Manual 21391674

Manual 21391674
Chapter 1: Introduction
CHAPTER 1 OVERVIEW 1.1. INTRODUCTION
The magnetotelluric method provides the geophysicist with a frequency domain
electromagnetic tool that is not hampered by the presence of conductive
overburden or sampling frequencies that do not allow for deep penetration.
Variations in the earth's natural magnetic field supply frequencies ranging from
nearly DC to several kilohertz, thus giving one the ability to study the electric
substructure of the earth to great depths. The final results of magnetotelluric
soundings are log-log plots showing apparent resistivity as a function of depth
calculated from large amounts of data collected during a sounding. One of the
main problems affecting the quality of the results is the presence of artificial
electromagnetic sources that are too close to satisfy the assumption that the
electromagnetic energy consists of plane waves. Statistical reductions of the
data aim to minimise the effect of this 'noise'. Unfortunately, most of the basic
minimisation techniques assume noise with a Gaussian distribution. In reality
this is not the case and this leads to poor quality results. The aim of this study is
to compare two statistical minirnisation techniques that try to take the actual
distribution of the noise into consideration.
1.2. SUMMARY OF CONTENTS
Chapter 2 gives a brief description of various sources of natural electromagnetic
energy. It is important to be aware of the different sources since this will indicate
the optimal time to do magnetotelluric soundings. The distance of the sources
means that the electromagnetic energy is in the form of plane waves. This is
one of the fundamental
assumptions
made
in the
deduction
of the
magnetotelluric theory. Chapter 3 starts with this assumption and uses
Maxwell's equations to derive wave equations that describe the propagation of
plane electromagnetic waves through the earth. By applying the wave equations
to various geological models, it is possible to arrive at the equations describing
the relation between the electric and magnetic fields measured at a sounding
Chapter 1: Introduction
station. These fields are related via the impedance tensor and it is the noise in
this tensor that needs to be minimised.
Data acquisitioning and basic processing techniques are described in Chapter
4. One of the final results in this chapter shows the relation between apparent
resistivity and impedance.
Chapter 5 contains a discussion on various statistical methods used to minimise
the effect of noise in data. It starts out with the L1- and L2 norms that make the
assumption of normally distributed noise. Two methods that address this
problem are the Robust M-estimation and adaptive Lp norm techniques . The
robust M-estimation method uses a weight function to ignore outliers in the
data. This effectively causes the actual distribution to approach a normal
distribution. With the adaptive Lp norm technique the actual distribution of the
noise is used to determine the value of p that will be used to minimise the error.
These methods are applied to synthetic data with both normal and non-normal
error distributions and the results are compared.
Statistical reduction methods discussed in Chapter 5 are applied to real data in
Chapter 6. Data for the case study were collected between Sishen and Keimoes
along a traverse that crosses a number of tectonic boundaries. The final model
calculated is compared to a deep reflection seismic line that ran along the same
traverse . Chapter 7 discusses the final results obtained with the various
statistical techniques.
2
Chapter 2 : Sources
CHAPTER 2 NATURAL SOURCES OF ELECTROMAGNETIC ENERGY 2.1. GENERAL
Cagnaird (1953) based the theory of the magnetotelluric method on two important
assumptions:
• The source is a natural electromagnetic plane wave propagating vertically
downward into the Earth and
• The Earth has a one dimensional electrical substructure.
The naturally occurring electromagnetic plane wave originates from a variety of
sources and may comprise a wide range of frequencies, depending on the origin.
The higher frequency component mainly emanates from meteorological activities
such as lightning. Variations in the Earth's magnetic field linked to solar activity are
responsible for a low frequency field.
2.2. SOURCES RELATED TO SOLAR ACTIVITIES
It is well known that the geomagnetic field is composed of three parts - the main
field that originates from an internal source, the external field originating outside the
earth and local variations in the main field caused by magnetic material in the
earth's crust (Telford et aI., 1976). The variable nature of the external field is of
particular interest to us since it induces currents in the ionosphere which act as
sources of natural electromagnetic energy
Pulkkinen and Baker (1997) describe geomagnetic activity as 'the general term
used to define variations in the Earth's surface magnetic field caused by sources
external to the Earth.'
They point out that these variations are caused by
fluctuations in current systems within the ionosphere and magnetosphere
controlled by the variable nature of the solar wind, the interplanetary magnetic field
(IMF) or the geometrical relation of the sun and earth.
3
Chapter 2 : Sources
2.1.1. Solar wind
The close relationship between geomagnetic variations and solar activity
warrants a quick look at the basic morphology of the sun. Frazier (1985)
describes the sun as 'a series of concentric layers that interact continuously.'
Figure 2.1 shows a schematic diagram depicting these concentric layers.
In the solar core at extreme temperatures of 15 000
ooooe
and pressure
200 billion times the pressure at the earth's surface , hydrogen atoms are
fused together to form helium, releasing massive amounts of energy during
this process. As the energy passes through the radiation zone, decreases
in temperature and pressure and a change in wavelength transform the
gamma rays into X-rays and from there into ultraviolet and visible light. The
convection zone consists of a cooler, more opaque gas and here the energy
is moved upward by convection cells into the photosphere. The energy
finally reaches the photosphere from where it is emitted into space. Solar
gases are confined by magnetic loops form the sun's atmosphere or corona.
Holes in the corona allow the constant movement of gas particles into space
thus forrning what is known as the solar wind.
The particles emitted by the sun consist mainly of ionized hydrogen that
forms a plasma of protons and electrons (Kaufman and Keller, 1981).
Experiments with the Lunik space probes four decades ago revealed a flux
of positive ions of approximately 2 X 10
8
particles cm- 2 sec- 1 beyond a
distance of 39 earth radii (R E ) (Snyder et al., 1963). During the end of 1962
and into the beginning of 1963 the space probe Mariner measured the
velocity of the interplanetary plasma for the first time directly and determined
an average velocity of 504 km/s during the experiment (Snyder et aI., 1963).
2.1.2. Relation between solar wind and geomagnetic activity
The geomagnetic field presents a barrier to the solar wind stopping it at
roughly 10 RE and deflecting it away from and around the earth (Moore and
Delcourt, 1995). The protons and electrons are often deflected in opposite
4
Chapter 2 : Sources
directions causing a magnetic field that cancels the earth's magnetic field
where it occurs. The boundary thus formed is known as the magnetopause
(Kaufman and Keller, 1981). In the process the solar wind modifies the
shape of the geomagnetic field compressing it on the daylight side and
causing it to be extended on the opposite side (figure 2.2) .
............. . . ..
CORONA
.. ' PHOTOSPHERE
CHROMOSPHERE'·· .
CONVECTION ZONE
RADIATION ZONE
.. . .
Figure 2.1.
Simplified diagram depicting the morphology of the sun
(adapted from Frazier, 1985).
The variable nature of the solar wind's strength and velocity cause the
magnetopause to fluctuate. The size of the magnetosphere changes and
new ionospheric currents form (Pulkkinen and Baker, 1997). When the solar
wind is strongly enhanced, stronger magnetic effects known as magnetic
storms occur.
5
Chapter 2 : Sources
------­ -MAGNETOPAUSE
-­ ---­
--------------­
--­ --­
__----4---------___
MAGNETOTAIL
R,
60
---­ - ----­
- ­-
--- - -­
Figure 2.2. The Earth's magnetosphere (Moore , 1994).
2.1.3. Magnetic storms
Sunspots, areas of intense magnetic activity on the surface of the sun,
release energy in the form of solar flares and other eruptions. Already in the
previous century scientists observed that sunspots waxed and waned in
cycles of nearly 11 years. These cycles correlate directly to times of
increasing and decreasing geomagnetic activity. This and the fact that
increased geomagnetic activity occurs at roughly 27 day intervals (period of
the sun's rotation) led to the assumption that solar flares serve as the main
instigators of large geomagnetic storms.
Solar flares were held responsible for solar energetic particle (SEP) events
even when no flares were visible on the solar disk. These events were
believed to result from flares on the back side of the sun. Several models
were derived to explain the relatively long duration of most of these events
compared with the short lifetime of a flare. One explanation for this
6
Chapter 2 : Sources
phenomenon
was
that
the
solar
magnetic
field
extended
through
interplanetary space in the form of 'magnetic tubes.' Particles emitted by
flares diffused through the solar atmosphere until they reached the tube of
force that extended out to the earth at that time, slowly filling it and
increasing the flux of particles measured on earth (figure 2.3). Rapid
discharge of particles from the tube resulted in magnetic storms (Reid,
1964).
TUBE OF FORCE
FLARE LOCATION
Figure 2.3. Reid 's diffusive model for the initial phase of a solar
proton event (Reid, 1964).
However, in recent years coronal mass ejections (CMEs) have gained
prominence as presenting the crucial link between solar activity and
transient interplanetary disturbances that cause large geomagnetic storms
(Gosling et aL, 1990). During coronal mass ejection events 10
15
-
10
16
gms
of solar material are suddenly propelled outward into space at speeds
ranging from less than 50 km/s to greater than 1200 km/s (Gosling et aL,
1991). CMEs are not always observed in association with solar flares but
when they are temporally related, CMEs usually begin to lift of from the sun
before any substantial flaring activity has occurred (Gosling, 1993).
When CIVIEs have outward speeds exceeding that of the ambient solar wind
a shock forms in front of the ejection and the slower moving plasma ahead
is accelerated and deflected from its path (Sheeley et aL, 1985). Gosling et
aL (1990) show that a strong relation exists between these shock
7
Chapter 2 : Sources
disturbances, CMEs and large geomagnetic storms . Still, it is important to
note that not all CMEs and shock disturbances cause geomagnetic storms.
A prerequisite for the formation of major magnetic storms seems to be the
presence of an intense, long-duration, southward-directed, interplanetary
magnetic field (B z) within the CME or shock (Tsurutani and Gonzalez, 1992;
Lundstedt, 1996; Pulkinnen and Baker, 1997). The strong Bz may be a result
of either compression of the ambient interplanetary magnetic field (IMF) by
the shock, or of draping of the IMF about the fast CME or a combination of
compression and draping (Gosling and McComas, 1987).
2.1.4. Geomagnetic activity as source for MT soundings
Variations in the geomagnetic field induce currents to flow in the ionized
layers of the earth's atmosphere (at 80-160 km altitude). These currents in
the ionosphere lead to a displacement of mass and together the magnetic
and inertial forces give rise to magnetohydrodynamic waves (Kaufman and
Keller, 1981). By the time the magnetic effects reach the earth's surface
they are strongly modified and classified as micropulsations. These are
divided into continuous (Pc) and irregular (Pi) pulsations. They in turn induce
currents in conductive layers within the earth. Table 2.1 summarises further
subdivisions of the two classes as discussed by Kaufman and Keller (1981).
2.3. SOURCES RELATED TO THUNDERSTORM ACTIVITY
Transient electromagnetic fields (also called atmospherics or sferics) associated
with lightning provide the main natural energy at frequencies ranging from 3 Hz to
30 kHz. The electromagnetic field generated by a lightning stroke , shows high
energy density at high frequencies when observed relatively nearby. As the energy
propagates to greater distances through wave guide propagation, some lower
frequencies are enhanced while the higher frequencies are attenuated (Kaufman
and Keller, 1981). The measured field is a superposition of individual sferics
originating from thunderstorms around the world (Zhang and Paulson, 1997).
8
Chapter 2 : Sources
Table 2.1. Summary of micropulsation's characteristics.
Classification
Pc-1
Pc
Appearance
Time of occurrence
Cause
Discrete signal
Middle and low latitudes:
Kinetic
with gradually
nights and mornings
increasing
frequency (pearls)
Pc-2
High latitudes: noon and
afternoon
Two maxima on
Midday
geomagnetic
field
Two maxima on
Oscillations
Midday
spectrum
Two maxima on
amplitude
One maximum on
amplitude
produced outside
magnetosphere
Mid latitudes: middays
High latitudes: nigl1t
spectrum
Pc-5
plasma
spectrum
amplitude
Pc-4
magnetospheric
Disturbance in
amplitude
Pc-3
instabilities in
Generated
during onset of
magnetic storms
High latitudes: mornings
and evenings
spectrum
Interaction of
solar wind with
magnetopause
Pc-6
Pi
Pi-1
PiB
Groups of irregular
Occur with explosive
Transverse
variations with
phase of su bstorm
vibration of
periods less than
(22:00-05:00)
magnetosphere
15s
PiC
IPDP
boundary
Irregular variations
Occur in both explosive
with dominant
and quasi-stable phase
period of 5 to 1Os
of substorm
Decrease in
period during
16:00-01 :00
course of
Excited in auroral
zone
occurrence
Pi-2
Decaying
Related to force
sequence of
Associated with
9
lines of
Chapter 2 : Sources
oscillations with
explosive phase of
geomagnetic
periods of 60-1 OOs
substorm
field along which
and duration of 5­
auroral activity
10min
proceeds
Development of
Pi-3
Periods>150s
Night time
Kelvin-Helmholtz
instability at
boundary of
magnetosphere
10 Chapter 3 : Theory
CHAPTER 3
BASIC THEORY OF THE MAGNETOTELLURIC METHOD
3.1
INTRODUCTION
Cagnaird and Tikhonov developed the theory underlying the magnetotelluric
method independent of each other in the 1950's (Tikhonov, 1950; Cagnaird,
1953). They both observed that the electric and magnetic fields associated with
telluric currents that flow in the Earth as a result of variations in the Earth's
natural electromagnetic field, should relate to each other in a certain way
depending on the electrical characteristics of the Earth. Since then tremendous
advances have been made in the understanding, processing and interpretation
of the data. However, the fundamental principles and assumptions have
remained unchanged . This chapter presents the principles that form the basis
of the magnetotelluric method.
3.2
MAXWELL'S EQUATIONS
The magnetotelluric method is a frequency domain electromagnetic technique.
As with all electromagnetic methods the fundamental principles underlying the
technique are summarised in Maxwell's equations given in differential form in
equations (3.1) to (3.4) (Reitz et a!. 1979).
V · S= 0
------- (3.1 )
V · D =q
------- (3.2)
as
v x E =-­
at
aD
V x H=J+­
at
The symbols are declared in the glossary.
11 ------- (3.3)
------- (3.4)
Chapter 3 : Theory
It is important to have a clear understanding of these equations and therefore
they will be discussed separately in more detail.
V' . B = 0
3.2.1.
The divergence of a vector (X) is the limit of its surface integral per unit
volume as the volume (V) enclosed by the surface goes to zero (Reitz, et
ai., 1979).
V' . X
1
= v.....
lim v <f X· nda
0
s
------- (3.5)
In other words it describes the net flow through a surface enclosing the
source of the flow_ If V . X > 0 . thArA is
rI
nAt olltflow from the position of
= 0 , there
X. If V . X < 0 , there is a net inflow to the position of X. If V . X
is no net inflow or outflow.
Therefore, V'. B = 0 indicates that for a closed surface surrounding the
source of a magnetic field, the net result of the inflow and outflow per
unit volume is zero as the volume goes to zero. This implies that the
magnetic source has a negative and positive pole and that isolated
magnetic poles do not exist. Figure 3.1 illustrates this point.
(a)
, -(I'
(b)
,
"
I
'\-
\
It­
...J
'f",
,
.-:.
I
\
I
/
-<
'
........ ,
..(- -------~=-=-- ....-- -':>­
I
....
1.:.'
I
'1/
1-
" ,
Y
/
-~,-
....
I
\ '..
r-
,
I
"I'\
1
1
Y
-..l
\
..:;t
'
'
,
\
Figure 3.1 Maxwell's equation
V'. B = 0 implies that the situation
depicted in (a) prevails and that single magnetic poles as denoted in (b)
cannot occur.
12 Chapter 3 : Theory
3.2.2.
V' . 0
=q
(Gauss' law)
The electric flux across a closed surface is proportional to the net electric
charge (q) enclosed by the surface.
------- (3.6)
The electric displacement (0) includes the charge embedded in the
dielectric medium (EoE) as well as the polarisation charge (P). Reitz et al.
(1979) defines polarisation as follows:
A small volume element of a dielectric medium which is electrically
neutral has been polarised if a separation of the positive and
negative charge has been effected. The volume element is then
characterised by an electric dipole moment L1p that determines the
electric field produced by the small volume L1v at distant points. P
is the electric dipole moment per unit volume.
3.2.3.
V' x E = -
as
at (Faraday's law)
Through experimentation it was found that an electromotive force
(~
) is
associated with a change in magnetic flux (cD) through a circuit.
~
dcD
=- -
dt
------- (3.7)
The EMF is independent of the way in which the flux changes. The
minus sign indicates that the direction of the induced EMF is such as to
oppose the change that produces it.
Define the EMF around an electric circuit as
------- (3.8)
and the magnetic flux as
cD
=
LS·nda
13 ------- (3.9)
Chapter 3 : Theory
where da is an infinitesimal area and n is the unit vector perpendicular to
da. Equation (3 .9) therefore gives the integral of the normal component
of the magnetic field over a surface S.
Substituting (3.8) and (3.9) into (3.7) yield
{E.dl = - :t lB.nda
------- (3.10)
Stokes' theorem states that the line integral of a vector around a closed
curve is equal to the integral of the normal component of its curl over any
surface
bounded
by the curve
(1J.dl = 1V x F· nda).
Therefore
equation (3.10) can be written as
r V x E . n da = - ~
r B . n da
dt Js
------- (3 .11 )
Is V x E· nda = - Is ~ .nda
------- (3.12)
.ls
This holds true for all fixed surfaces, therefore
aB
at
V x E=--
V x H =J +
3.2.4.
------- (3.13)
aD
at (Ampere's law)
This law describes the magnetic field due to a current distribution. J is
the transport current density that consists of the motion of free electrons
or charged ions. The electric displacement D was defined in equation
(3 .6).
a;:
gives the variation of the electric displacement with time and is
called the displacement current.
It is worthwhile to discuss the definition of the 'curl' and examine this in
order to gain a better understanding of Ampere's and Faraday's laws.
Reitz et al. (1979) define the curl of a vector as the limit of the ratio of the
integral of the vector's cross product with the outward drawn normal over
14 Chapter 3 : Theory
a closed surface, to the volume enclosed by the surface as the volume
goes to zero.
\l x F
= lim ~ J n x F da
V-40
------- (3.14)
V '1s
The cross product of two vectors is the product of the magnitudes times
the sine of the smallest angle between the two vectors, with the direction
of the resultant vector perpendicular to the two vectors according to the
right hand screw rule. The curl of a vector can therefore be interpreted
as the tendency of a vector to rotate around an axis perpendicular to the
vector and the normal (Ellis and Gulick, 1986). Figure 3.2 serves to
illustrate this point.
nxF
n +---11.
Figure 3.2. The curl of a vector (integration of n X F over
the total closed surface divided by the enclosed volume
V as V goes to zero)
3.3.
WAVE EQUATIONS
A wave equation describes the wave propagation in a linear medium. To derive
the wave equation for the magnetic field, one considers the curl of equation
(3.4) (Reitz et aI., 1979).
aD
\l x \l x H=\l x J + \ l x ­
at
let D = eE, J =0 E and B = J10H and use (3.3), then
15 ------- (3.15)
Chapter 3 : Theory
v x V xH=
a
G(V x E)+ £-(V x
01:
aH
= -Gllo
8t -
E)
a2 H
£fl o 01: 2
Using the identity V x V x X = VV . X - V 2X and (3.1) lead to the wave equation
for the magnetic field
------- (3.16)
The wave equation for the electric field can be derived similarly by taking the
curl of (3.3). In a charge free medium where V . D
=0
this results in
------- (3.17)
Equations
(3.16)
and
(3.17)
describe
the
electromagnetic field
in
a
homogeneous, linear med ium with no free charge density.
3.4. APPLICATION
OF
WAVE
EQUATIONS
IN
THE
MAGNETOTELLURIC METHOD
In the development of the theory for the magnetotelluric method, we make the
following assumptions (Kaufman and Keller, 1981):
• The Earth consists of N horizontal layers, each with resistivity pn and
thickness hn (Figure 3.3) .
• A horizontal current sheet located above the surface of the Earth acts as a
source for an electromagnetic field that depends only on the vertical (z)
coordinate and the distribution of resistivities.
• The horizontal current sheet Ux) in the source plane induces a uniform
primary magnetic field (H oy) that does not vary with z (z is positive
downwards).
16 Chapter 3 : Theory
Temporal fluctuations of the primary magnetic field generate the primary
horizontal electric field (Eo x). Variations in the primary electric field cause
currents to flow in conductive layers in the Earth which in turn serve as source
for the secondary electromagnetic field. Since we assume that the Earth
consists of homogeneous layers, the current density does not change over the
horizontal planes and the secondary electromagnetic field also consists of an
electric field in the x-direction and a magnetic field in the y-direction.
.
••
~ ~= =- ~=
-:z:= ~- ~?~~- --""-
~ -~'-~ "*'"1;
.
Eox(t)
•
Horizontal current sheet jx
z
Layer 3
Layer N-1
Layer N
Figure 3.3 Schematic diagram depicting the assumptions made during the
development of the MT theory
The total electromagnetic field is time dependant. Assume that the electric and
magnetic components can be written as
17 Chapter 3 : Theory
= Exe- iwt
------- (3.18)
H(r, t) = Hye - iOlt
------- (3.19)
E(r, t)
In the above equations the time dependency e-i(J)t=cosO)t - isinO)t implies an
assumption that the fields are continuous harmonic oscillators .
Substitute (3.18) into (3.17) and remember that the electric field varies only in
the z-direction. The wave equation for the electric field induced under the
assumptions made at the beginning of the section therefore is
. [o2E
.
az2 + f.!crE
e -Iwt
__
x
10)
x
+ O))J.D
~
x
=
0
------- (3.20)
According to Kaufman and Keller (1981) the displacement current can be
neglected in the MT method. Equation (3.20) then becomes
------- (3.21 )
In order to satisfy (3.21) the electric field must have the following form in each
layer (n)
------- (3.22)
where
------- (3.23)
is the wavenumber. Skindepth (8) and wavenumber are related as follows
1+i
k=8
------- (3.24)
Use (3.13) and (3.22) to determine the form of the magnetic field in each layer
18 Chapter 3 : Theory
------- (3.25)
In (3.22) and (3.25) we assume that the tangential components of the electric
and magnetic fields are continuous when passing through the interface
between two layers. It is now possible to examine the behaviour of the electric
and magnetic fields in different geological scenarios.
3.4.1.
Uniform half-space
In a uniform half-space the electromagnetic energy must decrease with
increasing depth since energy is transformed into heat. Since
iz
-z
e ikz = e&e &
------- (3.26)
and
e­ ikz
- iz
=
z
e b e&
------- (3.27)
it is clear that in both (3.22) and (3.25) the first term between brackets
represents the part of the field that decreases with increasing depth and
the second term that part of the field that increases with increasing
depth. For a uniform half-space, with the assumption that the field should
approach 0 as z becomes very large, the electric and magnetic fields
therefore reduce to
------- (3.28)
and
------- (3.29)
At the surface of the earth where z=O, (3.28) and (3.29) reduce to
Ex(O)
= e-iwtA
and
19
------- (3.30)
Chapter 3 : Theory
------- (3.31)
The ratio of the electric field to the magnetic field is known as the
impedance, Z.
Ex
fl
Z xy - --CD­
- H
k
------- (3 .32)
y
If the electric field is orientated in the y direction, the impedance
Z xy
can
be derived in a similar fashion as
------- (3.33)
1
Ii =
By using (3.23) and the fact that
e
-~
4
(see Appendix A for
derivation), (3.32) can be written in terms of the apparent resistivity p
_
ffi
~
- i1t
-34
Zxy - 2n 5T' 10 e
ohm
------- (3.34)
Since the impedance is complex, it has an amplitude and a phase. The
amplitude is given by the modulus of Z
Izxy l = 2n~ :T . 10-
3
ohm
------- (3.35)
and the phase by the tangency of the ratio of the imaginary part to the
real part.
------- (3.36)
For Zyx the amplitude is the same as for Z xy and the phase is
------- (3 .37)
3.4.2.
Impedance of a two-layer medium
The impedance for a two-layer medium can be derived from (3.22) and
(3.25). Figure 3.4 depicts a model of a two-layer medium.
20 Chapter 3 : Theory
,/
,/
,/
,/
Pl
h1
P2
h2
,/
,/
,/
X
..
,/
,/
,/
,/
I
IZ
Y
Figure 3.4 Two-layer medium
In the first layer the electric and magnetic fields are
------- (3.38)
and
------- (3.39)
The second layer is considered to be a half-space, and the electric and
magnetic fields are
------- (3.40)
and
------- (3.4 1) Using the impedance ratio and the boundary conditions given in (3.42)
and (3.43) the impedance relation (3.44) for a two layer medium can be
derived (Kaufman and Keller, 1981).
hn
------- (3.42)
z=h n
------- (3.43)
Z=
21
1. 1<796
'L-?- \ 0
b 15 '3"75" ~ q,1
Chapter 3 : Theory
------- (3.44) 3.4.3. Electromagnetic fields
in
the
presence
of a two­
dimensional structure
After developing the impedance for a layered medium, it is important to
consider what effects two-dimensional structures will have on the
electromagnetic field. Two scenarios will be considered, namely
• electric field parallel to the vertical structure (E - polarisation)
• electric field perpendicular to the vertical structure (H - polarisation)
E-Polarisation
Assume the two-dimensional structure strikes in the x-direction and the
primary electric field is directed along the x-axis. The primary electric
field does not intersect the surface and the total electric field has only an
Ex component. As a result all derivatives with respect to x are zero.
Maxwell's equation describing the magnetic field due to a current
distribution (3.4) therefore becomes
\1 x H
=DE ------- (3.45)
(the displacement current is negligibly small compared to the conduction
current for
the
frequencies
and
conductivities
measured
in
magnetotellurics (Kaufman and Keller, 1981 )).
Equation (3.45) reduces to
= 8H z
aE
8y
x
and (3.3) together with H = Hoe- iwt yield
22
8H y
8z
------- (3.46)
Chapter 3 : Theory
1 BEx
H =-Y
------- (3.47)
icu).! Bz
and
1 BEx
H =--Z
icu).! By
------- (3.48)
Therefore, in the case of E-polarisation, the magnetic field has a vertical
component.
H-Polarisation
In this case the primary electric field is directed perpendicular to the x­
striking two-dimensional structure. Electric charges develop on the
structure and the electric field has components Ey and Ez . The electric
field does not change in the x-direction and therefore all derivatives with
respect to x are zero . Ampere's law (3.45) again gives the magnetic field
associated with this current distribution, with
crE = BHx
Y
Bz
------- (3.4 9)
and
BHx
crE
=­
Z
By
------- (3.50)
These equations are substituted into (3.3) and yield the following
equation describing the magnetic field
------- (3.51)
When the electric field is directed perpendicular to the two-dimensional
structure, the magnetic field does not have a z-component.
23 Chapter 3 : Theory
3.4.4.
Tensor impedance
From the previous section, it is clear that the relative orientation between
structures in the earth and the primary electromagnetic field plays a
crucial role in impedance calculations. This is further complicated by the
fact that the orientation of the primary electromagnetic field changes with
time and several orientations may be present at a certain time resulting
in elliptical polarisation. In an attempt to deal with this problem the tensor
impedance was derived (Sims et aI., 1971; Kaufman and Keller, 1981).
In matrix form the tensor repesentation of (3.32) and (3.33) is
------- (3.52)
Therefore, the electric field in a certain direction may depend on
magnetic fields parallel and perpendicular to it and the impedances can
vary with time as the polarisation of the source field changes (Swift,
1986).
24 Chapter 4 : Data acquisitioning and processing
CHAPTER 4 DATA ACQUISITIONING AND PROCESSING The collection of magnetotelluric data in the field entails setting up the sounding
station and recording until adequate data have been gathered in the appropriate
frequency range. Time spent on a sounding depends on the required survey depth
and the level of natural electromagnetic activity. Basic processing involves a
number of steps, one of the most important being to determine whether natural
electromagnetic events occurred.
4.1. DATA ACQUISITIONING
4.1.1. Field Setup
It is clear from the development of the basic theory that inhomogeneities in
the substructure of the earth cause secondary electric and magnetic fields
that each have components in the
X-,
y- and z- directions. For this reason it
is necessary to measure three perpendicular magnetic components and two
horizontal perpendicular electric components. Two horizontal perpendicular
magnetic components are also measured at a remote station away from the
base station. This is based on the assumption that the noise will be different
at the two stations but the events will be the same. A typical field setup is
shown in Figure 4.1.
4.1.1.1.
Measuring the electric field
To measure the electric field, the potential difference between two
electrodes is measured. The electrodes must preferably be made of non­
polarising material such as a metal immersed in one of its salts in a porous
cup. The voltage difference between a pair of non-polarising electrodes is
relatively stable, whereas for metal electrodes potential differences resulting
25
Chapter 4 .' Oata acquisitioning and processing
from electrochemical reactions at the metal surface can be present
(Kaufman and Keller, 1981).
Remote station
N
- - - - -C==:::J
Hry
E
o
o
co
+1
/ I
/ I
I I
I
~
-D
/
<.)
I
<U
-
ro
><
ro
"
Local station
Ex I
I
I
6
o
1 I
I I
I I
E I
ol.()
I
+1
/
I
I I
I
I
-- -
I'
:
..­
I
J I
I' ~ I I
I
I
•
.... ,..
,
/
I'
"
J
/
/
/ I I
/I I
\:
11
'"
••
__ "
=======> . ....... . . . ..
I
/
I /
I I
/ / I I I
/1[ 1/
I
".. I
I I' " ...
/ I / I /
'\:
I "
..... ~ .....
/
/,~.-
I I III I I
I
"
11 ../
± 150m
Figure 4.1. Field setup for Magnetotelluric station
26 ======"/
Chapter 4 .' Data acquisitioning and processing
Contact resistances at electrodes can cause problems. In a dry soil or bare
rock the contact resistance of the electrode may be very high and the soil or
rock needs to be saturated with water or sometimes even saltwater to
improve the contact. It is also true that when the contact resistances are
high electrostatic and electromagnetic noise cause problems.
Electrode intervals vary from site to site. According to Ohm's law potential
(V) is proportional to resistance (R):
V= I R
------- (4.1 )
Therefore, as the resistivity of the geology increases the strength of the
measured signal will increase, if the current (1) remains constant. In a
conductive earth, the signal will decrease and an increase in electrode
spacing is necessary to amplify the signal strength (V = E I). It is important
to remember that noise will behave the same as natural signals and
consequently the amount of noise present will influence the choice of
electrode spacing.
Another important factor in laying out the electrodes is the geological
substructure. For a small electrode spacing local variations in resistivity near
the surface will negatively influence the measurements. If, for example, the
electrodes are placed in a localised shallow conductor, the electric
measurements will be strongly influenced by this feature while the magnetic
measurements will be almost unaffected. If a longer separation is used
between the electrodes, the average electric field will be more characteristic
of the dominant electric field and the dominant resistivity in the surface
layer.
Coaxial cables are used between the electrodes and the recording
equipment. Since motion of the cables must be minimised it should be laid
flat on the ground .
27
Chapter 4 : Data acquisitioning and processing
4.1.1.2.
Measuring the magnetic field
Magnetic induction coils are used to measure the magnetic field intensity.
The coil detects the rate of change of the magnetic field and the
electromotive force (EMF) induced in the coil is
dB
EMF = -nA(Cit) cos e
------- (4.2)
for a coil with negligible resistance, inductance and capacitance (Kaufman
and Keller, 1981). In (4.2)
n = number of turns of wire
A
=area of the coil (m 2 )
B = magnetic induction (T)
e = angle between
the magnetic field H and the normal to the plane
of the coil.
The voltage measured by an induction loop is proportional to the oscillation
frequency or time rate of change of the magnetic field that cuts through the
loop.
The coils are buried beneath the earth to minimise the effect of wind and
changes in temperature. Care must be taken to level the coils perfectly in
the
horizontal
and
vertical
positions
for
the
various
components
respectively.
4.1.2. Data sampling
The aim of a magnetotelluric sounding is to deduce an image of changes in
the electrical substructure of the earth with depth. For this reason data are
sampled at different frequencies. Frequency relates to depth via the skin
depth
28
Chapter 4 : Data acquisitioning and processing
8=
f2
------- (4.3)
V~
Data are recorded from both the electrodes and the magnetic coils in
analog form and need to be digitised. The main problem with digitising a
signal is frequency aliasing. The Nyquist criterion states that at least two
samples must be taken over each cycle of a frequency to be certain that the
frequency can be recognised. The sampling interval must therefore be
chosen in such a way that all the frequencies contained in the signal can be
recognised and not just those that we are interested in. A possible solution
is to filter out unwanted frequencies with an analogue filter before digitising
the signal. Unwanted frequencies such as 50Hz and its harmonics can be
filtered out before data processing starts.
The range of frequencies finally utilised at a sounding station (and therefore
the depth of investigation) depends mainly on the aim of the survey and the
geo-electrical substructure. According to the Nyquist criterion the highest
frequency that can be identified with a sampling period
1
f -­
N -
~t
is
------- (4.4)
2~t
For example, if the sampling frequency is 3000 Hz, the highest frequency
that can be recovered would be 1500 Hz. The lowest frequency that can be
measured with a specific sampling period
~t
is
1
fL = (n * ~t) / 2
where n is the number of points sampled (n = 2
------- (4.5)
m
because the first step of
processing is transformation to the frequency domain). Therefore, if we
sample 2048 points at a frequency of 3000 Hz, the lowest frequency that
29
Chapter 4 : Data acquisitioning and processing
can be identified is approximately 3 Hz. From equation (4.5) it is clear that
the range of frequencies can be improved by increasing the number of
sampling points.
4.2. DATA PROCESSING
The steps involved in data processing are shown in Figure 4.2. Each of the steps
will be discussed in more detail.
4.2.1. Transformation to Frequency Domain
Sampled time series data are transformed to complex amplitude spectra
using Fourier transformation. The Fast Fourier Transform is the algorithm
most widely used for this operation and the computational form to be
implemented is
N-1
X(n)
=I
-i 2 ;rk
N
xo(k)e,
n = 0,1, .. . , N -1
------- (4.6)
k=O
where xo(k) is the sampled time function.
4.2.2. Auto- and cross power spectra
The auto- and cross spectra are the frequency domain equivalents of auto­
correlation and cross-correlation in the time domain. They are defined by
Swift (1986) as follows:
Auto-spectra
<Ex Ex*>, <Ey Ey*>, <Hx Hx*>, <Hy Hy*>
Cross-spectra
<Ex H;>,<Ex H;>,<HxE ;> , e~ .
Ex = Ex(ro), Ey = Ey(ro), etc. are the Fourier spectra of the time domain
functions and Ex* = Ex*(ro ), Ey* = Ey*(ro), etc. are the complex conjugates of
30 Chapter 4 : Data acquisitioning and processing
the Fourier spectra. The brackets <> represent an averaging in time for
finite bandwidths.
<D
r
®
~
Transformation to
frequency domain
(FFT)
Rotate impedances
to principal axes
~
~
~
®
r
®
Auto- and cross
power spectra
Apparent resistivities
I
~
~
®
®
Coherences
Skew, Tipper, etc.
~
@
If coherences high enough calculate impedances
-
Figure 4.2. Processing steps
4.2.3. Coherences
It is necessary to determine whether a data set contains actual events or
only noise. An event must appear simultaneously on at least two related
31
Chapter 4 : Data acquisitioning and processing
components, e.g. Ex and Hy but is usually visible on all the components. The
following discussion is taken from an article on coherence functions by
Reddy and Rankin (1974).
We can view the MT system as a multi-input linear system where Hx and Hy
are the inputs, Ex and Ey are the outputs and Zzz, Zxy , Zyx and Zyy are the
frequency response functions. Three types of coherence functions can be
used to analyse the data quality:
• Ordinary coherence: Coherency between the output and each of the
inputs, e.g. between Ex and Hx or Ex and Hy. High ordinary coherences
between Hx and Ey and between Hy and Ex indicate a linear relation
between the inputs and outputs.
------- (4 .7)
• Multiple coherence: Coherency between the output and all of the inputs,
e.g. between output Ex and inputs Hx and Hy. High multiple coherences
indicate good signal to noise ratios.
------- (4.8)
• Partial coherence: This is the coherency between the output and a
specific input after the effect of the other inputs has been removed by
least-squares prediction from the specified output and input. For e.g.
between Ex and Hx after the effect of Hy has been removed by least
squares prediction from Ex and Hx. High partial coherences between Ex
and Hy and between Ey and Hx and corresponding low partial
32
Chapter 4 : Data acquisitioning and processing
coherences between Ex and Hx and between Ey and Hy indicate that the
rotation angle corresponds to the principal direction. The reason for this
being that in the principal direction Zxx and Zyy is close to zero. This
means that Ex
=
ZxxHx is close to zero and therefore the partial
coherency between Ex and Hx is low.
------- (4.9)
The equation looks similar to (4.7) but it differs in that the effect of the other
input (H y in this case) has been removed. The partial coherency can also be
formulated in terms of the multiple and ordinary coherences:
------- (4.10)
4.2.4. Impedances
If the coherences are high enough, indicating that there may be a true event
present in the data, we can proceed to calculate the impedance tensor
elements. This can be done in two ways, one in which only the components
measured at the local station are used and another in which the remote
station components are incorporated.
4.2.4.1.
Zij
Single station impedance
in equation (3.52) can be estimated in a least squares way (Sims et aI.,
1971). Young (1962) states the principle of least squares as follows: the
most probable value of a quantity is obtained from a set of measurements
by choosing the value which minimises the sum of the squares of the
deviations of these measurements. Deviation is defined as the difference
between any measurement in the set and the mean of the set. For a set of
33
Chapter 4 : Data acquisitioning and processing
measurements Xi, the most probable value of X is that which minimises the
quantity
n
L(X-
XY
;=1
For this equation to be a minimum
d n
-L(X-XY = 0
dx ;=1
------- (4.11)
Apply this to (3.52) (n is the number of measurements at a specific
frequency):
II
If'
= L (Exi - (Zu HXi + Z Xy H y J )2
------- (4.12)
i=!
Setting the derivatives of If' with respect to the real and imaginary parts of
Zxx equal to zero yields
n
n
n
;= 1
;=1
;=1
L Ex;H:; = ZxxL Hx;H:; + ZxyL Hy;H:;
------- (4 .13)
Setting the derivatives of If' with respect to the real and imaginary parts of
Zxy equal to zero yields
n
n
n
;=1
;= 1
;=1
L Ex;H~; = ZxxL Hx;H~; + ZxyL Hy;H~;
------- (4.14)
The solutions in (4 .13) and (4.14) minimise the error caused by noise on Ex.
By taking another least-squares estimate the noise on Hx can be minimised .
The various least squares estimates results in the following equations:
34 Chapter 4 : Data acquisitioning and processing
< ExE: >= Zxx < HxE: > +ZXy < HyE: >
----- (4.15)
< ExE: >= Zxx < HxE: > +ZXy < HyE: >
----- (4.16)
< ExH: >= Zxx < Hx H: > +ZXy < HyH: >
----- (4.17)
< ExH: >= Zxx < HxH: > +Z Xy < HyH: >
----- (4.18)
where the terms between brackets are the auto- and cross spectra and the
brackets indicate the average spectra over finite bandwidths.
By substituting (4.15) in (4.16), (4.17) and (4.18), (4 .16) in (4.17) and (4.18)
and (4.17) in (4.18), six estimates for the impedance tensor element Zxy can
be determined :
----- (4.19)
------(4.20)
------(4.21 )
----- (4.22)
----- (4 .23)
----- (4 .24)
Six estimates for Zxx can be determined in a similar way.
35
Chapter 4 : Data acquisitioning and processing
Following the same approach six estimates each for Zyx and Zyy can be
determined.
4.2.4.2.
Remote reference impedance calculations
The main problem in using the impedance estimates derived in the previous
section is that the auto-spectra of functions that contain noise may severely
bias the estimates. In order to address this problem, Gamble et al. (1979)
proposed the use of a remote reference station. At this station two
horizontally
perpendicular
magnetic
components,
H xr
and
H yr ,
are
measured . The noise at this station should not correlate with the noise at
the local station. Multiply the two linear relations in (3.52) with Hxr* and Hyr*
----- (4.25)
----- (4.26)
----- (4.27)
----- (4.28)
Solve these four equations for the impedance tensor elements:
----- (4.29)
----- (4.30)
----- (4.31)
36
Chapter 4 : Data acquisitioning and processing
----- (4.32)
4.2.5. Rotation of impedance tensor
The impedance estimates in the previous section were calculated for the
measuring axes x, y. In a two-dimensional earth the electric field in one
direction may depend on magnetic field variations both parallel and
perpendicular. The ideal is for the x-axis to point north in a one dimensional
case or parallel to the strike in a two dimensional case (Hobbs, 1992). In the
two dimensional case this will minimise the effects of the ExHx and EyHy
terms. However, the strike direction of the two-dimensional structures may
vary with depth and for this reason data are measured with x directed along
magnetic north. The next step in data processing is to rotate the calculated
impedance elements to the principal axes (parallel and perpendicular to
strike) using the following equations:
Zx·x.(a) = ~ ((Z xx + Zyy ) + (Z xx - Zyy ) cos 2a + (ZXy + Zyx ) sin 2a
----- (4.33)
Zx·y·{a) = ~ ({Z XY - Zyx) + (Z XY + Zyx ) cos 2a + (Zyy - ZxJ sin 2a ----- (4 .34)
ZY'x. {a)
= ~ ({ZYX -
Zy.y. {a)
= ~ ({Zxx
ZXy) + (Zyx + Zxy ) cos 2a + (Z yy - ZXX) sin 2a ----- (4.35)
+ Zyy ) - (Z xx - Zyy ) cos 2a - (Z XY + Zyx) sin 2a ----- (4.36)
The principal axes are those which maximise Zx' y' and Zy'x' (principal
impedances) or minimise Zx'x' and Zy'y ' (auxiliary impedances). The
impedance elements are rotated a few degrees at a time until the desired
maximum or minimum is found.
Rotation of the data assuming a two dimensional earth as described above
was proposed by Swift (1986). More recently various authors have given
attention to the effect of three dimensional conductivity distributions on the
impedance tensor (e .g. Bahr, 1988, 1991; Groom and Bailey, 1989, 1991).
37
Chapter 4 : Data acquisitioning and processing
The three dimensionality of the earth's electrical structure will ensure that
the
diagonal
elements
of
the
tensor
never
disappear.
Several
decomposition schemes for the measured impedance tensor have been
proposed by these authors. Bahr (1988, 1991) chose what he called the
principal superimposition model to be a local three dimensional anomaly
over a regional two dimensional structure. He used certain parameters to
classify different distortion
types and
subsequently decide whether
decomposition of the impedance tensor is necessary (Bahr, 1991). Figure
4.3(a) shows a schematic summary of his distortion classification process.
The equations referred to in this diagram appear in figure 4.3(b).
4.2.6. Apparent resistivity
The final processing step is the calculation of the apparent resistivity as a
function of frequency using Cagnaird's (1953) formula
Pij
= ;f IZijl2
i,j = x, Y
------- (4.37)
The apparent resistivities are shown on a log-log graph as a function of
frequency. Spies and Eggers (1986) noted that curves calculated with
equation (4.37) usually show an oscillation at low frequencies. Since these
oscillations are not present in the time domain they concluded that the
oscillations are artefacts of the frequency domain representation and
suggested using some alternative forms of (4.37)
2
i,j
= x, Y
------- (4.38)
= 5f ~m(Z):r
i,j
= x, Y
------- (4.39)
Pij
= ;f Um(Z2)]
i,j
= x, Y
------- (4.40)
Pij
= ;f IZ21
i,j
= x, Y
------- (4.4 1)
Pij
= 5f [Re(Z)f
Pij
2
38 "0
,0)0) 3
CD .......
"T\
to
..,c::
Ctl
,CD
,J::a.
CN
OJ
D)
--- (J)
Coordinate
Impedance . . - transformation
6)
I
(~.S)
angle a,
4 - Strike
(Swift) (eq, 4)
1
t
Skew (K) (eq.2)
0)
::::l'"
-'
~
to
to
~
»
Phase
difference oq,
(eq.8)
~ ...oq,«A ?
t
-+0
0
Yes
; 20 anomaly Pllrely local
~
<0.05 ? ~Local 30 anomaly ----JO' Phase c!>
Dimensionality I III( Yes K<O 1 ? ~Phase difference ~
(eq.3)
(.
II
(eq .9)
p. ",/'
I over layered Earth
(eq.l0)
I >O.1 ?
D­
>.
}o
.NO
Layered halfspace (Cagnaird.1953)
O)
co
w
to
~>0 .3? Yes~' RegionaI30
/
anomaly
~o
.
03
3
(J) o
::::l'" 0
Regional strike a ~ <0.1? Ye); Regional strike
(eq .13)
, Jl.. J
(eq.12)
-·L
~,
:::J
co
....... Coordinate
transformation ~Principal phases c!>.i c!>., ~ Regional strike a
(eq .S)
(eq.19)
.
(eq.12 or 13)
L
::::l'"
..,
CD
f
J
Regional 20 anomaly with
strong local distortion
()
c
0'
:::J
0
-+0
D-
Vi' .......
Skew angle P ~
(eq.17)
~
z"y<,z,'X'
or z"...«z".,. ? Regional20
anomaly
in rotated
Yes
_?
•
~" p , = P2 - p...: ~
0'
III
iii
III
()
..Q
c::
y
en'
g.
s'
~ ·P'+~ 2 ·900?
CO
III
::J
Q,
/
~ ../
~yes
0
;::::l.
:::J
p,<so and P2<20 0 ? or
.Jl 2<So and p,<200
Impedance tensor
(Eggers. 1982) (eq.1S)
+-
Regional 20 anomaly with
weak local distortion
"0
""CJ
Coordinate
transfonnation
(eq.S)
Skew angles P,. P2
(eq.t4)
t
0)
.......
"
III
<D
.....
~.
+
()
0)
"",.
::r
a",m
"0
Regional strike of neighbouring
sites With less distortion
l
Coordinate
r' lmpedance
transformation ~
tensor
(eq. 15)
(eq.S)
d
()
([)
en
en
S'
CO
Chapter 4 : Data acquisitioning and processing
Equation 1:
Equation 2 : Skew
Modified impedances
s, =zX7, + z 'I'f
- z."
0, = z"
S2= z.~y + Zyx
S;
= S, cos(2a)
0;
- 0, sin(2a),
Equation 9: Phase difference
=0,
(0
Zv'x'
=
Z x.v'J '= ( , 0 ,
0
S2 -
4> =
(Of + sO
I
~--,:;--~
O~
as
= -tan
.
4
Equation 7: Anisotropy
S~ + 0 ~J
O2
A=
0
11 =
10 21
(zxx - zvv )(z:v + z~x)+ (z:x + Z~y )(z xv + Zvx)
IZxx - Zvvl- ~ IZxv+ Zvxl'
2
7
Equation 8: Phase difference
ZV'x'
,
Equation 12: Regional strike
([S" S2] - [0,.0 2 ])
[S,.O,] + [S2,02])
(1[0,.S2 ]1-I[S,,02 ]IF
ar9(S, - 0,)
_I
Re(ZX'V')
Equation 11: Regional skew
Equation 10: Phase
(1[0,,8 2]1 + I[S,.02 ]IF
I.l =
z
S,'=S,
,
I =
Equation 6: 20 Impedance tensor
Equation 5: Coordinate transformation
0 ,' = 0, cos(2a) + S, sin(2a).
IS"
K=iDJ
D2 = Z'1.'f - Zyx
Equation 4: Strike angle (Swift, 1986)
Equation 3: Dimensionality
tan(2a psm ) = (
10 21
,
1 (B,A 2 + A,B2 + C,E 2) . [ 1 (B,A 2 + A,B2 + C,E 2)2 (B,B 2 - C,C 2)]2
tan(2a'2)=±
2
-(
,
2
(A,A 2 - C,C 2)
4
(A,A 2 - C,C 2)
A,A2 - C,C 2)
A, = ([S,.D,l + [S,.D,]) casto)
A, = ({S"D,) + {S,.D,}) sin(S)
B, = ([S"S,] - [0, ,0,]) cos(o)
B, = ({S, .S,) - {D,.DJ) sin(S)
C, = ([D"S,l
·C, = ({D,.S,) - {S,. D,}) sin(8)
- [S"D,]) cos(o)
E, = ({S,.S,) - {D"D,)) sin(S)
Equation 14: Skew angles
-Zx'x'
tan(13,) = Zv'x'
Equation 15 : Impedance tensor
ZV'v'
tan(132) = Z x'v'
Equation 16: Impedance tensor (Eggers, 1982)
-Z = (- a'2 Znv'x· a"Znx'v'J
- a22 Z nv'x·
a2,Znx'V'
[0
,
2
o -f- det(Z)
"'.2= -f±
Equation 19: Principal phases
Zp = T~TZ
tan(<Pe)=
tan(~)
=
K
,
[(lm(zxx)Y + (,m(zyxW]i
Equation 18: Rotation
J2
Equation 17: Skew angle
tan(q,e,) =
,
[(lm(zxv)Y + (Im(Zvv )YY
,
[(Re(Zxv)r + (Re(Zvv
[(Re(Z xx)t + (Re(zvxWY
Figure 4,3(b). Equations used in Figure 4.3(a)
40 )rr
Commutators:
[C"C,] = Re(C,) Im(C,} - Re (C,) Im(e,)
{C"C,} = Re(e,) Re(C,) + 1m (e, ) Im(C,)
Chapter 4 : Data acquisitioning and processing
4.2.7. Other parameters calculated during processing
A number of additional parameters can be calculated from the impedance
tensor and can assist in gaining a better understanding of the earth's
electrical substructure.
4.2.7.1.
Skewness
When the impedance tensor has been rotated to the principal direction, Zxx
and Zyy will be very small if the earth is laterally uniform or two-dimensional ,
but larger when the earth has a three dimensional structure (Vozoff, 1972).
At the same time the difference between the elements Zxy and Zyx is large
when the earth is strongly two-dimensional and small otherwise. The ratio
------- (4.42)
is known as the 'skewness' ratio. If S is large the structure of the earth
appears to be three-dimensional for that specific frequency range.
4.2.7.2.
Tipper
We saw in equations (3.47), (3.48) and (3 .51) that the magnetic field does
not have a vertical component when the electric field
is directed
perpendicular to the strike direction (H-polarisation). Therefore, the vertical
magnetic component can be used to determine the strike direction of the
two-dimensional structure (the horizontal direction in which the magnetic
field is most highly coherent with Hz is perpendicular to the strike). In
defining the 'Tipper' parameter we assume that Hz is linearly related to Hx
and Hy (Vozoff, 1972). At each frequency
Hz = AHx + BHy
41
------- (4.43)
Chapter 4 : Data acquisitioning and processing
where A and B are unknown complex coefficients. These coefficients (A,B)
can be thought of as 'tipping' part of the horizontal magnetic field into the
vertical and is therefore called the tipper. The tipper (T) is defined as
------­ (4.44)
with a phase
8=
(A~ + A~) arctan( :i ) + (B~ + Bn arctan(:i )
' T2
'
42 ------- (4.45)
Chapter 5: Statistical data reduction
CHAPTERS STATISTICAL REDUCTION OF DATA 5.1. GENERAL
In chapter 3 we derived the basic equations used in MT (eq . 3.52). In reality, in
the presence of noise the equations look as follows
Ex =Zxx Hx + ZxyHy + r
----- (5.1 )
Ey = ZxyHx + ZyyHy + r
----- (5.2)
where r represents the noise component. In the field N data sets are collected at
a specific frequency. The above equations in matrix form are
[E,] ~ [H"HYl[~:]+[r'1
~Y ]~ ~, H
y,
g:]
+ [r,]
----- (5.3)
----- (5.4)
In general the equations are written as
x=
u~
----- (5.5)
+r
with x an Nx1 matrix representing the electric field components, U an Nx2 matrix
containing the measured magnetic field components,
~
the 2x1 impedance matrix
and r an Nx1 matrix containing the noise component. Since the impedance
tensor is indicative of the properties of the underlying geology, there is only one
tensor for N sets of data recordings at a particular frequency. If the data were
noise free and consisted only of true natural electromagnetic events, it would be
sufficient to do only one recording per frequency. In reality noise contamination is
an ever-present problem in magnetotelluric data collection and consequently it is
necessary to record several data sets at a specific frequency. From a statistical
point of view the aim is to determine the impedance tensor that most Ilrohrlhly
represents the electrical properties of the underlying earth .
In order to determine the impedance value, it is necessary to minimise the error
between observed and calculated values, for example between
43 Eobserved
and
Chapter 5: Statistical data reduction
Ecalcualted
(as calculated using equation (3.52)). Figure 5.1 shows a schematic
diagram of this process.
• Calculated using a
first estimate of Z xy and Z xx
o Calculated using a second
estimate of Z Xy and Z xx
I'
EXC1
>.
N
iC
x
o
.~aI1
>.
::r:
-*
.- ...,.
EXC2
o
.-'-
Exo Residual 2 Figure 5.1 Visualisation of the minimisation of a residual
The objective is to minimise the residuals between N calculated and N observed
electrical components for an estimate of Zxx and Zxy.
5.2. II AND l2 NORMS
A possible solution to this problem is to use the Least Squares method to
determine
~.
The principle of least squares was outlined in section 4.2.4.1. as the
most probable value of a quantity obtained from a set of measurements by
choosing the value that minimises the sum of the squares of the deviations of
these measurements (Young, 1962). For a set of measurements
Xi
the most
probable value of X is that which minimises the quantity
----- (5.6) with x a variable that can be varied to obtain the minimum value of the function.
For this function to be a minimum the following condition must be satisfied:
d
N
-L(X-xY
=0
dx ;-1
44 ----- (5.7)
Chapter 5: Statistical data reduction
The derivative of a sum of terms is equal to the sum of the derivatives, therefore
1 N
x=- LXi
N i=1
----- (5.8)
The most probable value of X turns out to be the mean of the observations . This
minimisation is called the L2 norm. A major problem with this method is the effect
of one poor observation on the mean. The technique works best if the errors are
normally distributed. A first step to improve the robustness of the method is to
work with the median instead of the mean . This is achieved by minimising the
summed absolute values instead of the summed squared differences and is
known as the L1 norm:
d
N
-Llx-xil=O
dx i-1
N
d
L - I x - xil= 0
i= 1 dx
----- (5.9)
N
L sign(x- Xi) = 0
i=1
sign is +1 when the argument is positive, -1 when the argument is negative and
somewhere in between when the argument is zero (Claerbout, 1976). For N odd
X is equal to tile middle order statistic X(lN/2J+1) where
LJ denotes the integer part
while for N even the median is chosen as (XLN/2J+X(lN/2J+1))/2 (Chave et aI., 1987).
Before the median can be computed the data have to be ordered in ascending
order.
5.3. ROBUST M-ESTIMATION
From the discussion it is clear that the L1 norm is not as sensitive as the L2 norm,
but it is still prone to the effect of outliers. Weighted medians go some way to
improving the robustness of the impedance estimation procedure, minimising
N
Llw illx - Xii
----- (5.10)
i=1
with
Wi
the weight factor, but is still too vulnerable to bad data points.
Furthermore, the Least Squares estimate is only really adequate when the errors
in the input data have a Gaussian distribution (Egbert and Booker, 1986). It is
45 Chapter 5: Statistical data reduction
possible to test whether the errors are Gaussian distributed by making use of
Quantile-Quantile plots (Q-Q plots)
5.3.1. Q-Q Plots
A way of examining the distribution of the error data is by drawing Q-Q
plots. The Q-Q plot is achieved by plotting observed residuals against the
values expected if the residuals were normally distributed (Johnson and
Wichern, 1998). The observed residuals must be ordered (written in
ascending order). The inverse of the Gaussian distribution function (~-1)
gives the expected residuals.
Plot the ith value of the N observed residuals rj against ~-1(ilN). If the
points fall on a straight line with unit slope, the residuals have a normal
(Gaussian) distribution.
To determine the residual expected from a Gaussian error, calculate (i­
~)/N
and consult a table that depicts the areas under a normal curve
(Table A3. Walpole and Myers, 1989) for a value that corresponds to (i­
~)/N.
However, impedance tensors still have to be estimated regardless of the error
distribution. According to Johnson and Wichern (1998), it is possible to transform
non-normal data so that it has a more Gaussian distribution. This is in effect the
approach suggested by Sutarno and Vozoff (1989,1991) where they minimise a
function of the residuals and not the residuals themselves.
A very important fact to realise is that in all the above we have made use of the
principle of maximum likelihood, namely the assumption that the set of
measurements we obtained was the most probable set of measurements (Young,
1962).
With the classical least squares approach, impedance tensor estimates can be
determined by minimising the error component in equation (5.5) (Sutarno and
Vozoff, 1989, 1991).
46
Chapter 5: Statistical data reduction
N
I(X i
i= l
Find the value of
~
2
-
I Uij~j) 2 ~ min
----- (5.11 )
j= !
that minimises (5.11), that is, solve for the derivative equal to
o.
N
2
I(xi - Iqpj)qj
i=1
----- (5.12)
=0
j=1
In matrix form equation (5.12) becomes
(x-U~)u = 0
UTX-UTU~ = 0
UTUP = UTx
p= (U Ur U x
T
1
T
----- (5.13)
Huber (1981) solves the problem of robustness by minimising a sum of less
rapidly increasing functions of the residuals instead of minimising a sum of
squares:
----- (5. 14)
p(t) is known as a loss function and should be chosen so that the influence
function \V(t) = dp(t) is continuous and bounded.
dt
----- (5.15)
To ensure that the solution to (5 .14) is scale invariant it is necessary to introduce
a scaling parameter. Equations (5.14) and (5.15) are substituted by
----- (5.16)
47 Chapter 5: Statistical data reduction
Uik = 0,
k = 1,2
----- (5.17)
Chave et al. (1987) propose two possible choices for the scaling parameter,
namely
----- (5.18) ----- (5.19) SMAD
is the median value of the absolute residuals (median absolute deviation
(MAD))
SMAD
and
CJMAD
=median(lrj - median(r)l)
----- (5.20)
is the expected value of the MAD for the appropriate probability density
function . In equation (5.19) the subscript IQ marks the interquartile distance.
SIO
is the spacing between the 75% and 25% points of the sample distribution, or the
centre range containing half of the probability (Chave et aI., 1987)
----- (5.21)
CJIO
is the corresponding theoretical value and is equal to twice the MAD for
symmetric distributions.
Egbert and Booker (1986) follow a slightly different approach in determining the
scale factor. They compute an initial estimate from the root mean square residual
----- (5.22) Using the actual rms of the residuals
nn
for the nth iteration makes the estimate
extremely vulnerable to the effect of outliers. By replacing sample averages by
expectations, the scale estimate for the nth iteration becomes
1
2N
___ "
~(2N - 4)
with
~=O. 7784.
48
{Emeas _ E pred)2
'8 ~
In
n
----- (5.23)
Chapter 5: Statistical data reduction
For the loss function Huber (1981) uses a convex function that has a positive
minimum at O. It is based on a density function that has a Gaussian centre and
Laplacian tails and is defined as follows:
p(t) =
~t
1
2
ItI < to
----- (5.24)
1
toltl- 2 t~
It1 ;:::: to
to is the tuning constant and value of 1.5 gives at least 95% efficiency for outlier­
free normal data (Chave et aI., 1987).
3.0
-r---,----------r---,
p(t) 1.5
0.0 -+----,-----=::::..,..::::=----.----1
0.0
3.0
1.5
-3.0
-1.5
t
Figure 5.2. The Huber loss function (Sutarno and Vozoff,1989)
The influence function is
- to < t < to
t ;:::: to
t
~
----- (5.25)
-to
In order to achieve the best possible solution for the impedance tensor, the
robust linear regression problem can be converted into a weighted least squares
problem. Division of the influence function by the scaled residuals produces the
weight function. Through substituting the influence function in equation (5.15) with
the
49 Chapter 5: Statistical data reduction
2.0 - , . - - - - - - - - - - - - ,
\!f{t) 0.0
-2.0-+----.---.,---,-----1
-3.0
-1.5
0.0
1.5
3.0
t
Figure 5.3. The Huber influence function (Sutarno and Vozoff,
1989)
weighted function and writing it in matrix form, the problem reduces to solving the
following equation iteratively:
UTWr = 0
UTW(x - U~) = 0
UTWU~ = UTWx
~ = (U TWUr 1[jTV\tx
----- (5.26)
For the loss function in (5.24), the Huber weight function reduces to
W(t)
=
{t:
ItI
ItI < t
ItI ;::: to
----- (5.27)
Equation (5.26) is solved iteratively, choosing the least squares estimate as an
initial solution. From this the predicted outputs and residuals are calculated using
equations (5.1 and 2) and (5.28) respectively.
i = 1, ... ,N
50 ----- (5.28)
Chapter 5: Statistical data reduction
2.0 - - . - - - - - - - - - - - - - - - - ,
W(t) 1.0
0.0
-f-----r----r----r--------I
-3.0
-1.5
0.0
1.5
3.0
t
Figure 5.4. The Huber weight function (Sutarno and Vozoff, 1989)
Next, the scale parameter and Huber weights are determined and used to solve
(5.26). The new impedance tensor estimate is then used to calculate the
predicted outputs and the process is repeated until the estimates converge.
Sutarno and Vozoff (1991) states that "the Huber weights fall off slowly for large
residuals and provide inadequate protection against severe residuals". They
suggest the use of Thomson weights for a few iterations after convergence with
the Huber weights. Thomson weights are described by the function
------ (5.29) a determines the scale at which down weighting begins. Egbert and Booker
(1986) use a value of 2.8 for a. Chave et al. (1987) describes the Nth quantile of
the appropriate probability distribution as an excellent choice for a. Furthermore,
if outliers have been eliminated, the residuals are
x2 distributed.
This distribution
with two degrees of freedom is equivalent to the exponential distribution and thus
has the pdf
1 .::..t.
f(t) = - e 2
2
with
(JMAD
t~ 0
----- (5.30)
=2sinh-1(0.5) }} 0.9624 and OIQ =2 log 3 » 2.1972 (Chave et aI., 1987).
The quantiles of the exponential distribution are given by
51 Chapter 5: Statistical data reduction
j
= 1, ... ,N.
----- (5.31)
According to Sutarno and Vozoff (1991), if outliers have been eliminated the
magnitudes of the residuals are Rayleigh-distributed with pdf
f(t) = te
and
(JMAD
2
t~ 0
----- (5.32)
=0.44845. The quantiles are given by
j
= 1, ... ,N.
----- (5.33)
With magnetotelluric data, equations (5.1) and (5.2) consists of complex
numbers. Sutarno and Vozoff (1989) suggest two ways of handling complex data:
• regard the data as having independent Gaussian real and imaginary
parts and apply separate weights to them,
• use the magnitude of the complex numbers and apply identical weights
to the real and imaginary parts.
The second method is preferable since it is rotationally invariant (Sutarno and
Vozoff, 1991). Equation (5.26) then becomes
~=
(O'wOr 1 0'V\rx
----- (5.34)
where' denotes the Hermitian conjugate.
5.4. ADAPTIVE Lp NORM
An alternative approach has been developed to deal with the problem of non­
Gaussian distributed errors. Kijko (1994) proposes that it is not always necessary
to use the L1 or L2 norm to minimise residuals, but that one can use the Lp norm
where p can be a real value not necessarily equal to 1 or 2. He goes further to
develop an adaptive procedure whereby the value of p is automatically
determined from the quality of the data . The teohnique
W03
developed for use
with seismological data but can easily be applied to magnetotelluric data.
Instead of minimising equation (5.6), the following misfit function is minimised
52 Chapter 5: Statistical data reduction
n
Ilx-xr ------ (5.35)
i=1
where 1 :::; P <
(f).
The value of p depends on the distribution of the residuals and
is therefore related to the kurtosis of the residual distribution. Press et al. (1992)
define the kurtosis as a measurement of the peakedness or flatness of a
distribution relative to the normal distribution. A distribution with a sharp peak is
known as 'Ieptokurtic' and the term 'platykurtic' describes a flat distribution. The
kurtosis (P2) is given by
114
112
P2 = -2
------ (5.36)
where 112 and 114 are the second- and fourth order central moments.
Several authors suggested different ways to determine the value of p using the
kurtosis. Money et al. (1982) used the equation
A
9
P = ~~ +
1
where 1:::;
p<
(f).
------ (5.37)
Sposito et al. (1983) developed a different equation
A
P=
6
-;;-
P~
------ (5.38)
with 1:::; p:: ; 2.
Therefore, when data are severely contaminated, the error distribution will have
long tails, resulting in a large kurtosis and subsequently a small value for p.
Kijko (1994) developed an adaptive algorithm for determining an estimate for the
p-value.
5.5. APPLICATION
OF
STATISTICAL
REDUCTION
TECHNIQUES TO SYNTHETIC DATA
The statistical reduction methods discussed in the previous sections will first be
applied to synthetic data. Hattingh (1989) describes the construction of a unit
apparent resistivity curve. He generates electric field data by multiplying real
53 Chapter 5: Statistical data reduction
magnetic field data with unit impedance (equation (3.52)). The resultant apparent
resistivity versus frequency curve will plot as a straight line on double logarithmic
paper.
5.5.1.
Synthetic data with Gaussian distributed random errors
For the first test, noise with a Gaussian distribution was added to the data. Figure
5.4 shows the apparent resistivity and phase versus frequency curves. Noise
added to the two curves both had a zero mean, but different standard deviations.
The Q-Q plots for these data sets (Figure 5.6) plot on roughly straight lines,
confirming that the noise is normally distributed. The correlation coefficients
between the observed and expected residuals are 0.89516 and 0.896132 for
noise with standard deviations of 0.0001 and 0.2 respectively.
Figures 5.7 to 5.12 show the curves fitted to the data using the L1 norm, L2 norm,
adaptive Lp norm and robust M-estimation techniques. In the calculation of the L1,
L2 and adaptive Lp norms, an algorithm for the downhill simplex method taken
from Press et al. (1992) was used to minimise the impedance variables. Neider
and Mead (1965) introduced this method of minimising multidimensional
functions.
The L1 and L2 norm methods yield very similar results (Figures 5.7 and 5.8).
Curves estimated by these two techniques approximate the original data
displayed in Figure 5.5 very well.
54 Chapter 5: Statistical data reduction
Synthetic data
Synthetic data
Gaussian distributed noise added
Gaussian distributed noise added
(Stddev= 0.2)
(Std dev
>­
>­
:'=
:'=
:;;
:;;
>
>
.!!1
en E
Q)
-..
~
I:
Q)
0.1
e.
• e-.
E
.s::. 0.01
0
"'
0.001
<
0 .0001
c.
c.
.!!1
en
,.-,
~
••
10
100
0.1
~
Q)
E
.s::.
0
I:
...
Q)
0.01
-­.••
"'c.c.
<
"'
10
1000
100
1000
90
..
Q)
en
.s::.
c..
Q)
"'
Cl
"C
••
Frequency (Hz)
en 60
Q)
Q)
•• ...
0.0001
90
en
.s::.
c..
...
0.001
Frequency (Hz)
Q)
= 0.0001)
30
...
0
10
-... ... -... • •
- ­•
100
en 60 .
.
Q)
Q)
Cl
Q)
"C
30
0
1000
10
Frequency (Hz)
100
1000
Frequency (Hz)
Figure 5.5. Unit impedance magnetotelluric curves with Gaussian distributed
noise added.
Q-Q Plot for 2 .93 Hz
Q - Q Plot for 2.93 Hz
Gaussian distributed noise added
Gaussian di s tributed noise added
(real component. Std Oev=O ,2)
(real component. Std Oev=O .0001)
-;
!.;! [ ,.u),·· I · ·;·· " [
o
-3
-2
-1
0
2
"
~
40
~
20
'0
<I>
0
<I>
-20
>
....
II)
.c
l .. . .
60
II)
0
3
Expected residual
Figure 5.S.
-;
,
It,IIl• •
.. ' ' r'''''
."
.(I~n,u.'
I",
-40
-3
-2
I,
-1
0
2
3
Expected residual
Q-Q plots of the synthetic data with Gaussian distribution noise
added
55
Chapter 5: Statistical data reduction
Synthetic data
Synthetic da ta
Gaussian distributed noise added
Gaussian distributed noise added
(Std dev = 0.0001)
(Std dev
-
->.
>.
.:;
:~
.!!! ~
~ E;
a::
E
-..r::
c: 0
~ ~
t1I
:;:;
0.1
.!!! ~
~ E;
-'-----e
0.01
••
-- ••
~.§ 0.01
.-
0.001
Q.
Q.
0.0001
c:x:
10
0.1
100
~
••
0.001
t1I
..r::
Q..
VI
(I)
(I)
...Cl
(I)
"C
.­
0.0001
c:x:
10
1000
100
••
1000
Frequency (Hz)
90
60
(I)
VI
t1I
30
-• •
Q.
Q.
90
VI
--.• •
c:
0
CI)~
Frequency (Hz)
CI)
= 0.2)
..r::
Q..
;--
--
0
10
VI
(I)
(I)
...Cl
(I)
"C
60
30
0
100
1000
10
100
1000
Frequency (Hz)
Frequency (Hz)
Figure 5.7. Apparent resistivity versus frequency curves produced by the L1 norm
estimation technique for the synthetic data (with Gaussian distributed noise)
displayed in Figure 5.5.
56 Chapter 5: Statistical data reduction
Synthetic data
Synthetic data
Gaussian distributed noise added
Gaussian distributed noise added
(Std dev = 0.0001)
(Std dev = 0.2)
>.
:!:
>
:;:;
.~
0.1 .
E
Q>
0::
E
.... ..c: 0.01 r:: 0
~ns
0.001
••
0.1 -
~
0:: E 0.01
.... ..c:
r:: 0
Q>­
I/)
••
Q>
•• ~ ...
0.0001
1
~
.!!!
0.
0.
«
1
>
:;:;
-
I/)
>.
:!:
1
10
100
:v
••
0 .001 -
«
1
1000
I/)
ns
..c:
c...
...Cl
C1l
"C
10
100
••
1000
Frequency (Hz)
90
~
~
I/)
•• ...
0.0001
90
Q>
Q>
• • ••
0.
0.
Frequency (Hz)
Q>
........
I/)
60
Q>
I/)
ns
..c:
c...
30
...
0
1
10
Q>
Q>
...
Cl
C1l
"C
60
30
0
100
1
1000
Frequency (Hz)
10
100
-
1000
Frequency (Hz)
Figure 5.B. Apparent resistivity versus frequency curves produced by the least
squares (L 2 ) estimation technique for the synthetic data (with Gaussian
distributed noise) displayed in Figure 5.5.
The adaptive Lp norm technique yielded similar results to the L1 and L2 norms.
The curves in Figure 5.9 were calculated using the formula of Money et al. (1982)
to calculate exponent p. Figures 5.10.1 to 5.10.10 show the values of p
calculated for each frequency during the estimation of the apparent resistivity and
phase versus frequency curves.
57 Chapter 5: Statistical data reduction
Synthetic data
Synthetic data
Gaussian distributed noise added
Gaussian distributed noise added
(Std dev = 0.0001)
~
.:;:
:;:;
.!!! _
0.1
~ ~
c:::
E 0.01
-.I:.
~
(\I
0
t:
a.
a.
>­
.
>
:;:;
.!!! _
• ~...
0.001
0.1
~ ~
• • ..
10
100
c::: E 0.01
-
~£
••
~
0.001
<{
0.0001
a.
a.
1000
(\I
.I:.
a..
...Cl
Q)
"0
~
10
..
• .....
••
100
1000
Frequency (Hz)
90
If)
Q)
Q)
••
.I:.
Frequency (Hz)
Q)
If)
= 0.2)
;':::
0.0001
<{
(Std dev
90
60
Q)
If)
(\I
.I:.
a..
30
...
0
10
If)
Q)
Q)
60
Q)
30
...Cl
"0
-
0
100
1000
~
10
Frequency (Hz)
100
-'"c
1000
Frequency (Hz)
Figure 5.9. Apparent resistivity versus "frequency curves produced by the
adaptive Lp norm technique for the synthetic data (with Gaussian distributed
noise) displayed in Figure 5.5. The formula suggested by Money et al. (1982)
was used to calculate the exponent p.
58 Chapter 5: Statistical data reduction
Frequency = 2.93 Hz
Synthetic data with Gaussian distributed noise added (Std dey = 0.0001)
1.4 ~~.-----------------------------------------------------------,
Co
1.2
J.\!.,.-...•".,....~.,.,~".'.'F.'.' . . •~.,......~.'F.'.' .' F.'.".""'.'.'·..,.,.···.·,.,.,.···.,•.•···•··.'•...•
• •
~
~
0
~
w
m
N
N
~
N
00
N
~
~
~
~
~
~
0
~
~
~
w
m
~
~
Nr of iterations
Frequency = 2.93 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.2)
Co
5,--------------------------------------------------------,
3 ·· ·· /~\· · •. ..~ .. ... .. .... ... -. ----­ .. .-........ -.. -.. .... -.... .. .. .. ....... .. .. .. -­ .... .. ........ ------­ .. -­
/ \ / .....­.....-.-.-.-.-.-.-••• . • - •.• -.-.-.- •.• -.-.-.-.-.- ••••• -+.+-+-+-+-+-+-+.+-+-+-+-+-+-+
.~.~.
,.
~
V
I
~
0
w
m
~
~
~
\
~
•
~
M
~
~
~
~
Nr of iterations
Figure 5.10.1. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.9 at 2 .93 Hz
Frequency = 5.28 Hz
Synthetic data with Gaussian distributed noise added (Std dey = 0.0001)
3 ,------------------------------------------------------------,
Frequency = 5.28 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.2)
~ ~.~ gVy'Y.:;';:;";.~;.; ..• ~::;;:;·;;·; ...;;:;';.·.·.•:.;;-;·;.. ;..•;C;:;:.;
~
~
0
~
w
m
~
~
~
~
~
~
~
~
~
~
Nr of iterations
Figure 5.10.2. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.9 at 5.28 Hz
59 Frequency = 9.52 Hz
Synthetic data w ith Gaussian distributed noise added (Std dey =0.0001)
2.5,------------------------------------------------------.
c.
1.~ - ;,l\j\;;~~~~;~'~;~~~:;;~:~~:;-~--~:~~~:-:~~~:~~~:~:~:-:~.:~ :~:.:~.:~~~:~~:~:~-~:;~~:-:~~~:~:~-.~~;;-:~:~
1
.
,
~
,
0
~
,
M
~
m
W
~
N
N
~
N
,
00
N
~
M
,
~
M
~
M
0
~
M
W
~
~
m
~
Nr of iterations
Frequency = 9.52 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.2)
5,-------------------------------------------------------.
~
0
~
M
~
m
W
~
N
N
~
N
00
N
~
M
~
M
~
M
0
~
M
~
W
~
m
~
Nr of ite rations
Figure 5.10.3. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.9 at 9.52 Hz
Frequency
=17.17 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.0001)
2.5 -.-----------------------------------------------------~
•
c.
.
1.~ _. : :~-: ____ _::::: : :jy.~:/~>~.~~~~:-:~~~:-~:.:._-~-~~~~~::.:.:.-.~~:~~~:.~.~~~~:.:.~.:.:._- ~~~~:-:~~.~.
' ...... -.- ..... -.
1 ~·To~~~~~~~~~~~~~~~~~~~~~~~~~~~
/
~
~
~
0
~
M
~
W
~
m
~
N
N
~
N
00
N
~
~
~
~
~
~
0
~
~
~
w
~
m
~
Nr of ite rations
Frequency = 17.17 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.2)
3.-------------------------------------------------------.
c. 2
- j\/\/\!\-!\/\/\-)\/\/\i\/\-/\/\/\-/\!\/\/\i\/\/\/\/\­
........... .............
.....
Nr of ite rations
Figure 5.10.4. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.9 at 17.17 Hz
60 Chapter 5: Statistical data reduction
Frequency = 30.95 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.0001)
2,--------------------------------------------------------,
~
a
~
~
ill
m
N
N
~
N
~
~
00
N
~
~
~
~
a
~
~
~
ill
~
m
~
Nr of iterations
Frequency = 30.95 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.2)
3 ..-------------------------------------------------------------~
~
a
~
~
ill
m
N
N
~
N
00
N
~
~
~
~
~
~
a
~
~
~
ill
~
m
~
Nr of iterations
Figure 5.10.5. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.9 at 30.95 Hz
=
Frequency 55.81 Hz Synthetic data with Gaussian distributed noise added (Std dey =0.0001) 1.6,---------------------------------------------------------,
Co
..
•. . .
~ :; .J\<~<.~\/~~~~~~:-:.:.:.:~:.:.~~~~ -.~~:-:~~~:~:.:.: :.~.:.: ~~~~:-:~ ~.~~--:.:.:.:~-:.~.:~~~~.:.:. :~:~::~~~
•
~
a
~
~
ill
m
N
N
~
N
00
N
~
~
~
~
~
~
a
~
~
~
ill
~
m
~
Nr of ite rations
Frequency = 55.81 Hz Synthetic data with Gaussian distributed noise added (Std dey =0.2) 4 .---------------------------------------------------------------~
•
Co
~ /~<-~:~:.~;~~~~~;.o.-;~~~~~~.~~o:.-:. :~~~;.:~-:;~~~:;-~:.-.~;~:~~~;~ ..:.:;~~~:; ~~.~-- ;~~:~:.-.o~:~:.:;~~:~·.-;
_
.rT,-rr~rr,,_r~~~~_r,,_.,,_rrT._~._""_.,,_r~~,,_r~._~
~
~
a
~
ill
m
N
N
~
N
00
N
~
~
~
~
~
~
a
~
~
~
ill
~
m
~
Nr of iterations
Figure 5.10.6. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.9 at 55.81 Hz
61 Chapter 5: Statistical data reduction
Frequency = 100.62 Hz Synthetic data with Gaussian distributed noise added (Std dey =0.0001) 1 . 6.-----~.-----------------------------------------------,
Co
~:; :. j\..l</V~Y~~~~;~~-,:~:~;~:. ~:,-~:~:~~~;~~~-,.:~:~~~~~~~~~:~.~-'~:~:~:.:~:~~.:-:~~~~~:- .:.~;--~ :~~.:~~~
1 •
~
~
0
N
~
m
N
N
~
N
00
N
~
N
~
N
~
N
0
~
N
~
~
~
m
.
~
Nr of iterations
Frequency = 100.62 Hz Synthetic data with Gaussian distributed noise added (Std dey =0 .2) 1.3,---------------------------------------------------------,
c. 1.2
--.,;;."-.J.;..:. .".-.-,;-.:..--.-... ...-.....-.-.-.-.-. . . --------_.--------_.
.,,~::...-..........'-.-.-..... ~.-.-.- • .•-.--• .;;.--.-• .; .....
1.1
1
+O~rT~~rT"~""~~~~""-r~""~,,~,,,-"",-rT,,~
~
~
0
N
~
m
N
N
~
N
00
N
~
N
~
N
~
N
0
~
N
~
~
~
m
~
Nr of iterations
Figure 5.10.7. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.9 at 100.62 Hz
Frequency = 181.43 Hz Synthetic data with Gaussian distributed noise added (Std dey =0.0001) 3,----------------------------------------------------------.
c.
2 .. ... _.._... -- -- .. _.. .. --- --- .. .... ------ -- -- -•. --- +-- - ... . . .. . -- -- -- -- -- .. -- 1
•
-.-.-.-.-+ .. -- _. - - -- - -- -- • -- --. -- - -- _.. -- -- -­
/ \ / ' .-.-.-.-.-•.•-.-.- .......-.-.-.-.-.- •.•-.-.-•.•
+-+-+ - +-+-+-+-+-+ - +-+-+-+-+-+-+- +-+- • . . - ~ -- - - ...... - -- -- -- . --- --- -- --- - - -- - . . - - - - -- - ----- - - .-- -- -- - -- - - - ......... . . - - --- ­
O ~~~~~~~~~~,_~~~~~~~~~~~~~~~~~~
~
~
0
~
N
m
N
N
~
N
00
N
~
N
~
N
~
N
0
~
N
~
~
~
m
~
Nr of iterations
Frequency = 181.43 Hz Synthetic data with Gaussian distributed noise added (Std dey =0.2) .
- ;·,1·"V-V·V·V"'."·-.-·-.'.'.'.'.'.'.'.'.'.·.-.·.'.'.'.'.'.'......•......-...
".
1.4 .-----------------------------------------------------------~
Co
1.2 - _.. --- --. ----
1 .T~.~~·T?~-~·",,'-,,'-,,~-r~.-,,~-r"'-rT~~~-.~-r,,~~~~
~
~
0
N
~
m
N
N
~
N
00
N
~
N
~
N
~
N
0
~
N
~
~
~
m
~
Nr of iterations
Figure 5.10.8. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.9 at 181.43 Hz
62 Chapter 5: Statistical data reduction
Frequency = 327.11 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.0001)
~ :~ LjV'~'~';;':;_;~',;" _'_~, ;, ;,;, , _, m
v
~
0
~
m
m
N
N
~
N
ro
N
n uum .-­•.••-. uu
~
~
v
~
~
~
0
v
~
v
n- I
m
v
m
v
Nr of iterations
Frequency = 327.11 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.2)
1.7.-----------------------------------------------------~
v
~
0
~
m
m
~
N
N
N
ro
N
~
~
v
~
~
~
0
v
~
v
m
v
m
v
Nr of iterations
Figure 5.10.9. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.9 at 327.11 Hz
=
Frequency 589.79 Hz Synthetic data with Gaussian distributed noise added (Std dey =0.0001) Q.
1.6,------------------------------------------------------,
.-..............-.-.-. -. - • .• - +- .... - +- +-. -... +- ... +- +... - ...... +-.-+. +-. -....- .............- .-.-............... +- .... - ... . 1.4 -_ .. _. / _.. .... ..... . .... . .... ........ ... .... ... .. ...... ... .... ...... .. .. ..... ..... .. .. . . .. ..... ... ..... .... . 1.2
--....~- - - ----
/
o
m
~
N
N
~
N
ro
N
o
v
Nr of iterations
Frequency = 589.79 Hz Synthetic data with Gaussian distributed noise added (Std dey =0.2) 2 ,-----------------------------------------------------~
•
Q.
..'.,·:·.-..:·~·.'.:·,.:. ::~~.:.·.·.·:.,·-.·'.~·.-...,.-....~:.:..-.,.-...~ :~:....:.~.~.,(~~.~. . .:.~~;'.e·
1.5 . /V~~~
•
~
0
m
N
N
~
N
ro
N
o
V
Nr of iterations
Figure 5.10.10. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.9 at 589.79 Hz
Using the formula suggested by Sposito et al. (1983) in equation (5.38) to
calculate the exponent p, yield the apparent resistivity versus frequency curves
shown in Figure 5.11. The results are again of high quality as would be expected
for noise with a normal distribution. Figures 5.12.1 to 5.12.10 show the values of
p calculated during the adaptive Lp norm process.
63
Chapter 5: Statistical data reduction
Synthetic data
Synthetic data
Gaussian distributed noise added
Gaussian distributed noise added
(Stddev= 0.0001)
(Std dev = 0.2)
~
.:;
:;:;
1 r-----~----~----~
>­
:::
E·.
_
.~
0.1
~
~ ~
­
.~ -
~
•
0.001 ­
~
8
~ e;
..
( /)
nI
.c
a.
CIl
CIl
...Cl
CIl
~
----1- 8----,.---+-----1
• •
.
~- 0.001 -1--- - 1 - - - - 1 -. . ­
~
<C
0.0001 +-----+-----+-------1
10
100
1000
Frequency (Hz)
90
CIl
• ••
.1-
c: 0
Frequency (Hz)
(/)
--..-+-------+-----1
IV
0.0001 +-----+------+-------1
10
100
1000
<C
0.1 -1­
~ ~ 0.01
0.01 -1­ - -.+-'. -..=----+-------1
c: 0
~
IV
1 r-----.,....-------,-----.....,
>
:;:;
90
( /)
60
CIl
(/)
nI
.c
a.
30
.
0
1
10
CIl
CIl
60
...
Cl
CIl
~
30 .
0
100
1
1000
Frequency (Hz)
10
100
1000
Frequency (Hz)
Figure 5.11. Apparent resistivity versus frequency curves produced by the
adaptive Lp norm technique for the synthetic data (with Gaussian distributed
noise) displayed in Figure 5.5. The formula suggested by Sposito et al. (1983)
was used to calculate the exponent p.
64 Chapter 5: Statistical data reduction
Frequency =2.93 Hz
Synthetic data with Gaussian distributed noise added (Std dey
=0.0001)
~ :; j.•.•.• ..n............ n......... .,.,.",n ..n .•nn,.....: nn..... n ,nn] ~
~
0
~
w
m
N
~
00
N
N
N
~
~
~
~
~
~
0
~
~
~
w
~
m
~
Nr of iterations
Frequency = 2.93 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.2)
iii
iii
ii
i
,
i
o
""
N
I
~
N
N
Nr of iterations
Figure 5.12.1. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.11 at 2.93 Hz
Frequency =5.28 Hz
Synthetic data with Gaussian distributed noise added (Std dey = 0.0001)
~
~
~
0
w
m
N
N
~
00
N
N
~
~
~
~
~
~
0
~
~
~
w
~
m
~
Nr of iterations
Frequency = 5.28 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.2)
2
_-_
c­ 1 51 --~
n. nnnnn, ,, I
- - .. ---­
1
i
~
~
0
i
~
w
m
N
N
~
N
00
N
~
~
~
~
~
~
0
~
~
~
w
~
m
~
Nr of iterations
Figure 5.12.2. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.11 at 5.28 Hz
65 Chapter 5: Statistical data reduction
Frequency = 9.52 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.0001)
~ 1~::1 'L~:,..,...,..-,.,
,"I
-r-r
," - .. .,....,. - • • '.,...,..
' .. ' .,.-,-"
".,.-," .. .,....,.. - • 'r-r'
- - - ".,.-,-"
.. -.,.-,""" - ".,...,.....
" " .,.-,-"
.. -.,.-,' - • -.,.-,--.,-," .. " - • • ,....,." .•• '.,.-,' •• -,......,...
- . .. '.,-,-'
• • •,....,.-­
• • • ', ...-," • •,.....,....
•••• '.,-," -T""T"""f
,"
- ,---,-­
I
~
~
0
~
M
~
~
~
i i ,
m
~
N
N
i
i
~
N
i
'
iii
ro
N
~
~
~
~
~
~
0
v
M
~
~
~
m
v
Nr of iterations
Freque ncy = 9.52 Hz Synthetic data with Gaussian distributed noise added (Std dey =0.2) Nr of iterations
Figure 5.12.3. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.11 at 9.52 Hz
Frequency = 17.17 Hz Synthetic data with Gaussian distributed noise added (Std dey =0.0001) Nr of ite rations
Frequency = 17.17 Hz Synthetic data with Gaussian distributed noise added (Std dey =0.2) j
~ , : N mm
1.4
m ,u,u" umm, : , " u"u,u"
mum,u,'. " " u,um m ""u,u, uuuu
I
Nr of iterations
Figure 5.12.4. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.11 at 17.17 Hz
66 Chapter 5: Statistical data reduction
Frequency = 30.95 Hz Synthetic data with Gaussian distributed noise added (Std dev =0.0001) 2r------------------------------------------------------.
Q.
1.5
~
~
0
~
~
m
N
N
~
N
ro
N
~
~
~
~
~
0
~
~
~
~
~
~
m
~
Nr of iterations
Frequency = 30.95 Hz Synthetic data with Gaussian distributed noise added (Std dev =0.2) 25T--------------------------------------------------------~
~ ': j.~:~?Y>. U .••·•.• .•.·.·· · U-_m: -:
mUUm:·_···_-_· uj
Nr of ite rations
Figure 5.12.5. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.11 at 30.95 Hz
Frequency = 55.81 Hz
Synthetic data with Gaussian dis tributed noise added (Std dev =0.0001)
Nr of iterations
=
Frequency 55.81 Hz Synthetic data with Gaussian distributed noise added (Std dev =0.2) ~
~
0
~
~
~
m
N
N
~
N
ro
N
~
~
~
~
~
~
0
~
~
~
~
~
m
~
Nr of iterations
Figure 5.12.6. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.11 at 55.81 Hz
67 Chapter 5: Statistical data reduction
Frequency =100.62 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.0001)
~
~
w
~
n
0
~
~
m
~
N
N
~
N
ro
N
~
~
~
~
~
~
0
~
n
~
w
~
m
~
Nr of iterations
Frequency = 100.62 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.2)
~ :; j:.:.", "":' :':,,:::'.::".':""
n
. . n,,' , ' ,",
'0. ,:.:,'::""'.,.0,. ",:, :,,'I
Nr of iterations
Figure 5.12.7. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.11 at 100.62 Hz
Frequency = 181.43 Hz
Synthetic data with Gaussian distr ibuted noise added (Std dey =0.0001)
~
~
0
~
n
~
w
m
~
N
N
~
N
ro
N
~
~
~
~
~
~
0
~
n
~
w
~
m
~
Nr of iterations
Frequency = 181.43 Hz
Synthetic data with Gaussian distributed noise added (Std dey =0.2)
~
25.------------------------------------------------------.
': t
,."
~
,
· " ..
~
0
~
n
"
""
w
~
m
~
N
N
,· " n
~
N
,·., n n " ' ,
ro
N
~
~
~
~
~
~
0
~
n . . •,• .
n
~
w
~
,~
m
~
Nr of iterations
Figure 5.12.8. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.11 at 181.43 Hz
68 Chapter 5: Statistical data reduction
Frequency = 327.11 Hz Synthetic data with Gaussian distributed noise added (Std dev =0.0001) ~ :; 1-..•,', ......... .,., m,, ·.•.,
v
~
0
~
~
~
~
~
u•• ,•.." u .mu", .·. m,u.,. mm ·." H,·., 1 m
~
N
N
~
N
~
N
~
M
v
~
~
M
0
v
~
v
~
v
m
v
Nr of iterations
Frequency =327.11 Hz Synthetic data with Gaussian distributed noise added (Std dev =0.2) o
~
.,.....
~
m
.,......,.....
N
N
~
N
~
N
Nr of iterations
Figure 5.12.9. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.11 at 327.11 Hz
Frequency = 589.79 Hz Synthetic data with Gaussian distributed noise added (Std dev =0.0001) 25 ·,---------------------------------------------------.
~ 1; j_m
u
.••••..•.. uu." "' " un"
m
~
N
N
~
N
", ,u:1
~
N
Nr of iterations
Frequency = 589.79 Hz Synthetic data with Gaussian distributed noise added (Std dev =0.2) 2.5,----------------------------------------------------,
2 · .. • ........ ...... ... ....... .. .. ..... .. ... ... .... ... .
a. 1.5
.. . .. ......... .... .. ....... ....
........... .. . !.\!.\;.y!,,-."'. ,•.•.•.•~•.•.•.•.•-•.•.•..•.•.•..•,.~•.-.'.'.'."+>.'.'.'.-.'.'.'.".'.'.'.".'.'.'.'.
1 •
•
v
~
0
.,.....
~
.,.....
~
.,.....
m
.,.....
N
N
~
N
~
N
~
~
v
~
~
M
0
v
~
V
~
v
m
v
Nr of iterations
Figure 5.12.10. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.11 at 589.79 Hz
The amount of iterations is markedly less when Sposito's formula is used in the
estimation of p. A main reason for this is that equation (5.38) does not allow p to
be greater than two . The adaptive procedure is terminated when p becomes too
large.
69
Chapter 5: Statistical data reduction
The minimisation uSing the Robust M estimation technique also yielded very
good results (Figure 5.13), similar to the L1, L2 and adaptive Lp norm methods.
Synthetic data
S ynthe tic data
Gaussian distributed noise added
Gaussian distributed noise added
(Std dev = 0.0001)
(Std dev = 0.2)
>­
::::
?:
.:;
>
:;:;
:;:;
~
.!!!
0.1
~ ~
E
c:::
-..c:
t: 0
~~
0 .01
"'
0 .001
<
0.0001
0.
0.
••
~
.!!!
I
--••
E 0.01
- ..c:
~e.
.--.---­
:;
100
II)
ra
..c:
a.
Q)
Q)
...Cl
Q)
"0
•
•
I
-­
••
0.
0.
<
0.0001
1000
10
100
1000
Frequency (Hz)
90 -
90
Q)
•
0 .001
Frequency (Hz)
II)
••
0::
•
10
0.1
~ ~
II)
60
Q)
II)
ra
..c:
a.
30
...
0
10
Q)
Q)
...Cl
Q)
~
60 30
0
100
10
1000
Frequency (Hz)
100
1000
Frequency (Hz)
Figure 5.13. Apparent resistivity versus frequency curves produced by the
Robust M estimation technique for the synthetic data (with Gaussian distributed
noise) displayed in Figure 5.5.
All of the tested statistical reduction methods yielded good results for data
containing normally distributed errors, as would be expected . An increase in
standard deviation of the noise did not affect the quality of the estimated curves.
5.5.2.
Synthetic data with non-Gaussian distributed random errors
Figure 5.14 shows two curves, both with Gaussian distributed random noise
added. In this case the mean is 0.0 and the standard deviation is 1.0 for both
70 Chapter 5: Statistical data reduction
curves. Random noise without a specific distribution was added to the data in the
first curve. This introduced outliers to the data.
Figure 5.14. Apparent resistivity versus frequency curves using the unit
impedance with Gaussian and randomly distributed noise added.
The Q-Q plots for these two data sets calculated at a frequency where outliers
are clearly visible (Figure 5.15) confirm that the noise in the first curve does not
have a perfect normal distribution. The correlation coefficients between the
observed and expected residuals are 0.771726 for the curve with the randomly
distributed errors, and 0.984253 for the data containing only normally distributed
errors.
Synthetic data
Gaussian distributed noise
(Stddev= 1. 0)+ random noise added
Z'
.:;;
10
•
:0:;
:0:;
0.1
c 0 0.01
~[
0.001 -
E;
~.§ 0.01
• • i I •• I•
I : .._
..i"
0.0001
100
10
J:
a.
...
-
+-- - + - - - - + - - ----1
Q)
(/)
(/)
Q)
..c
OJ
1000
.
60
0...
Q)
~
30
o
100
100
ro ~
30 -1- - --+---::----+---- 1
10
'TeI
90
Q)
Cl
~
I
Frequency (Hz)
•
~ 60
.••-
U. UUU1
90~----~----~----~
Q)
• 4
1000
Frequency (Hz)
II)
(\)
r--I
~o
~ ­ 0.001
c.
• I
10
01
.
.!:Q ~
-J:
..i"
Z'
.:;;
••
.!:Q­
~ E;
~ E
Synthetic data
Gaussian distributed noise added
(Std dev = 1.0)
,
• - I
10
1000
• •• • I
• ••
100
Frequency (Hz)
Frequency (Hz)
71 1000
Chapter 5: Statistical data reduction
Q-Q Plot for 30.9 Hz
Gaussian distributed noise (Std Oev=1.0)+ random noise added (real) cv
:::l
:s!
en
...
Q)
-c
Q)
>
...
Q)
en
.c
0
Q-Q Plot for 30.9 Hz
Gaussian distributed noise added
(Std. Oev=1 .0) (real component)
cv
4
:::l
l.r'
2
0
-2
en
...
Q)
-c
Q)
...>
'1
•
-4
-20
:s!
-10
0
Q)
en
.c
0
10
Expected residual
4
2
.,. ...
0
.II ..."
,
••
."
-2
•
-4
-4
-2
o
2
4
6
Expected residual
Figure 5_15. Q-Q plots for the curves displayed in Figure 5.14.
The curves fitted to the data using the L1 and L2 norms yield very different
results (Figures 5.16 and 5.17).
The L1 norm produces a good fit for the
apparent resistivity curve and a mediocre fit for the phase curve. In contrast with
this the L2 norm results in a very bad fit for both apparent resistivities and
phases compared to the L1 norm.
Phases calculated for the data containing
only Gaussian distributed noise also show larger misfits at higher frequencies.
72 Chapter 5: Statistical data reduction
Synthetic data
Gaussian distributed noise
(Stddev= 1. 0)+ random noise added
Synthetic data
Gaussian distributed noise added
(Std dev = 1.0)
.?:'
.?:'
:~
:~
~
~
.~ -
01
'=.§
~o
ro
«
••
• 4
0.01
~
0..
0..
•
.
~ ~
.~
0.001
E•
'=.§
•
Co
0.0001
100
0.01
•••
~ ~ 0.001
. - e_.__
•
10
0.1
CD
0..
0..
«
••
•
...
4
0.0001
1000
100
10
Frequency (Hz)
••
1000
Frequency (Hz)
90
90
~
Q)
Cfl
ro
Cfl
Q)
60
-
~
..c
OJ
0...
:s
Q)
30
o
A
I
A
Q)
Cfl
ro
. ..
...
10
...
...
100
Cfl
Q)
60
~
..c
OJ
0...
:s
Q)
30
o
1000
...
•
10
Frequency (Hz)
...
...
100
..
1000
Frequency (Hz)
Figure 5.16. Apparent resistivity versus frequency curves produced by the L1
norm estimation technique for the synthetic data displayed in Figure 5.14.
73 Chapter 5: Statistical data reduction
Synthetic data
Gaussian distributed noise
(Stddev= 1. 0)+ random noise added
~
:......
~
.~,-...
~
01 ~
::.§
Co
0.01
~ '-'0.001
0.
0.
«
••
.
Synthetic data
Gaussian distributed noise added
(Std dev = 1.0)
>­
..
~
...... .~ ,-...
CD
~
0.
0.
«
1000
10
(f)
CD
..c
CJ)
...
••
100
1000
90
60
CD
(f)
ro ~
D... ~
••
Frequency (Hz)
90
CD
•
0.0001
Frequency (Hz)
(f)
••
ro '-' 0.001
0.0001
100
•
"
~o
•
10
.
a::
E
...... ..c 0.01
•
­
0.1
E
(f)
...
(f)
CD
60
ro ~
30
'-'
o
•••
• •
10
•
100
..c
D...
...
CJ)
2,
30
o
1000
. . ..-­
...
...
10
Frequency (Hz)
100
1000
Frequency (Hz)
Figure 5.17. Apparent resistivity versus frequency curves produced by the L2
norm estimation technique for the synthetic data displayed in Figure 5.14.
Results obtained with the adaptive Lp norm procedure and using Money et al.'s
method of calculating p (equation 5.37) are depicted in Figure 5.18. The
apparent resistivity curve is very similar to the curve estimated with the L1 norm.
The phase data again yield better results than the L2 norm but worse results
than the L1 norm. Figures 5.19.1 to 5.19.10 show the value for p calculated
during each iteration.
74 Chapter 5: Statistical data reduction
Synthetic data
Gaussian distributed noise
(Stddev= 1. 0)+ random noise added
>.
:~
1
Synthetic data
Gaussian distributed noise added
(Std dey 1.0)
=
~
,..------r----,------,
...... .(f)
~ -E
Q)
1 ,---- ,,----.----,
:~
...... 01
.
.~ ~E
-j- - = -- - i - - - - j - - -I Q)
0:::: E
0.01
-I-----+~.,...--j---I
ro0.. ~ 0.001
+---+---+-'''~---I
0.. <{
-+----+---j--~
...... £
~o
O.0001
0.1 -1-,•.---+-----+----1 ::..§
0.01
--1 -...,---+-___
cO·•
•
.
•
-1___
~ ~ 0.001
0..
0..
<{
4
1--'. .
1
-j------if-----.-j-......,---=---I
••
0.0001 -+----+---+----1
10
100
1000
Frequency (Hz)
90 - , - - -, ---.-----,
90.---.---,---,
~
~ ~
<1l
£
0..
~
60
-1-------+-------1------1
~
0>
Q)
2.
~ ~
<1l ~
60
-I- - - - + - - - - - j - - - -I
0..
30
+ -- - - + - - - - - j - - - -I
£
. .....
30 +- - - - + - - -- -,- - ---1
10
100
Q)
o
O ·f---____~._.---....!L..-'f----~
1
0>
2.
1000
...... .. ..
... -'"'­
10
Frequency (Hz)
100
1000
Frequency (Hz)
Figure 5.18. Apparent resistivity versus frequency curves produced by the Lp
norm estimation technique for the synthetic data displayed in Figure 5.14. Money
et al.'s (1982) equation was used to calculate p.
75 Chapter 5: Statistical data reduction
Frequency =2.93 Hz Synthetic data with Gaussian distributed noise (Std dev ;; 1.0) + random noise • :~""'--\i-.'. ::: : -'
: "-: : -: " ::'-': -:" -....-::::.:::::::.- - .:::-::.:-::::-..:::::- ::..::..- :.:-:::.-........ -.-,:,:­
..::- ::..---,1
.....
N
M
N
lI'I
'"
l"N
4'
....
NM
M
Ihr-4'....-M
f"'IMMM
VV
Nr of Iterations
Frequency = 2.93 Hz Synthetic data with Gaussian distributed noise added (Std dev = 1.0) ~ : j~""'~""'H"H" '~" ~"""~'" "~'~" ~" .' .'n'~""j 1
4
'
'
"
''''
"
""'"
,,'
,
"
"
""'
"
Nr of iterations
Figure 5.19.1. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.18 at 2.93 Hz
Frequency = 5.28 Hz Synthetic data with Gaussian dis tributed noise (Std dev = 1.0) + random noise 104,--------------------------~
c.
1
o~ -k:.,.,.•.!.~~,.~~.~ . --,--~ . , .,--,--..-- -- --., ':.:'.,. ,., ~ --.--. .
.,.',', .:
m
••
______
,
.
,
•
•,
•
• , __ , __ ,
,· ~ · ·I
Nr of iterations
=
Frequency 5.28 Hz Synthetic data with Gaussian distributed noise added (Std dev = 1.0) ~:~
1 ,
,"
,
Nr of ite rations
Figure 5.19.2. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.18 at 5.28 Hz
76
· Chapter 5: Statistical data reduction
Frequency = 9.52 Hz Synthetic data with Gaussian distributed noise (Std dey = 1.0) + random noise ~ ~': k~" "" '~~"'''' ~H'n""'~~"~" 'H.•,..n •.•.H.~" •.",.1
1 "
,
"
' "' "
""
' "
'"
"
"
"
"
,,"
"
Nr of ite rations
=
Frequency 9.52 Hz
Synthetic data with Gaussian distributed noise added (Std dey =1.0)
Nr of iterations
Figure 5.19.3. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.18 at 9.52 Hz
=
Frequency 17.17 Hz Synthetic data with Gaussian distributed noise (Std dey
=1.0) + random noise ~ ~ 'i E,',',',',',',"',' ',','," ,',',',',',' ",',',',',','," ',',',',',',",',',',., v
~
0
~
ill
m
N
N
~
N
ro
N
~
~
v
~
~
~
0
v
~
v
ill
v
m
v
Nr of ite rations
Frequency = 17.17 Hz
Synthetic data with Gaussian distributed noise added (Std dey = 1.0)
Nr of iterations
Figure 5.19.4. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.18 at 17.17 Hz
77
Chapter 5: Statistical data reduction
Frequency = 30.95 Hz
Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise
Nr of iterations
Frequency = 30.95 Hz
Synthetic data with Gaussian dis tributed noise added (Std dev = 1.0)
~:~
1
, "
"
Nr of ite rations
Figure 5.19.5. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.18 at 30.95 Hz
Frequency =55.81 Hz
Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise
~ ~:: E n,n,n,n,n, n ,nn,n,n,nn,nnm,: n,': , :'" ,mn,n,n:,n,n,mm,n,n,n l
1
Nr of iterations
Frequency =55.81 Hz
Synthetic data with Gaussian distributed noise added (Std dev = 1.0)
~
,;
r~,
-'-~-:_--.-[.-["-[~'-_.-[.-,_.-[ .-.[ [.-:.-:-!-.-.-:-.--'-'_ , , ~ ~ ,• -,'_-,.-.-,.-:-_,.-','-,'-,.-:-:-::­__.-_-.:-,-.-.-j-'
-_ - --_
_
••
Nr of iterations
Figure 5.19.6. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.18 at 55.81 Hz
78
Frequency = 100.62 Hz
Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise
Nr of iterations
Frequency = 100.62 Hz Synthetic data with Gaussian distributed noise added (Std dev = 1.0) 0.
~ -'-J~- --------- -.__.-.-.•-_.-u.--.-._ . -.-_._ __._-.-_.--.-.-.-_._ __ __ __
. -.-u.-.-.--._-.____ ___
. -.-.-u.-.-.--._-.-._____ _ _ _.-_.-.-u._ __-.-._ ___
. -.-.---',1
1J~, ,1 1
~
~
III
0
11
m
~
M
,
l
i
1
,
111
~
N
N
~
N
N
il,
~
111
~
~
111
~
~
11
11
1
~
0
v
v
11
I,
ill
m
v
v
Nr of iterations
Figure 5.19.7. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.18 at 100.62 Hz
=
Frequency 181.43 Hz Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise 0.
1.051r. . . . . . . . . . . . . . . . . . . . . . . 1L
'
ii
'
ii
'
ii
l
ii
l
iiliil
,
I
i
ii
i
iii!
i
,
I
;
i
,
,
i
Nr of iterations
Frequency = 181 .43 Hz Synthetic data with Gaussian distributed noise added (Std dev =1 .0) 4-,-------------------------------------------,
0.
21••
~ ••'"
o,
I,
1--,
V
-r ,
~
0
~
w
m
N
N
~
N
~
N
~
V
~
~
~
0
v
~
v
w
v
m
v
Nr of iterations
Figure 5.19.8. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.18 at 181.43 Hz
79
Chapter 5: Statistical data reduction
=
Frequency 327.11 Hz Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise 0.
~ rw-:---.....•....-~-~-.•--•
.•.-.•-.•.~-..-•.•.-..•
•--.-...-.•---.
.•.• •- •.-.-.•-.•.-~-.•.~-.•-.~-...-.-.-.-.
.•-..-.•-.-.•.•
--..-•.-.-~-.•-.•.~-.•-.•­ .~- "I
ol" , "
" ,"
"' " '"
""
" , II"
"
"Ii '
,
Nr of iterations
Frequency = 327.11 Hz Synthetic data with Gaussian distributed noise added (Std dev = 1.0) ~ : LLr:-.-.-..-.•----.•..•.•...-.-.•-.--•.•
.•..--...
..-..•.-.-.---•-.-.-.•.•.. .•".-..-.-,,-.•-.•-.-.---.•.•.....-.-.-.•
..--.•-.-•.---..•.....•.-.-.
..-•-'j
1
~
v
'"
~
, , . ,-,- . , "
0
~
ill
m
, "
N
N
~
N
"
ro
N
'"
~
~
v
~
'"
~
~
, , , , , "
~
0
v
v
ill
v
"'~
m
v
Nr of iterations
Figure 5.19.9. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.18 at 327.11 Hz
=
Frequency 589.79 Hz
Synthetic data with Gaussian distributed nois e (Std dev = 1.0) + random nois e
··..·.. ,· ·: · ~ · · ·· · ·: · : · I
Nr of iterations
=
Frequency 589.79 Hz
Synthetic data with Gaussian distributed noise added (Std dev = 1.0)
Nr of ite rations
Figure 5.19.10. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.18 at 589 .79 Hz
Figure 5.20 shows the results of using the adaptive Lp norm estimation
technique and Sposito et al.'s (1983) formula (Equation 5.38) to calculate p.
80
Chapter 5; Statistical data reduction
Where non-Gaussian distributed noise was added, the results are very poor and
correlate very well with the curves obtained from the least squares method. The
reason for this becomes clear when one looks at the values of p calculated for
each iteration in Figures 5.21.1 to 5.21.10. At most frequencies the adaptation
procedure was terminated after only a few iterations because the values
calculated for p were greater than 2, and this is not allowed when using
equation (5.38).
Synthetic data
Gaussian distributed noise
(Stddev= 1. 0)+ random noise added
Synthetic data
Gaussian distributed noise added
(Std dev= 1.0)
>.
>.
:5
~
- ­E
.~
.~
E
(J)
0.1
'=.§
~O
ro
a.
a.
•• • •
.
~
«
0.01
0.001 ­
0.0001
.~
rf)
•
••
.
0::
E
ro
~
-..c
~o
.~
a.
a.
«
I
10
(J)
100
0.1
--.
0.01
0.001
1000
10
rf)
(J)
(L
OJ
(J)
2­
60
(J)
rf)
---rf)
(J)
100
1000
60
ro ~
..c
30
o
••
90
ro ~
..c
...
Frequency (Hz)
90
(J)
••
0.0001
Frequency (Hz)
rf)
• J•
• • •••• •
10
100
(L
•
OJ
(J)
2­
. . ..
30
o
...
...
10
1000
...
100
1000
Frequency (Hz)
Frequency (Hz)
Figure 5.20. Apparent resistivity versus frequency curves produced by the Lp
norm estimation technique for the synthetic data displayed in Figure 5.14.
Sposito's (1983) equation was used to calculate p.
81 Chapter 5: Statistical data reduction
Freque ncy = 2.93 Hz
Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise
Nr of IteratIons
=
Frequency 2.93 Hz Synthetic data with Gaussian distributed noise added (Std dev = 1.0) ~ ; 1',' m"
,n,n,n nm nm,n,n,n" ,m , n mn" ' ,' " n nn m,n , m, n, m
Nr of iterations
Figure 5.21.1. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.20 at 2.93 Hz
=
Frequency 5.28 Hz Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise ~ ~Emmm mm
1 ,
i
ii
i
i
iii
i
,
ii
i
i
tt'
t
ii,
i
i
Nr of iterations
Frequency = 5.28 Hz Synthetic data with Gaussian distributed noise added (Std dev = 1.0) · .... 1
I
i
ii
,
I
j
,
i
ii
i
,
i
,
i
i
Nr of iterations
Figure 5.21.2. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.20 at 5.28 Hz
82
Chapter 5: Statistical data reduction
=
Frequency 9.52 Hz
Synthetic data with Gaussian distributed noise (Std dev
~
:E,
" ,u
mm m m m m u mmm m,u,
"
,um
=1.0) + random noise
muu m m ,u,u
u
Nr of iterations
=
Frequency 9.52 Hz
Synthetic data with Gaussian distributed no ise added (Std dev =1 .0)
2r.-~--------------------------------------------------,
Cl.
1.5
1
'~,:.o, :u.. __ ...'u'u. ',",.0,'.0 .0" L
m
~
~
0
~
"'.0 .. .0' .0.0 ' . .0'.0.0 .. .0.0..
i 'iii"
~
~
N
N
N
,"
ro
N
"
.0 ..... .0 .. .0 ',",.01
']"
~
~
~
~
~
~
ii"
0
~
~
~
~
~
"
m
~
Nr of iterations
Figure 5.21.3. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.20 at 9.52 Hz
=
Frequency 17.17 Hz Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise 3.-----------------------------------------------------~
Cl.~~E_~···~·· ~:
- -i· ~----~····~, ~~,,~i
i
·~ · ~- :·~
~ ··i· ·~
:·~··~
i · ~i·~
· ~~····~~·····
· ···~··· i ·:·.o ·~~
i · i· Nr of iterations
Frequency = 17.17 Hz
SynthetiC data with Gauss ian distributed noise added (Std dev = 1.0)
Cl.
~1 :·r---l
. .0,..,-.. ..........
- ......-..........-......... -............................ ,--·,·-·· ······ ··-·······,-- ,··
----,
,--,--,-- 1
[,
i'
i i i
i
,
I
i i i
i
ii
Nr of iterations
Figure 5.21.4. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.20 at 17.17 Hz
83 Chapter 5: Statistical data reduction
Frequency = 30.95 Hz
Synthetic data with Gaussian distributed noise (Std dev
=1.0) + random noise
~ :Fm
mu
1
, , ,
. ... . .. . '. . 1 iii
i
,
I
,
i
i
i
ii'
i
ii
i
i
i
i
•
J
I
Nr of iterations
=
Frequency 30.95 Hz Synthetic data with Gaussian distributed noise added (Std dev = 1.0) ~ l-r---F'", " ~ ~", ,, -..........-.............,
- ..:....-.......,..~ ..., ~ .~ ..,-..:.... ..-.. --·i··i ··i· · i· :--i ·: ····i· ------,:
-i · '·
· ~ - I
Nr of iterations
Figure 5.21.5. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.20 at 30 .95 Hz
Frequency = 55.81 Hz
Synthetic data w ith Gaussian distributed noise (Std dev = 1.0) + random noise
l
~ Em,u,u, ,,
i
,
i
I
ii
i
i
i
i
,
i
ii
I
Nr of iterations
Frequency = 55.81 Hz Synthetic data with Gaussian distributed noise added (Std dev = 1.0) ~ ~1l
',-.-.,.-.,..-"--,,-,,.-..-..-..-...-..-,--.,.-..-.,.-..,,-",-,,- -..-...-..-..-..-..-...-..-..-..-.-..-...-..-..-..-..-..-..-..-..-..-..-..-..-..-..-.- -.-.·---,··1
[
ii
i
Ii
i i i
i
ii
i
i
,
i
Ii
Nr of iterations
Figure 5.21.6. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.20 at 55 .81 Hz
84
Chapter 5: Statistical data reduction
Frequency = 100.62 Hz
Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise
~ :F~
", " -,..,.,. . .-.... ~. ," ;,",,'....... .....~ . ,...,. ~ . -,..
. , . ..-........;-, -" ·....·
- . :··-,· ----.
- ,. ,·1
Nr of iterations
=
Frequency 100.62 Hz Synthetic data with Gaussian distributed noise added (Std dev = 1.0) ~ ~:: t um,m, ,"u umm:
Nr of iterations
Figure 5.21.7. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.20 at 100.62 Hz
Frequency =181.43 Hz
Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise
~ ~ l~·~, -',_.~'..-'
~. ''',.-,'.-~' -'.,. .'-'-''r-r'-.~ ~. :~ .~.-.--;. -. ,~ ~ ~. ~~~~~.- ~. ~-
~
•.-..,....
.-..,....
. -....,...
. . -....,........,.
. -•...,.
. ..,-.""T
. ,-."""T
. '- """T
'
. • .•-.- .-.r. -.r.. ..-.,_
. -.,_
. -.,._
•.
..
. ,....
. -•...,...
. -•...,..
•.
':"1
.....,.
. ,.""T
- .• ..-,"""T
••-,- ,;-. ,-•...,
.
Nr of iterations
Frequency = 181.43 Hz Synthetic data with Gaussian distributed noise added (Std dev =1 .0) ~ : '-1~' --~. '-,,,-,..- - -..-..-....:..,...- - - .:-.,- - -.:
,- ,",- "..:,-"- " ,,-. - -",,,,-,.. -:. ',"- ,' ","-,- ,,,,,, -.-.. ,-,~-,"..:
-----'-I
Nr of iterations
Figure 5.21.8. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.20 at 181.43 Hz
85
Chapter 5: Statistical data reduction
Frequency =327.11 Hz
Synthetic data with Gaussian distributed noise (Std dev
=1.0) + random noise
4,------------------------------------------------------,
0.
~
·iu···~·
·iu:uu.___,__r___r,_u
u--iui--~···:··
u~
i u ....
u
-+--r--,-,-'r
r-r-r--o--;
i
i--i -·-r-rT""T
i --i--·--·~i
i ~~I
T'""T""T""T""T
i i
i
i
Nr of iterations
Frequency =327 .11 Hz
Synthetic data with Gaussian distributed noise added (Std dev = 1.0)
Nr of iterations
Figure 5.21.9. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.20 at 327.11 Hz
Frequency = 589.79 Hz
Synthetic data with Gaussian distributed noise (Std dev = 1.0) + random noise
~ ~ Em­
1 .
I
i
v
~
ii
i
~
0
w
m
,
,
N
N
iii
~
~
N
~
~
N
v
~
~
~
~
0
v
v
w
m
v
v
Nr of ite rations
Frequency =589.79 Hz
Synthetic data with Gaussian distributed noise added (Std dev = 1.0)
0.
~1 .~l-.-.-..--.......-u..- ..-....-..
- - -....
- -..-..-------..-....
- -u......- - - . -. - ...
- -. -..-...- ...-...- ------.-....-..-...~
.
~
iii
i
v
~
i
)
0
iii
j
~
w
ii
m
i,
N
N
"
~
N
iii
~
N
i
~
~
v
~
i
~
~
i
0
v
~
v
i
w
v
i
m
V
Nr of iterations
Figure 5.21.10. Values calculated for the exponent p during the estimation of the
apparent resistivity values displayed in Figure 5.20 at 589.79 Hz
86 Chapter 5: Statistical data reduction
The Robust M estimation method yields very good results for the apparent
resistivity curve at most frequencies (Figure 5.22), even though it starts with the
least squares estimate of the impedance tensor as an initial estimate.
Synthetic data
Synthetic data
Gaussian distributed noise
(Stddev= 1. 0)+ random noise added
Gaussian distributed noise added
(Std dev = 1.0)
c
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:.....
~
:~
.....
.~
Q)
E.
0.1
~.§ 0.01
Q)
• w.
~O
ro . . . . . 0.001 0.. 0.. <{
­
. (/)
~ E
••4
E 0.01
0:::
••
......!::
••
0.0001
10
0.1
.
100
~o
ro . . . . . 0.001 •
0.. 0.. <(
Q)
(/)
10
1000
­
30
o,
100
1000
~ ~ 60
(/)
(])
0.. (])
~
••
90
--- 60
Ol
...
Frequency (Hz)
ro ~
.!::
••
,
0.0001
Frequency (Hz)
90
••
.
10
ro ~
.!::
... ...
,
• • ...
100
Ol
0.. (])
~
30
o-
1000
.
...
10
Frequency (Hz)
...
...
100
..
I
1000
Frequency (Hz)
Figure 5.22. Apparent resistivity versus frequency curves produced by the
Robust M estimation technique for the synthetic data displayed in Figure 5.14.
Where random noise without any specific distribution was introduced to the
impedance tensor, the L1 norm, Robust M estimation and Lp norm (using
equation (5.37) to calculate p) techniques yielded satisfactory results for the
apparent resistivity curves. The least squares technique and Lp norm with
equation (5.38) yielded bad results. All of the estimation methods used resulted
in bad fits for the phase curves.
87 Chapter 5: Statistical data reduction
5.5.3.
Conclusions drawn from synthetic data tests
In the case where only Gaussian distributed noise are introduced to the
impedance tensor, all the tested statistical reduction techniques yielded very
good results. An increase in the standard deviation of the distribution of the
noise causes a slight deterioration in the quality of the curve fitted to the phase
data.
Completely random noise added to the impedance tensor caused a marked
decrease in the success of some of the minimisation techniques. The least
squares method did not produce good results at all. The same is true for the
adaptive Lp technique where equation (5.38) was used to determine the value of
p. Estimated values of p greater than 2 caused the adaptive process to be
terminated and therefore at most frequencies L2 minimisation occurred.
The L1 norm, robust M estimation method and Lp norm using equation (5.37) all
yielded good results , with the best fit produced by the L1 norm. From the above
examples it is concluded that the adaptive Lp-norm method is more susceptible
to the starting impedance values than the robust M-estimation technique.
From the examples studied in this chapter it is clear that none of the
minimisation techniques yielded perfect results. It is therefore critical that the
curves obtained should be studied very carefully, keeping in mind the amount of
artificial noise present near the sounding station. Additionally calculated
parameters that can provide more information on the presence of noise, such
as the Tipper, must be taken into account.
88 Chapter 6: Case study
CHAPTER 6 CASE STUDY A magnetotelluric (MT) survey was conducted along the road between Sishen
and Keimoes in the Northern Cape Province of South Africa. It followed the
route of a deep seismic reflection survey that was carried out during 1989 on
behalf of the Geological Survey and the National Geophysics Programme by
Geoseis (Pty) Ltd. of South Africa. The aim of the MT survey was twofold:
• Compare the results obtained by the two methods to determine
whether it would be beneficial to do a magnetotelluric survey prior to a
deep reflection seismic survey in order to locate areas of interest. This
would be of economic interest since a deep reflection seismic survey
costs considerably more than a magnetotelluric survey.
• Shed light on a number of interesting features that is visible on the
reflection data.
The statistical techniques discussed in the previous chapter were applied to the
data .
6.1. SURVEY LOCATION
Eleven sounding stations were positioned along the Sishen - Keimoes road at
roughly 20km intervals. Figure 6.1 shows the location of the survey area in
South Africa.
89 
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