that is M0 ≥ 1011 N·m (Mw ≈ 1). Then the iterative multi-step procedure was
applied to about 700 microearthquakes in the moment range 1011-1014 N·m and recorded
at the dense, wide-dynamic range, seismic networks operating in Southern Apennines
(Italy). Our results show that the constant-Q attenuation model is preferred to frequency
dependent Q-models. Using the retrieved corner frequencies and the Madariaga’s (1976)
crack model to get the source radius, we computed the variation of the source radius and
static stress release with seismic moment. The self-similarity of earthquake source
parameters is observed over whole range of seismic moment, with a constant values of
static stress drop.
The analysis of the source parameters is often complicated when we are not able
to model the propagation accurately. In this case the empirical Green function approach
is a very useful tool to study the seismic source properties. In fact the Empirical Green
Functions (EGFs) consent to represent the contribution of propagation and site effects to
signal without using approximate velocity models.
An EGF is a recorded three-component set of time-histories of a small earthquake whose
source mechanism and propagation path are similar to those of the master event. To
establish how much is the difference in magnitude between the EGF and master event, a
104
synthetic test was performed. It has been shown that the EGF optimal magnitude is about
1 units smaller than the mainshock.
Thus, in time domain, the deconvolution method of Vallée (2004) was applied to
calculate the source time functions (RSTFs) and to accurately estimate source size and
rupture velocity. This technique was applied to 1) large event, that is Mw=6.3 2009
L’Aquila mainshock (Central Italy), 2) moderate events, that is cluster of earthquakes of
2009 L’Aquila sequence with moment magnitude ranging between 3 and 5.6, 3) small
event, i.e. Mw=2.9 Laviano mainshock (Southern Italy).
From duration and area of RSTFs accurate estimations of source radius, and so corner
frequency, seismic moment and rupture velocity were obtained. From estimates of source
radius and seismic moment the static stress drop was calculated and the scaling laws
were investigated for smallest events of L’Aquila sequence. For these events and also for
L’Aquila mainshock a low velocity rupture was estimated in agreement with kinematic
inversion models obtained by the inversion combined of teleseismic, accelerometer and
GPS data (Balestra et al., 2010, Yano et al., 2009). Moreover, the inversion of RSTFs of
Laviano mainshock allowed to constrain the fault plane and provided us with the
estimation of the slip distribution, the rupture direction and the average velocity rupture.
105
References
Abercrombie, R. E. (1995), Earthquake source scaling relationships from -1 to 5 ML
using seismograms recorded at 2.5 km depth, J. Geophys. Res., 100, 24,015-24,036.
Abercrombie, R. E. and Rice, J. R. (2005), Can observations of earthquake scaling
constrain slip weakening?, Geophys. J. Int., 162, 406-424.
Akaike, H. (1974), A new look at the statistical model identification, IEEE Trans. Autom.
Control, 6, 716–723.
Aki, K. (1987), Magnitude-Frequency Relation for Small Earthquakes: A Clue to the
Origin of fmax of Large Earthquakes, J. Geophys. Res., 92(B2), 1349–1355,
doi:10.1029/JB092iB02p01349.
Aki (1967), Scaling law of seismic spectrum, J. Geophys. Res., 72, 729-740.
Aki, K., and P. G. Richards (1980), Quantitative Seismology, Theory and Methods (2
volumes). W. H. Freeman, San Francisco, 932 pp.
Amato, A., Alessandrini, B., Cimini, G.B., Frepoli, A. and Selvaggi, G., (1993), Active
and remnant subducted slabs beneath Italy: evidence from seismic tomography and
seismicity, Ann. Geofis., 36, 201-214.
Ammon, C. J., A. A. Velasco, and T. Lay (1993), Rapid estimation of rupture directivity:
application to the 1992 Landers (MS=7.4) and Cape Mendocino (MS=7.2), California
earthquakes, Geophys. Res.Lett. 20, 97–100.
Anderson, J. G. and S. E. Hough (1984), A model for the shape of the Fourier amplitude
spectrum of acceleration at high frequencies, Bull. Seismol. Soc. Am., 74, 1969-1993.
Archuleta, R.J., E.C. Cranswick, C. Mueller, and P. Spudich (1982), Source parameters
106
of the 1980 Mammoth Lakes, California, earthquake sequence, J. Geophys. Res., 87,
4595-4607.
Azimi, S.A., Kalinin A.V., Kalinin V.V. Pivovarov, B.L. (1968), Impulse and transient
characteristic of media with linear and quadratic absorption laws, Izvestiya, Physics od
solid Earth, 88-93.
Balestra, J., M. Chlieh and B. Delouis (2010), Slip inversion of the L'Aquila (Italy) April
9, 2009 earthquake (Mw 6.3) from strong motion, teleseismic, and InSAR data, Eos
Trans. AGU, 90(52), Fall Meet. Suppl., Abstract, S12A08.
Ben-Menahem, A., and S. J. Singh (1981), Seismic Waves and Sources, Springer-Verlag,
New York.
Bernard P., and A. Zollo (1989), The Irpinia (Italy) 1980 earthquake: detailed analysis of
a complex normal fault. J. Geophys. Res. 94, 1631-1648.
Bertero, M. (1989), Linear inverse and ill-posed problems, in Advances in Electronics
and Electron Physics, Vol. 75, P. W. Hawkes (Editor), Academic, New York, 1–120.
Bertero, M., P. Boccacci, and F. Maggio (1995), Regularization methods in image
restoration: an application to HST images, Int. J. Imaging Systems Tech. 6, 376–386.
Bertero, M., D. Bindi, P. Boccacci, M. Cattaneo, C. Eva, and V. Lanza (1997),
Application of the projected Landweber method to the estimation of the source time
function in seismology, Inverse Problems 13, 465–486.
Bigi, S., C. Doglioni, and G. Mariotti (2002), Thrust vs normal fault decollements in the
Central Apennines, Boll. Soc. Geol. Ital., 1, 161–166.
Bindi D., Spallarossa D., Augliera P., and M. Cattaneo (2001), Source Parameters
Estimated from the Aftershocks of the 1997 Umbria–Marche (Italy) Seismic Sequence,
107
Bull. Seismol. Soc. 815 Am., 91, 448-455; doi: 10.1785/0120000084.
Bindi D., S. Parolai S., Grosser H., C. Milkereit C., and S. Karakisa (2006), Crustal
Attenuation Characteristics in Northwestern Turkey in the Range from 1 to 10 Hz, Bull.
Seismol. Soc. Am., 96, 200-214, doi: 10.1785/0120050038.
Bobbio, A., M. Vassallo, and G. Festa (2009), A local magnitude scale for Southern Italy,
Bull. Seismol. Soc. Am., 99, 2461–2470, doi: 10.1785/0120080364.
Bouchon M., (1981), A simple method to calculate Green's functions for elastic layered
media. Bull. Seism. Soc. Am., 71(4), 959-971.
Brune J.N., (1970), Tectonic stress and the spectra of seismic shear waves from
earthquakes. J. Geophys. Res. 75, 4997-5009.
Cantore L., V. Convertito, and A. Zollo (2010), Development of a site conditions map for
the Campania–Lucania region (Southern Apennines, Italy), Annals of Geophysics, 53 (4);
doi:837 10.4401/ag-4648.
Chiodini, G., C. Cardellini, A. Amato, E. Boschi, S. Caliro, F. Frondini, and G. Ventura
(2004), Carbon dioxide Earth degassing and seismogenesis in central and southern Italy.
Geophys. Res. Lett. 31(L07615). doi: 10.1029/2004GL019480.
Cirella, A., A. Piatanesi, M. Cocco, E. Tinti, L. Scognamiglio, A. Michelini, A. Lomax,
and E. Boschi (2009), Rupture history of the 2009 L'Aquila (Italy) earthquake from nonlinear joint inversion of strong motion and GPS data. Geophys. Res. Lett., 36(19),
L19304, doi:10.1029/2009GL039795.
Clinton, J., (2004), Modern Digital Seismology - Instrumentation, and Small Amplitude
Studies for the Engineering world, Ph.D. Thesis.
Courboulex, F., M. A. Santoyo, J. F. Pacheco, and S. K. Singh (1997a), The 14
108
September 1995 (M 7.3) Copala, Mexico, earthquake: a source study using teleseismic,
regional, and local data, Bull. Seism. Soc. Am. 87, 999–1010.
Coutant, O. (1989), Programme de simulation numérique Axitra. Rapport LGIT,
Grenoble.
Davis L. (1987), Genetic Alghorithms and Simulated Annealing. Morgan Kaufmann
Publishers, London.
D’Agostino, N., S. Mantenuto, E. D’Anastasio, A. Avallone, M. Barchi, C. Collettini, F.
Radicioni, A. Stoppini, and G. Castellini (2009), Contemporary crustal extension in the
Umbria –Marche Apennines from regional CGPS networks and comparison between
geodetic and seismic deformation, Tectonophysics, 476 (2009) 3–12.
Deichmann, N. (2006), Local magnitude, a moment revisited, Bull. Seismol. Soc. Am. 96,
1267-1277, doi 10.1785/0120050115.
de Lorenzo, S., A. Zollo, and G. Zito (2010), Source, attenuation, and site parameters of
the 1997 Umbria-Marche seismic sequence from the inversion of P wave spectra: A
comparison between constant QP and frequency dependent QP models, J. Geophys. Res.,
115, B09306, doi:10.1029/2009JB007004.
Devoti, R., F. Riguzzi, M. Cuffaro, and C. Doglioni (2008), New GPS constraints on the
kinematics of the Apennines subduction, Earth Planet. Sci. Lett., 273, 163–174,
doi:10.1016/j.epsl.2008.06.031.
Doglioni, C., F. Mongelli, and G. Pialli (1998), Boudinage of the Alpine belt in the
Apenninic back-arc, Mem. Soc. Geol., 52, 457– 468.
L. Elia, C. Satriano and G. Iannaccone (2009), SeismNet Manager - A web application to
manage hardware and data of a seismic network. Seismol. Res. Lett., 80(3), doi:
10.1785/gssrl.80.3.420.
109
Edwards, B., A. Allmann, D. Fäh and J. Clinton. (2010), Automatic Computation of
Moment Magnitudes for Small Earthquakes and the Scaling of Local to Moment
Magnitude, J. Geophys. Res., 183, 407-420, doi: 10.1111/j.1365-246X.2010.04743.x
Festa, G., and A. Zollo (2006), Fault slip and rupture velocity inversion by isochrone
backprojection. Geophys. Journal International 166(2), 745-756. doi:10.1111/j.1365246X.2006.03045.x
Fletcher, J. B., J. Boatwright, L. C. Haar, T. C. Hanks, and A. McGarr (1984), Source
parameters for aftershocks of the Oroville, California, earthquake, Bull. Seismol. Soc.
Am., 74, 1101-1123.
Fracassi, U., and G. Valensise (2003), The "layered" seismicity of Irpinia (Southern
Italy): Important but incomplete lessons learned from the 23 November 1980 earthquake,
in The Many Facets of the Seismic Risk, Proceedings of the Workshop on
Multidisciplinary Approach to Seismic Risk Problem, edited by M. Pecce et al., pp. 46 52, CRdC AMRA, S. Angelo dei Lombardi.
Fukuyama, E., and K. Irikura (1986), Rupture process of the 1983 Japan Sea (Akita-Oki)
earthquake using a waveform inversion method, Bull. Seism. Soc. Am. 76, 1623–1640.
Goldberg D. E. (1989), Genetic algorithms in search, optimization and machine learning.
Addison-Wesley Piblishing Company, New York.
Hanks, T. C., and H. Kanamori (1979), A moment magnitude scale, J. Geophys. Res., 84,
2348 -2350.
Hanks, T. C. (1981), The corner frequency shift, earthquake source models and Q, Bull.
Seism. Soc. Am, 71, 597-612.
Hartzell, S. H. (1978), Earthquake aftershocks as Green’s functions, Geophys. Res. Lett.
5, 1–4.
110
Hough, S. E. (1997), Empirical Green’s function analysis: taking the next step, J.
Geophys. Res., 102(B3), 5369–5384.
Ide, S., and G. C. Beroza (2001), Does apparent stress vary with earthquake size?,
Geophys. Res. Lett., 28, 3349-3352.
Ide S, Beroza GC, Prejean SG, Ellsworth WL (2003), Apparent Break in Earthquake
Scaling Due to Path and Site Effects on Deep Borehole Recordings, J. Geophys. Res.,
108, B5, 2271, doi:10.1029/2001JB001617
Ihmlé, P. F. (1996), Frequency-dependent relocation of the 1992 Nicaragua slow
earthquake: an empirical Green’s function approach, Geophys. J. Int. 127, 75–85.
Janert K. P. (2009), Gnuplot in Action: Understanding Data with Graphs pp. 396
Manning Publications Co.
Kanamori, H., and Anderson, D. (1975), Theoretical basis of some empirical relations in
seismology. Bull. Seismol. Soc. Am. 65, 1073-1095.
Keilis-Borok, V. I. (1959), On the estimation of the displacement in an earthquake source
and of source dimensions, Ann. Geophis., 12, 205-214.
Kjartansson, E. (1979), Constant QWave Propagation and Attenuation, J. Geophys.
Res., 84(B9), 4737–4748, doi:10.1029/JB084iB09p04737.
Lawson, C., and R. Hanson (1974), Solving Least Square Problems, Prentice Hall, New
York.
Lin, G, Shearer, P.M. and E. Hauksson (2007), Applying a three-dimensional velocity
model, waveform cross correlation, and cluster analysis to locate southern California
seismicity
from
1981
to
2007
J.
of
Geophys.
Res.,
112(B12309),
doi:10.1029/2007JB004986.
111
Lomax, A., J. Virieux, P. Volant, and C. Berge (2000), Probabilistic earthquake location
in 3D and layered models: Introduction of a Metropolis-Gibbs method and comparison
with linear locations, in Advances in Seismic Event Location, Thurber, C.H., and N.
Rabinowitz (eds.), Kluwer, Amsterdam, 101-134
Lucente, F. P., P. De Gori, L. Margheriti, D. Piccinini, M. Di Bona, C. Chiarabba, N. P.
Agostinetti (2010), Temporal variation of seismic velocity and anisotropy before the
2009 MW 6.3 L'Aquila earthquake, Italy. Geology 38 (11), 1015-1018. doi:
10.1130/G31463.1
Madariaga, R. (1976), Dynamics of an expanding circular fault, Bull. Seismol. Soc. Am.,
66, 639-666.
Madariaga, R. (2009), Earthquake scaling laws, in Encyclopedia of Complexity and
Systems Science, R. A. Meyers ed., Part 5, 2581-2600, Springer, New York, DOI:
10.1007/978-0-387 30440-3_156
Maggi, C., A. Frepoli, G. B. Cimini, R. Console, and M. Chiappini (2008), Recent
seismicity and crustal stress field in the Lucanian Apennines and surrounding areas
(Southern
Italy):
Seismotectonic
implications,
Tectonophysics,
463,
130-144,
doi:10.1016/j.tecto.2008.09.032.
Marquardt, D. (1963), An Algorithm for Least-Squares Estimation of Nonlinear
Parameters, SIAM Journal on Applied Mathematics 11: 431–441. doi:10.1137/0111030.
Matrullo, E., O. Amoroso, R. De Matteis, C. Satriano, and A. Zollo (2011), 1D versus 3D
velocity models for earthquake locations: a case study in Campania-Lucania region
(Southern Italy). Geophysical Research Abstracts 13, EGU2011-9305, 2011
McGarr, A. (1986), Some observations indicating complications in the nature of
earthquake scaling. In: Das, S., Boatwright, J. and Scholz, C.H., Editors, 1986.
Earthquakes Source MechanicsAm. Geophys. Union Monogr., p. 37.
112
Menke W. (1989), Geophysical Data Analysis: Discrete Inverse Theory. Academic Press,
International Geophysics series, 45.
Molnar, P., B. E. Tucker, and J. N. Brune (1973), Corner frequencies of P and S waves
and models of earthquake source, Bull. Seismol. Soc. Am., 63, 2091-2104.
Montone, P., A. Amato, and S. Pondrelli (1999), Active stress map of Italy, J. Geophys.
Res., 104, 25,595– 25,610, doi:10.1029/1999JB900181.
Montone, P., M. T. Mariucci, S. Pondrelli, and A. Amato (2004), An improved stress map
for Italy and surrounding regions (central Mediterranean), J. Geophys. Res., 109,
B10410, doi:10.1029/2003JB002703.
Mori, J., and A. Frankel (1990), Source parameters for small events associated with the
1986 North Palm Springs, California, earthquake determined using empirical Green
functions, Bull. Seism. Soc. Am. 80, 278–295.
Morozov, I.B. (2008), Geometrical attenuation, frequency dependence of Q, and the
absorption band problem, Geophys, J. Int., 172, 239-252. DOI: 10.1111/j.1365246X.2008.03888.x.
Mueller, C. S. (1985), Source pulse enhancement by deconvolution of an empirical
Green’s function, Geophys. Res. Lett. 12, 33–36.
Nadeau, R. M. and T. V. McEvilly (1999), Fault slip rates at depth from recurrence
intervals of repeating microearthquakes, Science, 285, 718–721.
Nelder, J.A., and Mead, R. (1965), A simplex method for function minimization.
Computer Journal, 7, p. 308.
Orefice, A. and A. Zollo (2010), European Seismological Commission Abstracts,
ES5/P31/ID134, 32nd ESC GA Montpellier, France
113
Prieto, G:A., Shearer, P.M., Vernon, F.L. and Debi K. (2004), Earthquake source scaling
and self-similarity estimation from stacking P and S spectra. J. Geophys.Res., 109
(B08310), doi:10.1029/2004JB003084.
Reasenberg, P., and D. Oppenheimer (1985), FPFIT, FPPLOT and FPPAGE: Fortran
computer programs for calculating and displaying earthquake fault plane solutions. U.S.
Geol. Surv., Open File Report, 85-739.
Reed, C. (1989), Linear least-squares fits with errors in both coordinates, Am. J. Phys. 57
(7), 642-646.
Savage, J. C. (1966), Radiation from a realistic model of faulting, Bull Seism. Soc. Am.
56: 577–592.
Savage, J. C. (1974), Relation between P- and S-wave corner frequencies in the seismic
spectrum, Bull Seism. Soc. Am. 64, 1621-1627.
Scandone, P. (1979), Origin of the Tyrrhenian Sea and Calabrian Arc., Boll. Soc. geol.
ital., 98, 27-34.
Schwartz, S. Y. (1999), Noncharacteristic behavior and complex recurrence of large
subduction zone earthquakes, J. Geophys. Res. 104, 23,111-23,125.
Sato, T., and T. Hirasawa (1973), Body wave spectra from propagating shear cracks, J.
Phys. Earth 21, 415–431.
Selvaggi, G. (1998), Spatial distribution of horizontal seismic strain in the Apennines
from historical earthquakes, Ann. Geofis., 41, 241–251.
Serpelloni, E., M. Anzidei, P. Baldi, G. Casula, and A. Galvani (2005), Crustal velocity
and strain-rate fields in Italy and surrounding regions: new results from the analysis of
permanent and non-permanent GPS networks, Geophys. J. Int., 161, 861 – 880,
114
doi:10.1111/j.1365-246X.2005.02618.x.
Stabile, T. A., C. Satriano, A. Orefice, G. Festa, and A. Zollo (2011), Anatomy of an
earthquake crackling sequence on an active normal fault, submitted to Scientific Reports
Toksoz, M.N., D.H. Johnsont and A. Timur (1979), Attenuation of seismic wave in dry
and satured rocks-1. Laboratory experiments, Geophysics, 44, 681-690.
Vallée, M. (2004), Stabilizing the empirical Green function analysis: development
of the projected Landweber method, Bull. Seism. Soc. Am. 94, 394–409.
Valensise, G., A. Amato, P. Montone, and D. Pantosti (2003), Earthquakes in Italy: Past,
present and future, Episodes, 26(3), 245-249.
Velasco, A. A., C. J. Ammon, and T. Lay (1994), Empirical Green function deconvolution
of broadband surface waves: rupture directivity of the 1992 Landers, California
(MW=7.3), Bull. Seism. Soc. Am. 84, 735–750.
Walters, R. J.; J. R. Elliott, N. D’Agostino, et al. (2009), The 2009 L’Aquila earthquake
(central Italy): A source mechanism and implications for seismic hazard. Geophys. Res.
Lett. 36, L17312, doi:10.1029/2009GL039337.
Warren, L. M., and P. M. Shearer (2002), Mapping lateral variations in upper mantle
attenuation by stacking P and PP spectra, J. Geophys. Res., 107(B12), 2342,
doi:10.1029/2001JB001195.
Weber, E., V. Convertito, G. Iannaccone, A. Zollo, A. Bobbio, L.Cantore, M.Corciulo, M.
Di Crosta,L. Elia, C. Martino, A. Romeo and C. Satriano (2007), An advanced seismic
network in the southern Apennines (Italy) for seismicity investigations and
experimentation with earthquake early warning, Seismol. Res. Lett., 78, 622.
Winkler, K.W., and A. Nur (1979), Pore fluids and attenuation in rocks, Geophys. Res.
115
Lett., 6, 1-4.
Yano, T. E., G. Shao, Q. Liu, C. Ji and R. J. Archuleta (2009), Finite Fault Kinematic
Rupture Model of the 2009 Mw6.3 L’Aquila Earthquake from inversion of Strong
Motion, GPS and lnSAR Data. AGU, Fall Meeting 2009, Abstract S34A-02.
Zollo, A., A. Orefice and V. Convertito (2011), Scaling Relationships for Earthquake
Source Parameters Down to Decametric Fracture Lengths, submitted to J. Geophys. Res.
116
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