Design of a GPR System for Dielectric Constant Measurement

1

Chapter1 Introduction

The electromagnetic geophysical exploration methods are of increasing importance in geology,

environmental science and civil engineering. One of the important electromagnetic methods is the

subsurface sensing by a ground penetrating radar (GPR). The working function of a GPR is based

on a non-destructive method. Over the last years, GPR has been extensively used as a

non-destructive method for locating different subsurface anomalies.

In general, to investigate the subsurface structure, GPR methods use electromagnetic waves by

transmitting radar pulses into the ground and receiving the return pulses from the below interfaces.

Radar return pulses are gathered and imaged and the images are analyzed based on the electrical

properties of the underlying features. During the measurement process, the GPR equipment can

easily move through the ground surface and acquire the data quickly. Therefore, it is very

convenient for a rapid survey in a selected test area.

GPR systems can be successfully used for solving different problems in various fields of science

and technology, for example, they might be used for mapping of different infrastructures and

mineral locations, mapping of quarries, determination of underground water levels, finding

archaeology, determining shallow subsurface geological structures and other civil and

environmental studies.

1.1 Background and Objective Generally, compaction quality control system is very important for the civil engineering. For

controlling the compaction quality, the soil dielectric constant can be used. Determining the

dielectric properties of materials is very important for different non-destructive evaluation

techniques, because these properties are usually affected by the volumetric properties of the

materials [23]. Different techniques had been developed during the last decade to measure the

dielectric properties of the laboratory-prepared samples. However, the estimation of the

dielectric-properties in a field has not been sufficiently studied. In this study, different methods for

the estimation of dielectric properties are investigated.

Time Domain Reflectometry (TDR) is a relatively new method for measurement of soil moisture

content. The first application of the TDR to the measurement of soil water content was introduced

by Topp et al.[37] . The main advantage of the TDR is that it is very easy to use and convenient for

a field measurements. The disadvantage of a TDR method over other methods of soil moisture

2

measurement is that it is impossible to do measurement of deep object. It can measure the

parameters of very shallow region, i.e, less than 50 cm. In addition, TDR is difficult when the soil is

very hard or there are many stones.

However, the above mentioned problems can be solved by using a GPR. The GPR technique is

conceptually quite simple. The essential features of the GPR are a source antenna placed on the

ground surface, radiating energy both upward into the air and downward into the soil, and an

antenna receiving the signal transmitted by the source. Any subsurface contrast in electrical

properties will result in some energy being reflected back to the surface [2]. Most of the phenomena

of EM wave related to GPR are determined by the dielectric constant of the medium and GPR

offers a fast way for estimating the soil dielectric constant. Here, wave propagation velocity

depends on the dielectric constant of the medium and it was found that wave velocity varies from

30cm/ns in air to 6-15 cm/ns in soils. The electromagnetic waves penetrate into the soil, and reflect

off interfaces with different dielectric constants and speed of the waves can be determined using the

travel time passing through different layers.

Normal GPR survey cannot measure the dielectric constant directly. To measure the dielectric

constant accurately a Common Source (CS) and Common Midpoint (CMP) methods of GPR can be

used for primary data acquisition. Usually, the acquired raw field data contains much noise and

attenuating reflections. Therefore, signal processing is needed before the actual data interpretation.

Different signal processing methods can be applied to the acquired GPR data and reliable results

can be obtained if correct interpretation of radar data is performed after the conducted signal

processing.

The main aim of this research is to develop a simple GPR equipment and establish a methodology

to estimate the dielectric constant of the materials. For this purpose, a simple GPR equipment was

developed. For the determination of the soil dielectric constant the interval velocity estimation

method has been used.

1.2 Thesis Outline Chapter 2 reviews the theoretical fundamentals of a GPR and some data processing techniques.

Chapter3 describes the GPR system which I have developed for velocity estimation.

Chapter4 describes the laboratory experiment which we conducted to validate the developed GPR

system for velocity estimation.

Chapter 5 presents conclusions and further recommendations.

Chapter 2

Estimation of the dielectric constant of subsurface material by GPR

2.1 Introduction

In general, data obtained by a GPR measurement is similar to the data obtained by a seismic survey.

Therefore, they can share many techniques for data processing and analysis. Usually, it is difficult

to interpret the raw GPR data and there is a need to apply signal processing. In this chapter, the

theoretical fundamentals of GPR and some data processing techniques are reviewed. Velocity

estimation from GPR data is our main interest.

2.2 Relationship between the time domain and the frequency domain

The relationship between the frequency domain and the time domain is described mathematically

by the Fourier transform. Fourier transform of a rectangular pulse signal shown in Fig.2.1

( )

0

A t Tf t

t T

⎧ <⎪= ⎨≥⎪⎩

can be described as

{( ) expT

F AT

ω = −∫−

It is plotted in Fig.2.2. In

pulse in terms of the sinc

terms of the sinc function,

sin( ) 2 TF A ωωω

=

t

A

-T T

f(t)

0

Figure 2.1 Rectangular pulse.

} sin2 Tj t dt A ωωω

= (2.1)

applications, we usually write the Fourier transform of the rectangular

function. If we write the Fourier transform of the rectangular pulse in

then

(2.2) 2 sin TAT c ωπ

⎛ ⎞= ⎜ ⎟⎝ ⎠

3

The rate of oscillation of ( )F ω in frequency domain is inversely proportional to the width of

rectangular pulse T. The narrower the width of f(t) the higher the oscillation ( )F ω .

Figure 2.2 Fourier transform of rectangular pulse.

The scaling of a signal in the time domain leads to inverse scaling in the frequency domain, i.e.,

(2.3) ( )tf F αωα⎛ ⎞ =⎜ ⎟⎝ ⎠

where α is a real constant [7,11].

Fig.2.3 illustrates ( / )f t α and the corresponding Fourier transform. The signal’s time and

frequency representations cannot have short duration simultaneously, because when the time

duration gets larger, the frequency bandwidth must be smaller.

Figure 2.3

( / )f t α ( )F αω

The top plots correspond to the small scaling factor α . The bottom plots

correspond to the large scaling factor α (taken from [7]).

4

2.3 Filtering

If the noise and signal are separated in the frequency domain, then filtering can be applied to the

traces to remove such noise. This problem may be reduced by applying a standard windowing

function, such as the Hanning (raised cosine) window [8]. Any number of functions can be used as

windows for the data, but the most commonly used one is the Hanning window. Hanning window is

a digital manipulation of the sampled signal in an FFT analyzer which forces the beginning and

ending samples of the time record to zero amplitude.

Windowing is a frequency filter that we apply to the frequency domain data when we convert it to

the time domain data. This filtering rolls off the abrupt transition at –T and T. This effectively

produces a time domain response with lower sidelobes. Windowing allows a limited degree of

control over the pulse shape, trading off ringing (time sidelobes) for pulse width (Fig.2.2 and

Fig.2.4). Various windows with different properties are known for the purpose of spectral estimation.

In the following, a brief overview of Hanning window is given. The Hanning window is defined as

follows:

(2.4) 20.5 - 0.5 cos( ), 0,1,..., -1

( ) -10, .

n n Nw n N

otherwise

π⎧ =⎪= ⎨⎪⎩

where N is a number of points.

Figure 2.4 Hanning window function

Fig.2.4 shows the Hanning window. This window is used to smooth the transition of the spectrum

as shown in Fig.2.5. The abrupt change of the spectrum at the edge of the frequency bandwidth

normally causes the truncation effect. Fig.2.5(a) shows the measured frequency domain data before

Hanning windowing and after Hanning windowing. Fig.2.5(b) shows the transformed data from the

frequency domain (Fig.2.5(a)) to the time domain data. If we apply IFFT to the raw data, then time 5

domain data has some ringing. Therefore, we use this windowing to suppress this ringing. That

means, if we apply windowing before IFFT, then the time domain data has less ringing.

(a) Spectrum

(b) Time domain

Figure 2.5 Measured raw data and filtered data.

6

2.4 Velocity analysis

A commonly used velocity analysis technique in GPR is based on computing the velocity spectrum.

The basic idea of the velocity spectrum is to display some measures of signal coherency on a figure

of velocity versus two-way zero-offset time.

Fig.2.6 shows a simple case of two horizontal layers. At a given midpoint location M, the travel

time along the raypath from the transmitter position T to the depth point D, then back to the receiver

point R is t(x). The travel time as a function of the offset (x) is defined as:

7

2

2 2 2( ) (0) /t x t x v= + (2.5)

where x is the distance (offset) between the transmitter (source) and the receiver positions, is the

velocity of the medium above the reflecting interface, and t(0) is the two-way travel time along the

vertical path MD.

v

Note that vertical projection of depth point D to the surface, along the normal to the reflector,

coincides with midpoint M. This is the case only when the reflector is horizontal [9].

Fig.2.7(a) is an example of traces in a common midpoint (CMP) gather. All of the traces in this

CMP gather contain a reflection from the same depth point. The time difference between the two

way travel times at a given offset t(x) and at zero offset t(0) is called normal moveout (NMO). The

velocity required to correct for the normal moveout is called the normal moveout velocity. The

NMO correction is given by the difference between t(x) and t(0), which is defined as:

12( ) (0) (0) 1 1

(0)xt t x t tNMO vt

⎧ ⎫⎪ ⎪⎡ ⎤⎛ ⎞⎪ ⎪∆ = − = + −⎨ ⎬⎢ ⎥⎜ ⎟

⎝ ⎠⎣ ⎦⎪ ⎪⎪ ⎪⎩ ⎭

(2.6)

From Eq.(2.5), we see that velocity can be calculated when offset (x) and two-way travel times t(x)

and t(0) are known. Once NMO velocity is determined, the travel times can be corrected to remove

the influence of offset so that all the traces arrive at the same time and all traces align horizontally

as shown in Fig.2.7(b). Traces in the NMO-corrected gather then are summed to obtain a stack trace

at the particular CMP location.

Figure 2.6 The NMO geometry for a single horizontal reflector (refer to Equation (2.5))

Figure 2.7 The NMO correction (Equation (2.6)) involves mapping nonzero-offset travel time

t(x) onto zero-offset travel time t(0). (a) Before and (b) after NMO correction (taken from [9]).

8

From Eq.(2.5), we can develop a practical way to determine the stacking velocity from a CMP

gather. Eq.(2.5) describes a curve on the t2(x) versus x2 plane. The shape of the curve is 1/vst2 and the

intercept value x=0 is t(0). The t2-x2 velocity analysis is a reliable way to estimate stacking

velocities.

The method of constant velocity scans of a CMP gather is an alternative technique for velocity

analysis. The most important reason to obtain a reliable velocity function is to get the best quality

stack of signal. Stacked amplitude is defined as:

(2.7)

9

where is the amplitude value on the i-th trace at two-way time t(i). Here, M is the number

of traces in the CMP gather. The resultant stack traces for each velocity side by side on a plane of

velocity versus two-way zero-off time is called the velocity spectrum. An example of the velocity

spectrum is shown in Fig.2.8.

, ( )fi t i

2 2 2( ) /( , ) , ( )1 1

M MS f f t i xt v i t i i ii i

⎛ ⎞= = +∑ ∑ ⎜ ⎟⎝ ⎠= =

v

Figure 2.8 Velocity Spectrum obtained from the CMP gather

In signal processing, several different velocity estimation techniques are known. We estimated the

velocity spectrum by using the unnormalized crosscorrelation sum. Firstly, we should define the

stacked amplitude from the data sets. The stacked amplitude is defined as:

(2.8)

10

where , ( )i t if is the amplitude value on the i-th trace at two-way time t(i). Here, M is the number of

traces. Two-way time t(i) lies along the trial stacking hyperbola:

(2.9)

where stv is assumed velocity of the medium and t(0) is the vertical travel time from the antenna to

the reflector. Then, we calculate the unnormalized crosscorrelation sum within a time gate that

follows the path corresponding to the trial stacking hyperbola across the data sets. The expression

for the unnormalized crosscorrelation sum is given by:

0] derived the travel time equation for th

(2.11)

here and C2, C3, … are complicated functions that depend on layer

sses and interval veloci

(2.12)

here is the vertical two-way time through the ith layer. The series in Eq.(2.11) can be

d a

(2.13)

t i, ( )1

M

t ii

s f=

= ∑

22

2( ) (0) i

st

xt i t v= +

(2.10) 2

2 2, ( ) , ( ) , ( )

1 1 1

1 1( (0), )2 2

M M

st i t i i t i t i t it i i t i

CC t v f f s f= = =

⎧ ⎫⎡ ⎤ ⎧⎪ ⎪= − = − 2 ⎫⎨ ⎬ ⎨⎢ ⎥⎣ ⎦ ⎩⎪ ⎪⎩ ⎭

∑ ∑ ∑ ∑ ∑

where CC can be interpreted as half the difference between the output energy of the stack and the

input energy. Using the unnormalized crosscorrelation sum, we can obtain the velocity spectrum.

Now let us consider that a medium composed of horizontal isovelocity layers. Each layer has a

certain thickness that can be defined in terms of two-way zero –offset time. The layers have interval

velocities (v1,v2, …,vN), where N is the number of layers. Consider the raypath from source T to

depth point D, back to receiver R, associated with offset x at midpoint location M. Taner and

Koehler [1 is path as:

w 0 12 2C =t (0), C =1/v ,rms

thickne ties. The rms velocity vrms down to the reflector on which depth point

D is situated is defined as

⎬⎭

12 2 (0)(0) 1

v v trms i it i= ∆∑

=

60 1 2 3

2 2 4( ) ...,t x C C x C x C x= + + + +

N

w ti∆

truncate s follows:

2 2 2 2( ) (0) /t x t x vrms= +

11

hen Eq.(2.5) and Eq.(2.13) are compared, we can see that the velocity required for NMO

velocity that optimally

where vst is the velocity that allows the er to a

hyperbola within the spread length.

from the surface to the boundary is homogeneous, when the

ubsurface consists of multiple horizontal layers. Therefore, in order to estimate the dielectric

here: -vertical reflection travel time to the nth layer.

Application of this formula can provide non-real velocities, if the travel time intervals are small or

if the NMO velocity change is large. Such problems were not encountered in our case.

W

correction for a horizontally stratified medium is equal to the rms velocity.

The huperbolic moveout velocity should be distinguished from the stacking

allows stacking of traces in a CMP. The hyperbolic form is used to define the best stacking path:

(2.14)

2 2 2 2(0) /t t x v= +st st st

best fit of the travel time curve tst(x) on a CMP gath

The velocity estimated from velocity spectrum is the NMO velocity, which is the same as the RMS

velocity assuming that the medium

s

constant of each layer, the RMS velocities have to be corrected to interval velocities. Fig.2.9 shows

the relationship between RMS velocity and interval velocity. The average interval velocity of n-th

layer can be determined using the Dix formula [38].

(2.15)

2 2(0) (0) 12 1(0) (0) 1

V t V trms n rms nn nVn t tn n

− −−=− −

(0)t nVrmsnw -RMS velocity and

Interval

RMS

Velocity

depth

Interval

RMS

Veloci

depth

Figure 2.9. The relationship between the RMS velocity and the interval velocity.

ty

12

2.5 Dielectric constant estimation

Most of the phenomena of EM wave related to GPR are determined by the dielectric constant of the

medium. The difference in dielectric constant of liquid water (about 81) and other materials (e.g.,

soil: 3-5) is large. When the relative dielectric constant of soil is rε , the EM wave velocity (v) in

the soil is given by:

(2.16)

where c is the velocity of light in air. Therefore, the travel time (τ ) from a boundary at the depth (d)

is given by the following formula:

7)

(2.1

r

cvε

=

22 rddv c

ετ = =

13

.6 Application to hydrogeology

.6.1 Introduction

he velocity estimation techniques described in the section.2.4 is commonly used in GPR surveys.

this section, I will demonstrate an example. Using GPR, we normally measure a groundwater

ath in their steady states. In order to evaluate the effectiveness of GPR for monitoring dynamic

roundwater movements and hydraulic property of groundwater, the GPR measurements were

onducted at a water source of Ulaanbaatar city, in Mongolia.

.6.2 GPR survey in 2001

ield experiments in Ulaanbaatar were carried out in September 2000, October 2001, April 2002

nd November 2003. For our field survey, we collaboratively worked with Water Supply &

ewerage System Company of Ulaanbaatar city and Mongolian University of Science and

echnology. RAMAC GPR system (MALA geoscience, Sweden) with a 100MHz antenna was used

this study. The transmitting antenna and the receiving antenna are separated and the CMP

easurement can be carried out. The GPR survey lines were set around a pumping well No10.

0.

the pumping house. Three types of survey lines and grids

aving different densities are set, which are shown in Fig.2.11. CMP measurements were also

carried out along every survey line. Here, we use GPR data acquired along the survey line N. The

surface of the ground is dry sandy and covered by short grass. In this study, we used CMP data sets

2

2

T

In

p

g

c

2

F

a

S

T

in

m

Experimental site is shown in Fig.2.1

The survey lines begin from the wall of

Figure 2.10 Experimental site and a pump house of the No.10

h

14

of GPR measurement, conducted in 2002, April 02-04.

2.6.3 Velocity estimation by CMP

In order to estimate the true depth of the groundwater, we have to estimate the electroma

wave velocity in the soil. CMP can be used for the estimation of the vertical profile of the velocity.

The common midpoint was set at 15m from the wall of the pumping house.

d out when the ground

ater level was highest, which was 6.85m. These changes can be represented by changes of velocity,

hich may be observed more directly through the velocity spectrum derived from CMP data.

ig.2.12(b) and Fig.2.13(b) show the velocity spectrum, which were obtained from Fig.2.12(a) and

ig.2.13(a). Fig.2.12(c) and Fig.2.13(c) show the velocity plots obtained from semblance analysis.

n examination of velocity spectrum shows that the velocity decreased from about 84ns when the

round water level is highest. Comparing it with low water condition, it can be concluded that the

elocity changes from about 0.1440m/ns to 0.1430m/ns.

gnetic

CMP gathers are shown in Fig.2.12(a) and groundwater was in the lowest water level condition. The

ground water level was at 7.1m. Fig.2.13(a) shows the CMP gathers carrie

NNW NE

Well No.10

Pumping house

CMP point

15m 9m

9m

Rx

Tx30m 30m

NNW NE

Well No.10

Pumping house

CMP point

15m 9m

9m

Rx

Tx30m 30m

Figure 2.11 GPR survey lines around the pump house.

w

w

F

F

A

g

v

Figure 2.12 CMP gather along the survey line N, which the water level condition is high

(7.1m). (a) CMP profile 7. (b) Velocity spectrum obtained from (a). (c) Velocity estimated from semblance analysis.

(a) CMP gather (b) Velocity spectrum (c) Velocity plot(a) CMP gather (b) Velocity spectrum (c) Velocity plot

Figure 2.13 CMP gather along the survey line N, in which water level condition is high

(6.85m). (a) CMP profile 6. (b) Velocity spectrum obtained from (a). (c) Velocity estimated from semblance analysis.

(a) CMP gather (b) Velocity spectrum (c) Velocity plot(a) CMP gather (b) Velocity spectrum (c) Velocity plot

15

16

.7 Design of ‘equipment9’ e carried out many measurements by RAMAC GPR system. CMP method needs very long time

r measurement. Therefore, we designed a new equipment (equipment9) for CMP measurement

nd it is shown in Fig.2.14. Fig.2.15 shows a block diagram of equipment9. This equipment is more

seful for measurement process, easy to use and needs short time for measurement. We carried out

ome measurements for checking the designed equipment9. The equipment9 works as follows:

rstly we drug tape1 and tape2 by receiver and transmitter antenna, then this tape (meter) controls

x and Tx antenna position by tape1 and tape2. Distance between Rx antenna and equipment9

hanges in the same distance as between Tx antenna and equipment9, whenever the two antennas

ove apart.

e carried out some measurements by with ‘equipment9’ and without ‘equipment9’ RAMAC GPR

ystem. Main aim of this measurement was to check data accuracy of the two methods. For the

ethods, survey line is the same. Experimental setup is shown in Fig.2.16. Acquired data sets are

imilar and shown in Fig.2.17. Fig.2.17(a) shows raw CMP data without equipment9. Fig.2.17(b)

nd Fig.2.17(c) show raw CMP data with equipment9 and with the same setup.

2W

fo

a

u

s

fi

R

c

m

W

s

m

s

a

Figure 2.14 The equipment9

Figure 2.15 Block diagram of equipment9

17

Figure 2.16 Experimental setup with ‘equipment9’ and without ‘equipment’

Figure 2.17 Data sets of measurements. (a)-data without ‘equipment9’ (b)-data1 with ‘equipment9’ (c)-data2 with ‘equipment9’

Figure 2.18 Trace number 12 of data sets. (a)- trace of measurement without equipment9, (b)-

trace of measurement data1 with equipment9, (c)- trace of measurement data2 with equipment9

18

Fig.2.18 shows a 12th data trace of measurements. Fig.2.18(a) is trace which is estimated from

Fig.2.17(a). Fig.2.18(b) and Fig.2.18(c) are trace estimated from Fig.2.17(b) and Fig.2.17(c).

Advantage of this ‘equipment9’ is that it solves the problem of measurement time. For example

RAMAC GPR system needs too long time for CMP measurement. If measurement interval is 0.1m

and survey line is 9m then we need about 10 minute for the measurement and setup. If measurement

interval is more accurate then we need very long time (eg, if measurement interval is 0.05m then

need about 20 minute).

We used the designed equipment for the measurement then data accuracy was similar to the

measurement without equipment9. The measurement time was estimated to be about 4-5 minute for

m

a ed equipment9 has the advantage in terms of the quick

and accurate measurement. Sometimes, the measuring meter inside of the case is entangled and

ere is a need to fix it. Therefore, the designed equipment9 needs further improvement.

2.8 Time Domain Reflectometry

Time Domain Reflectometry (TDR) is an alternative technique to estimate the dielectric constant of

easurement and setup. When the measurement interval is more accurate then measurement time

lmost does not change. Although, the design

th

19

Figure 2.19 Time Domain Reflectometry

the soil. In general the knowledge of soil moisture

is essential to many applications in hydrology,

agriculture and civil engineering. Among the

various electromagnetic or moisture measurement

methods, TDR has become one of the most

popular methods. This is due to the early

establishment of simple approximate relations

between soil moisture and water content and the

availability of field portable instrument [1].

TDR as moisture measurement method is the

most direct method to estimate water content of

the materials. The ordinary TDR is shown in Fig.2.19. One of the most often used equation applied

for the estimation of the dielectric constant (rε ) is given by Topp et al [37]. It is described as

follows:

2 33.03 9.3 146 76.7rε θ θ θ= + + − (2.18)

where: θ -water content [%]

2.9 Summary

sitions very accurately, we can obtain radar profiles with very high

oherency. We conducted a measurement and processed the data by CMP method for ground water

ent time of

MP measurement without equipment9 is dependent on the measurement interval, but CMP

t dependent on the measurement interval. As seen, the designed

In this chapter, the principles of the theoretical fundamentals of data analysis, application of GPR

for hydrology and design of new equipment for CMP measurement were described.

We conducted a test for groundwater level monitoring by GPR. If we acquire the GPR data by

locating the antenna po

c

level monitoring and estimated velocity spectrum from the data sets and we can estimate dielectric

constant from the velocity spectrum.

As seen, CMP measurements need too long time. Therefore, we designed new equipment for CMP

measurement to save a time. If we used this equipment9 then we can save time and the data

accuracy is almost the same as the CMP measurement without this equipment. Measurem

C

measurement with equipment9 is no

equipment for CMP method needs further improvements.

Chapt

Compact GPR system fo

3.1 Introduction This chapter describes the GPR system which I ha

divided into three main sections, including the sign

conducted laboratory measurements.

er 3

r velocity estimation

ve developed for the velocity estimation. It is

al processing, the developed hardware and the

stigation of underground structures or buried

ethod, a source antenna placed on the surface of a ground, radiates energy both upward

to the air and downward into the soil, and a receiving antenna receive the signal transmitted by the

source. Then any subsurface contrast in electrica

flected back to the surface [2]. The principles of the GPR are illustrated in Fig.3.1. It shows the

er soil. As the transmitter and receiver of the GPR system move along

e ground at a constant velocity, the data regarding the electrical properties of the subsurface

quired. The received signal is recorded by a network analyzer and stored in a

3.2 Principles of GPR The GPR has been extensively used for the inve

objects in geology, civil engineering, environmental and soil sciences [3].

In GPR m

in

l properties will result in some energy being

re

propagation paths in a two lay

th

structure are ac

personal computer (PC). When the electromagnetic wave velocity (v) is known, measuring the travel time (τ ), we can estimate the depth of the reflecting object boundary (d) as follows:

2vd τ

= (m) (3.1)

Here, the travel time is defined as the sum of the time of the transmitted signal reaching the

geological boundary and the time the reflected from the boundary signal is received in a receiver.

20

Transm itting antenna Receiving antennaTransm itting antenna Receiving antennaTransm itting antenna Receiving antenna

1ε

2ε

d1ε

2ε

d1ε

2ε

d

Figure.3.1 Electromagnetic wave reflection at a geological boundary

21

3.3 Survey methods Dependant on the transmitter and the rece surveys can be classified as: common

receiver meth source gather

method. Most tion between

e transmitter and receiver is fixed. However, compared to the common offset survey, common

on source methods use require different signal processing approaches [14].

this study, we used common source CS method. In the CS

ethod, while the transmitter antenna is fixed, the receiving antenna moves along the survey line in

in Fig.3.2(b).

through an inverse ray if directions of the

reflected ray emitted from a transmitter (Tx)

and a receiver (Rx) can be determined. CMP

gather and CS gather are investigated for

determining the directions of an inverse ray.

For simplicity, I present the two way travel

time of 2D homogeneous irregular layer with

a constant velo

iver positions GPR

od, common offset method, common midpoint method and common

GPR surveys use a common offset survey method in which the separa

th

mid point and comm

Most GPR surveys for estimation of dielectric constant use a common midpoint (CMP) mode in

which the separation between the transmitter and receiver moves along the survey line in a certain

distance, as shown in Fig.3.2(a). In

m

a certain distance, as shown

A reflected point at an interface is imaged

city of v.

(a) (b)

Figure 3. 2(a) Common m oint and (b) Common source methods idp

Figure 3.2(c) Common source method not

horizon l layer. ta

22

e theorem of the triangle the travel time hyperbola of a CS method, can be By considering the cosin

defined as: 2

2 2 2 sinx m

x dt tv

α+⎛ ⎞= + ⎜ ⎟⎝ ⎠

(3.2)

where 2 cosmdtv

α= ⋅ - the shortest travel time and offset pairs of the reflected hyperbola. Since the

shortest travel time (tm) and the associated offset ( 2 sinmx d α= − ) cannot be picked up from the

travel time hyperbola (Eq.(3.2)) in a CS method, the least travel time error is applied to fit the travel time hyperbola and to determine velocity (v) and a dip angle(α ) and it is shown in Fig3.2(c) [29].

Unfortunately, since the travel time hyperbola of a CMP is symmetric with respect to (x),

parameters (d) can be determined from fitting the travel time hyperbola only if velocity (v) is known

advance. Travel time CMP method can be derived by the square of the layer velocity as: in2 2

2 2x

d xtv v

⎛ ⎞ ⎛ ⎞= +⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠

(3.3)

owever, in our case (2D homogeneous irregular layer with a constant velocity) travel time

quation is the same. Because, in our case the dip angle equals to 0.

or estimation of the dielectric constant, we used an array antenna system with a controlling switch.

MP array antenna method uses two switches for controlling receiver array antenna and transmitter

rray antenna, but CS array antenna method needs only one switch for controlling receiver array

ntenna. Therefore, as the CS uses one switch, it is suitable for an array antenna. Another advantage

t the size of the equipmen smaller than the case of the CMP method.

advantage of the CS method is that data is not so accurate than the data sets of CMP method

hen the layer i point position,

ut CS method cannot measure the fixed point and measures several continuous points. Therefore,

r further measurements and development of the GPR system we used CS method.

work of this study uses the above principle. The

tenna and 4 receiver array antenna sets and the

H

e

F

C

a

a

of the CS is tha t is

Dis

w n not horizontal. That means CMP method can measure one fixed

b

fo

The GPR system, developed within the frame

developed GPR system uses 1 transmitting an

distance between each antenna is the same.

23

Figure 3.3 General block diagram of the radar system for dielectric constant.

system, the key components are an antenna system, a transmitter and receive unit,

system calibration and the experimental setup.

antenna and four receiving array antennas. W

a controller without m

Receiver antenna). This adjustment makes it possible to quickly measure many points within a

sed commerc

GPR system for measurement of the dielectric

t of soil. In the system, the transmitting antenna receives signals from the network analyzer

tes electromagnetic waves of varying frequencies. The radiated waves will penetrate into

edium and some reflection will be expected from the subsurface layer. Some direct and some

fields will be received by the receiving array antennas. Then the signals are recorded by a

a computer. The GPR system uses an antipodal Vivaldi antenna and

3.4 Structure of the developed system In the GPR

Our developed GPR system uses a transmitting

hen we acquire data, the focusing point can be quickly

adjusted by oving the receiving array antenna (from Receiver #1 antenna to

short time. For the transmitter and receiver unit, we u ial network analyzer, Site Master

ModelS251C (Anritsu company). Since this is a very compact network analyzer, we adopted it for

the GPR system.

#4

Fig.3.3 shows a block diagram of the developed

constan

and radia

the m

scattered

network analyzer and stored in

a network analyzer with low frequencies.

3.5 Antenna One of the most important hardware components for the performance of a GPR system is the

antenna system. The antenna system for GPR system needs to meet several requirements depending

on the application. The antenna characteristics required in GPR system for dielectric constant is

different from other applications. We designed an antenna used in a very broad frequency range for

high imaging resolution [4].

Figure 3.4 Antipodal Vivaldi antenna.

Figure 3.5 Array antenna.

For our GPR system, we selected an antipodal Vivaldi antenna and adapted it for the desired

frequency range. The antipodal Vivaldi antenna is shown in Fig.3.4. This type of antenna has a flat

shape, and it is easy to construct an antenna array. This type of antenna has quite suitable shape for

constructing an array antenna. In addition, this antenna does not require a balance-unbalance

transformer such as balun, and therefore its construction is quite simple [34,35,36].

In the experiment, the antipodal Vivaldi antenna which we designed and fabricated in our laboratory

was used as an element of array antenna as shown in Fig.3.5. Fig.3.6 shows the measured data of

return loss of the designed antenna

which makes it more suitable for GPR applications.

. We find that this antenna works better at lower frequencies

In the experiment for dielectric constant estimation, we used a combination of one transmitter and

four receivers (common source method), i.e., consisting of 5 Vivaldi antennas spaced at every 9 cm

from each other.

24

25

ransmitter and

e receive conditions. For example, the output power and the frequencies can be adjusted

at includes a built-in synthesized signal source.

he Site Master is capable up to 2.5 hours of continuous operation from a fully charged

ain characteristics of this network analyzer are shown in Table 3.1 [6]. The used Site Master

etwork analyzer is shown in Fig.3.7. When we receive a signal, the received signal is shown on the

etwork analyzer display and it is automatically stored in a PC connected to the network analyzer.

3.6 Site Master In this study for dielectric constant measurement, Site Master S251C (Anritsu) network analyzer

has been used. Our laboratory is now cooperatively working with Anritsu cooperation to develop a

new compact network analyzer. A network analyzer is quite flexible in setting the t

Figure3.6. Return loss of designed Vivaldi antenna.

th

dependent on the measurement purposes. This network analyzer is much smaller than the

conventional network analyzers and has a light for convenient work in the field experiment. The

Site Master is a hand held SWR/RL (standing wave ratio/return loss), transmission gain/loss and

Distance-To-Fault (DFT) measurement instrument th

T

field-replaceable battery. The displayed trace can be scaled or enhanced with frequency markers or

a limit lines [5].

M

n

n

Table 3.1 Specifications of Site Master S251C

Frequency Range 625 MHz to 2500 MHz

Frequency Accuracy (CW mode) 75 ppm

Frequency Resolution 10 kHz

Display Resolution 130, 259, 517 data points

operation 0 to 50°C Temperature

Storage –20°C to 75°C*5

Weight 1.81 kgs (4.0 lbs.)

Size 25.4 x 17.8 x 6.10 cm (10 x 7 x 2.4 in.)

26

Figure 3.7 The Site Master Model S251C

3.7 System calibration This section discusses the calibration of the measurement. Calibration plays an important role in

determining the accuracy of the measurement system. To obtain accurate results, the system must be

accurately calibrated. Measurement errors must be reduced by a process that uses calibration

components. In general, there are standards for the calibration. About these standards we will

discuss below.

Initially, the Site Master was calibrated manually with Open, Short, Load calibration components.

In conjuction with a through connection, these components can correct the major errors in a

icrowave test system. These errors are directivity, source match, load match, isolation, and

e carried out the two port calibration for the system. Two port calibration requires two Load

m

frequency tracking (reflection and transmission). We know that adapters and cables degrade the

basic directivity of the system, but these errors are compensated by vector error correction.

W

27

Figure 3.8 Calibration setup of Site Master

components. The main cal er is shown in Fig.3.8 [5].

The experimental setup consists of a network analyzer, a switch box, coaxial cables, attenuators and

antenna arrays. That means, we need to remove a transfer function of a system by a network

analyzer. The transfer function of the system is defined as:

ibration setup of the Site Master network analyz

( ) ( ) ( ) ( ) ( )VNA connectors cables switchH H H H Hω ω ω ω ω⋅ ⋅ = ⋅ (3.4)

where ( )H ω -transfer function of network analyzer VNA

( )connectorsH ω - transfer function of connectors

( )cablesH ω -transfer function of cables

( )switchH ω -transfer function of switch

If we measure the transfer function of the system by the calibration, the network analyzer

alibration by two-port) can removes the transfer function parts. Measured signal spectra with (c

calibration is defined as:

( ) ( ) ( ) ( ) ( )antenna antenna

Tx RxS H H H Tω ω ω ω ω= ⋅ ⋅ ⋅ (3.5)

where ( )antenna

TxH ω -transfer function of transmitter antenna

( )antenna

H Rx ω -transfer function of receiver antenna

( )T ω - transfer function of object (target)

After the removal of the transfer function of the system, we have only a transfer function of

transmitter and receiver antenna and object (target).

The network analyzer was used to evaluate the transmitted portion of signal through the medium

(S21). The switch box controls the array antennas in the array and selects them individually to

perform S21 measurement. In order to remove the effect of wave reflections and loss in the coaxial

cables, attenuators and switch box from the measurement matrix, there is need of calibration with respect to each transmission ( ( ) ( )mea

ijS ω ).

For the calibartion, the below calculations were made. We can calculate system calibration by

dividing each measured transmission by calibration. All measurement effects from the coaxial

cables, attenuators and switch box (calibration factor), can be obtained by dividing each measured transmission ( ( ) ( )mea

ijS ω ) by measured transmission for calibration ( ( _ ) ( )cal meaijS ω ) as follows for the

case of transmitting and receiving array j [28].

( ) ( )ea( ) ( )

mijcal S

Sω

28

( _ ) ( )ij cal meaijS

ωω

= (3.6)

Where: ( ) ( )meaSij ω - is measured transmission

( ) ( )calSij ω - calibrated transmission

(l mea_( ) )caSij ω - measured transmission for calibration

Initially, for the me tenna system. Vector Network Analyzers (VNA) are

ferent calibration procedure and these network analyzers can save a calibration data.

can store the saved calibration file manually, but for our measurement we

need an automatic process. Therefore, each measurement is measured in the frequency domain and

calibration of Channel sets were used

is the calibration procedure made in between the transmitter and first receiver antenna. To solve thi

and then used calibration of Channeldata sets and Channel , Channel , Channel calibration coefficient for each receiving

antenna array data

asurement, we used array an

used for dif

VNA can store each saved calibration data by automatically. But, Site Master can store only one

calibration data and cannot store calibrated data sets which is needed for each antenna during the

measurement process. We

#1 for the final system calibration. Calibration of Channel #1

s

problem we elaborated a software program using MATLAB #1 #2 #3 #4

measurement.

29

tup was conducted as follows:

Figure3.9. General block diagram of calibration and acquired

of Channe (Receiver )

h a transmitter coaxial cable ( ). A transmitter

coaxial cable is connected with a receiver coaxial cable and a coaxial switch ( ) by Through

calibration component and we can receive data omain of received signal ( ).

The received signal is given by Eq.(3.7a).

is

q.(

The below procedure shows in detail how to solve the problem of calibration for array antenna by

Site Master network analyzer. The main calibration se

1. Calibration of Channel #1

Initially, the Site Master was calibrated manually with Open, Short, Load calibration components.

We carried out the two port calibration, then using Receiver #1 coaxial cable. A block diagram of

calibration is shown in Fig.3.9.

l #1 #1data by calibration

The generated signal ( 0V ) will propagate throug xT

1S

#1V in frequency d

#1 1 0xV T S V= ⋅ ⋅ (3.7a)

After this calibration measurement, the transmitter coaxial cable is connected to the transmitter

antenna and the receiver coaxial cable is connected to the receiver antenna and we can acquire the

primary data. The acquired data is given by Eq.(3.7b).

#1 1 21 0measured xV T S S V= ⋅ ⋅ ⋅ (3.7b)

However, for data analysis, we need to have calibrated data ( 21S ). Therefore, we can find th

calibrated data sets by E 3.7c). This calibrated data is displayed in frequency domain and used for

the analysis.

#121

#1

measuredVSV

= (3.7c)

30

This is calibrated now by the calibration of Channel .

Master. Therefore, we use the calibration e Receiver

itially, this system have to be calibrated for t

tore multiple calibration data sets. Therefore, we did the calibration by thru calibration component

alibration coefficient), then using Receiver coaxial cable. General block diagram of

alibration is shows Fig.3.10.

of Channe (Receiver )

The generated signal ( propa

able is connected with a receiver coaxial cable and a coaxial switch ( ) by Thru calibration

#1

2. Calibration of Channel #2 to Channel #4

For the calibration of the receive antennas (from Receiver # to Receiver #4 ) we cannot use a

calibration data stored the Site

2

data for th #1

for the other antennas.

In receiver array antennas. But Site Master is canno

s

(c #2

c

Figure3.10. General block diagram of calibration and acquired

data by calibration l #2 #2

0V ) gates through the transmitter coaxial cable ( xT ). Transmitter coaxial

2Sc

component and we can receive data in frequency domain of received signal ( #2V ).

The received signal is given by Eq(3.8a).

2 0 2#2

1 0 1

x

x

T S V SVT S V S⋅ ⋅

= =⋅ ⋅

to

s given by Eq.(3.8b).

(3.8a)

After this calibration, the transmitter coaxial cable is connected the transmitter antenna and the

receiver coaxial cable is connected to the receiver antenna and we can acquire the primary data. The

acquired data i

2 21 0 221

S S V S S#21 0 1

xmeasured

x

TVT S V S

ΙΙ⋅

=

, for data analysis, we need to have calibrated data (21

). Therefore, we can find this

⋅ ⋅= ⋅

⋅ ⋅ (3.8b)

ISHowever

calibrated data sets by Eq.(3.8c).

#221

#2

measuredVS Ι =V

(3.8c)

da

n data, we elaborated software using MATLAB.

he next receiver antenna calibration data equation is a similar the above principles and we used

ese above principles for the data analysis. Other data sets can be calibrated by Eq.(3.8c). Each

easurement is measured in frequency domain and calibration of channel1 sets was used for final

ystem calibration.

.8 Antenna setup ven with the calibration, a measured signal had some ripple. We think that it is caused by a

ismatching of antennas. We solved this problem by connecting an attenuator to antennas. We

carried out some meas nd it is shown

in Fig.3.11. Fig.3.12 show ec nu r. It was found that 3dB

ttenuator is better than any other attenuators. After this measurement, we carried out 3

he

how y blue curve in

calibrated by calibration of system without attenuator.

he result of this measurement had more ripple than the former measurement and it is shown by

black curve in Fig.3.12. In the third m

cable and antenna and calibrated by calibration system with attenuator. The result of this

easurement had smaller ripple and it was better than the measurements. It is shown by red curve

This calibrated data is displayed in the frequency domain and will be used for the analysis. However,

we need to consider the first receiver calibration ta (calibration by Receiver #1 coaxial cable).

For processing of the calibratio

T

th

m

s

3E

m

urement in the anechoic chamber room, using the attenuators a

s antenna coupling data for ch king the atte ato

a

measurements to test t connecting point for the attenuator. That means, firstly we used attenuator

to connect it to the directly network analyzer connector and then calibrated by calibation of system

with attenuator. The result of this measurement had some ripple and it is s n b

Fig.3.12. For the second measurement, we

T

easurement, attenuator was connected in between the coaxial

m

in Fig.3.12. From these results, we used attenuator connected in between the coaxial cable and

antenna for next several measurements.

31

Figure 3.11 Experimental setup for checking the effect of attenuator

32

Figure 3.12 Antenna coupling data of attenuator connecting test.

33

3.9 Experimental setup Fig.3.13 shows the GPR system for dielectric constant measurement. This system can be operated at

frequencies between 625MHz and 2.5GHz. The measurement and data acquisition were

accomplished by a specially designed control program. Experimental parameters are shown in

Table3.2.

Table 3.2 Specifications of a developed GPR system for dielectric constant

Vivaldi antenna 189x200mm

Coaxial cables 1meters

Calibration full 2 port

Parameter S21

Fstart 625MHz

Fstop 2.5GHz

Number of points 517

This system works a

Firstly, we need to select appropriate frequency range for measurement manually using the

network analyzer.

s follows:

Figure 3.13 A developed GPR system for dielectric constant measurement.

1.

34

tch contoller by elaborated software program using VEE program. Then

ired data is recorded by a network analyzer and stored in a PC.

3.10 Summary In this chapter the PR methods, developed GPR system for dielectric constant

measurement and calibration method of Site M array antenna measurements were

discussed.

We designed the GPR system for determining the d constant of soil and other materials and

developed a calibra he Site Mast en we selected the CS method and Vivaldi

rray antenna, because this method had some advantages for this system. This developed GPR

ystem has to be calibrated for receiver array antennas. But Site Master cannot store multiple

alibration data sets. Therefore, we solved this problem by improving calibration equation for

ystem and other data sets can be calibrated by the improved equation. Each measurement is

easured in frequency domain and calibration of Channel sets was used for the final system

alibration.

2. We control a coaxial swi

GPIB cable connector is used.

3. The system automatically acquires data by changing receiver array antennas using a coaxial

switch.

4. The acqu

principles of G

aster for

ielectric

tion technique for t er. Th

a

s

c

s

#1 m

c

35

oratory experiments

4.1 ction

validate the developed

PR system. We highlighted experimental sites, data acquisition procedures using the developed

e final results.

the research, initially we carried out many measurements using the developed GPR

ystem. We did some measurements by the developed system in combination with the Site Master.

measured raw data sets were processed using a filter (Hanning window). IFFT was

mployed for transforming the data set from frequency domain (frequency domain data of Fig.4.3

nd Fig.4.4) to time domain data (time domain data of Fig.4.3 and Fig.4.4) set. Fig.4.3 and

ig.4.4shows waveforms and spectrum array antenna sets. From the Figures, we can see surface

flection from the sand surface from the waveform. However, reflection from the gravel layer is

ot clear. This means that reflection from the gravel layer is not stronger, because we used low

equency bands and there are some unwanted reflection from the wooden plate of box.

Table 4.1 Network analyzer settings

Calibration 2-port

Chapter 4 Lab

Introdu

This chapter describes the laboratory experiment which we carried out to

G

GPR system and th

4.2 Two layers model

As part of

s

The system includes the Site Master network analyzer, array antenna, switch and switch controller.

Main aim of these measurements was to estimate velocity spectrum from the accurate data sets.

Measurements were made in the wooden sandbox as shown in Fig.4.1. Survey object consists of the

sand, gravel and sand and the experimental model as shown in Fig.4.2. We carried out measurement

changing depth of sand each time by 0.05m. Table 4.1 shows the used Site Master network analyzer

settings. Water content of sand was 7.6% by TDR measurement.

Initially, the

e

a

F

re

n

fr

Parameter S21

Start frequency 625MHz

Stop frequency 2.5GHz


36

Figure 4.1. Experimental setup

Figure 4.2. Experimental model

Figure 4.3(a) Measured data of sand (depth of sand is 0.5m and Receiver antenna set) #1

Figure 4.3(b) Measured data of sand (depth of sand is 0.5m and Receiver et) #2 antenna s

Figure 4.3(c) Measured data of sand (depth of sand is 0.5m and Receiver antenna set) #3

Figure 4.3(d) Measured data o Receive antenna set) r #4 f sand (depth of sand is 0.5m and

37

Figure 4.4(c) Measured data of sand (depth of sand is 0.9m and Receiver #3 antenna set)

Figure 4.4(b) Measured data of sand (depth of sand is 0.9m and Receiver # antenna set) 2


Figure 4.4(d) Measured data of sand (depth of sand is 0.9m and Receiver antenna set) #4

38

39

.3 Two layers model with metal plate

e carried out measurements in the wooden sandbox as shown in Fig.4.5. That experimental

ondition is modeled as a two layer homogeneous model and the first layer is air and the second

yer is sand. Survey object consists of the sand, metal plate, gravel and sand and the experimental

odel is shown in Fig.4.6. We carried out measurement changing depth of sand each time by 0.05m.

he Site Master network analyzer setting is the same as the former measurement. We selected a

istance between the antenna bottom and the sand surface as 0.1m. Water content of sand is 7.6%

y T rr and

ame signal processing was used. Then depth of sand is 0.5m and 0.9m.

e can see clearly surface reflection from the sand surface from the waveform. However, reflection

om the metal plate is not clear, because we used low frequency bands and there is some unwanted

flection from the wooden wall of box.

4

W

c

la

m

T

d

b DR measurement. Fig.4.7 and Fig.4.8 shows waveforms and spectrum a ay antenna sets

s

W

fr

re

Figure 4.5 Experimental setup Figure 4.6 Experimental model

40

Figure 4.7(a) Measured data of sand (depth of sand is 0.5m and Receiver #1 antenna set)

Figure 4.7(b) Measured data of sand (depth of sand is 0.5m and Receiver antenna set) #2

Figure 4.7(c) Measured data of sand (depth of sand is 0.5m and Receiver antenna set) #3

Figure 4.7(d) Measured data of sand (depth of sand is 0.5m and Receiver antenna set) #4


Figure 4.8(b) Measured data of sand (depth of sand is 0.9m and Receiver #2 antenna set)

Figure 4.8(c) Measured data of sand (depth of sand is 0.9m and Receiver #3 antenna set)

antenna set)r #4Figure 4.8(d) Measured data of sand (depth of sand is 0.9m and Receive

41

42

Figure 4.10 Experim tal model en

Figure 4.9 L

.4 The test site in the large sand pit and object

he experimental test by using a small sand box could not give a good result, as it was described in

e Section4.2 and 4.3. The reason was the limitation of the operating frequency and the size of the

rget. Therefore, we conducted more experiments by using a large scale sand pit. And also, we used

different network analyzer to check the developed method. All the measurements were made in

e large sandbox in our GPR laboratory room as shown in Fig.4.9. The experimental model is

how

e selected a sandy area as our research object. The reasons for selecting such area are as follows:

(a) Sand is the common surface material on the Earth’s surface.

(b) Experiment area is very comfortable for test measurement

(c) Sand layer is homogeneous.

e selected a distance between the antenna bottom and the sand surface as 0.1m and the distance

etween the sand surface and the buried metal plate as 0.1m. That experimental condition is

odeled as a two layer homogeneous model and the first layer is air and the second layer is sand.

irstly, we carried out measurement estimation of sand moisture content using a Time Domain

efle

e estimated dielectric constant of the sand in the test area using Eq.2.18. As the data, the TDR

easurement data was used. As a result, it was defined that the water content was 6.7% and related

Test measurements

entioned before as part of the research, initially we carried out many measurements using the

. The results of those measurements were not so good to be used for the

4

T

th

ta

a

th

s n in Fig.4.10.

arge sandbox of GPR laboratory

W

W

b

m

F

R ctometry (TDR) for the purpose of checking the result of the developed GPR system.

W

m

dielectric constant was equal to 4.2854.

4.5

As m

developed GPR system. The system includes the Site Master network analyzer, array antenna,

switch and switch controller

43

analysis. Therefore, we solved this problem by

changing the network analyzer of the developed

system. The new data sets were of good

qualities and used for further analysis. The

laboratory experiment was performed at first

floor of our GPR laboratory in order to check

the data acquisition

Figure4.11 HP8753E network analyzer

Figure 4.12 Measurement setup used by Site

Master network analyzer

process of the developed

GPR system. We selected sand box

experimental sites for the laboratory

measurements. We carried out three

measurements using two different network analyzers, applying two GPR methods. Firstly, we used

our developed GPR system. Here, we used Site Master network analyzer and common source

method. After that, we did two measurements for checking the quality of the first measurement data.

For these measurements, we used HP8753E network analyzer, Common Mid Point methods and our

designed array antenna sets. Fig.4.11 shows the used HP8753E network analyzer.

4

Here, we carried out a measurement using our developed GPR system. The measurement setup of

4.2 Network analyzer settings

he system is able to measure data in the same sand target using lower frequency bands. A

the processing steps are shown in Fig.4.13. Firstly, the data is carried out

Calibration 2-port

.5.1 System with Site Master

the system is shown in Fig.4.12. Table 4.2 shows the used Site Master network analyzer settings.

Table

Parameter S21


Stop frequency 2.5GHz


T

processing flowchart of all

in frequency domain. Accordingly, the signals are transformed into the frequency domain using the

fast Fourier transform (FFT) and then the inverse fast Fourier transform is applied to obtain the

filtered signals in the time domain. The applied filtering function is described by Hanning

44

re calibrated by radar system calibration.

ssed using a filter (Hanning window). IFFT was

frequency domain (frequency domain data of

data of Fig.4.14-4.17) set. Fig.4.14-4.17 show

can see surface reflection from the sand surface

etal plate is not clear. This means that reflection

e, because we used low frequency bands.

windowing function. The frequency domain data a

Initially, the measured raw data sets were proce

employed for transforming the data set from

Fig.4.14-4.17) to time domain data (time domain

waveforms and spectrum array antenna sets. We

from waveform. However, reflection from the m

from the buried metal plate and sand surface is very clos

Measured data

Frequency domain

Background subtraction

Frequency domain

Windowing

Frequency domain

IFFT

Time domain

Velocity analysis

Output

Figure 4.13 Signal processing flowchart

45

Figure 4.14(c) Subtracted antenna coupling data (Receiver antenna set)

#1

Figure 4.14(b) Antenna coupling data (Receiver antenna set) #1

Figure 4.14(a) Measured data of sand (Receiver antenna set) #1

46

Figure 4.15(a) Measured data of sand (Receiver #2 antenna set)

Figure 4.15(b) Antenna coupling data (Receiver #2 antenna set)

gure 4.15(c) Subtracted antenna coupling data (Receiver #2 antenna set)

Fi

47

Figure 4.16(a) Measured data of sand (Receiver set) #3 antenna


#3


48

Figure 4.17(a) Measured data of sand (Receiver # antenna set) 4

Figure 4.17(b) Antenna coupling data (Receiver # antenna set) 4

F

igure 4.17(c) Subtracted antenna coupling data (Receiver #4 antenna set)

49

Figure 4.18 Measurement setup used by HP 8753E

network analyzer and CS method

.5.2 System with HP8753E

Here, we carried out this measurement using a our developed GPR system and HP8753E network

analyzer. This network analyzer has a wideband frequency range and high accuracy. This

measurement setup of the system is shown in Fig.4.18. Table 4.3 shows the used HP8753E network

analyzer setting. This time we changed only network analyzer of the former measurement. The

measurement was for checking the quality of the first measurement data.


We changed the frequency bands from 300MHz to 6GHz. We picked signals of four antenna arrays

and the waveforms and spectrums are shown in Fig.4.19-4.22. The black point indicate the

reflections from sa mployed for

transforming the from the frequency domain data set to time domain data set. We can see surface

reflection from the sand surface and reflection from the metal plate from waveform. This two

reflection clearly separated, because we used high frequency bands.

Calibration Full 2-port

4

Parameter S21


Stop frequency 6GHz


nd surface and metal plate. The same signal processing was e

50

Figure 4.19(a) Measured data of sand (Receiver set) #1 antenna


Figure 4.19(c) Subtracted antenna coupling data (Receiver antenna set) #1

51







#2

52

Figure 4.21(a) Measured data of sand (Receiver # antenna set) 3

Figure 4.21(b) Antenna coupling data (Receiver # antenna set) 3


53

Figure 4.22(a) Measured data of sand (Receiver antenna set) Figure 4.22(a) Measured data of sand (Receiver antenna set) #4



53

#4


Figure 4.22(c) Subtracted antenna coupling data (Receiver #4 antenna set)

54

.5.3 CMP method with HP8753E

We carried out this measurement by CMP array antenna system and HP8753E network analyzer.

Fig.4.23 shows a measurement setup. Table 4.4 shows the network analyzer settings. This time we

changed network analyzer and antenna system. The purpose of the measurement was to check the

data quality of former measurements and designed GPR system. CMP method is one of the well

known signal processing methods, which is widely used in seismic studies. In the CMP data

acquisition, we acquire reflection signal by changing the separation of a transmitter and a receiver,

keeping the center position of the transmitter and the receiver at a fixed position. This can be

achieved by switching the transmitting and the receiving antenna sequentially from the center to the

outer direction as shown in Fig.4.23(b). Three sets of transmitter and receiver pairs were used to

acquire radar signal at one fixed position.


During the measurements, the signal acquired in the frequency domain by the network analyzer is

Fourier transformed, and we obtain the time domain reflection signal. We picked signals of three

antenna array etal

plate is clear because we used high frequency bands.

Calibration Full 2-port

4

switch driver

transmitter array antenna

receiver array antenna

1 12 323

sw itch driver

transmitter array antenna

receiver array antenna

1 12 323

(a) (b)

Figure 4.23 Measurement setup used by HP8753E and CMP method

Parameter S21


Stop frequency 6GHz


s and waveforms and spectrums are shown in Fig.4.24-4.26. Reflection from the m

55

Figure 4.24(a) Measured data of sand (Transmitte and Receiver antenna set) r #1 #1

Figure 4.24(b) Antenna coupling data (Transmitter #1 and Receiver #1 antenna set)

Figure 4.24(c) Subtracted antenna coupling data (Transmitter #1 and Receiver #1 antenna set)

56

Figure 4.25(a) Measured data of sand (Transmitter # and Receiver # antenna set) Figure 4.25(a) Measured data of sand (Transmitter # and Receiver # antenna set) 2 2

Figure 4.25(b) Antenna coupling data (Transmitter #2 and Receiver # antenna set) Figure 4.25(b) Antenna coupling data (Transmitter #2 and Receiver # antenna set) 2

Figure 4.25(c) Subtracted antenna coupling data (Transmitter # and Receiver # antenna set) Figure 4.25(c) Subtracted antenna coupling data (Transmitter # and Receiver # antenna set) 2 2

2 2

2

2 2

56

57

Figure 4.26(a) Measured data of sand (Transmitter and Receiver antenna set) Figure 4.26(a) Measured data of sand (Transmitter and Receiver antenna set) #3 #3

Figure 4.26(b) Antenna coupling data (Transmitter and Receiver antenna set) Figure 4.26(b) Antenna coupling data (Transmitter and Receiver antenna set) #3 #3

Figure 4.26(c) Subtracted antenna coupling data (Transmitter and Receiver antenna set) Figure 4.26(c) Subtracted antenna coupling data (Transmitter and Receiver antenna set) #3 #3

#3 #3

#3 #3

#3 #3

57

58

4.6 Estimation of Velocity spectrum

Using the unnormalized crosscorrelation sum (Eq.(2.10)), we can obtain the velocity spectrum.

Fig.4.27 shows an example of the velocity spectrum, which was obtained by the unnormalized cross

correlation. Fig.4.27(a) shows the velocity spectrum of measurement by the Site Master network

analyzer and CS method, which were obtained from Fig.4.14(c), Fig.4.15(c), Fig.4.16(c) and

Fig.4.17(c). Fig.4.27(b) shows the velocity spectrum of measurement by HP8753E network

analyzer and CS method, which were obtained from Fig.4.19(c), Fig.4.20(c), Fig.4.21(c) and

Fig.4.22(c). Fig.4.27(c) shows the velocity spectrum of measurement by HP8753E network

analyzer and CMP method, which were obtained from Fig.4.24(c), Fig.4.25(c) and Fig.4.26(c).

From the a

vertical direction. The velocity will be used for NMO and image reconstruction. However, in CS,

the velocity can be estimated using the same data sets which we used for imaging. In order to stack

the four traces at several continuous midpoints, we have to shift the signal to compensate for the

different time delays along the penetrating travelpaths. This is normally referred to as

NMO-correction in seismic signal processing. We use the velocity spectrum technique in order to

estimate the rms velocity along the path, and then calculate the theoretical delay shift. The four

traces are then stacked into one trace.

Using the acquired data set, we can estimate the propagation velocity of soil by velocity spectrum

technique and Dix formula. We can see clearly rms velocity of sand from Fig.4.27(b)(c). As seen

from the F d v rly

separated. But, velocity of sand from Fig.4.27(a) is not clear, that means velocity of the first and

second layer is not separated, but velocity spectrum appears continuously. It should be noted that,

the estimated velocity is almost same as the value estimated by using HP8753E. This means that,

even the frequency range is limited, and the reflection waves could not be separated, we could

estimate the velocity of the subsurface layer. Table 4.4 shows the dielectric constant estimated by

the measurements. Table 4.4. Dielectric constant estimation

Instrument GPR method Water content

[%]

Velocity

[m/ns]

Dielectric constant

peak value at each depth t(0), we can obtain the best estimate of the velocity vst in

ig.4.27(b)(c) first layer (in air) velocity and secon layer (in sand) elocity are clea

TDR 6.7% 0.1449 m/ns 4.2854

HP8753E CMP 0.1448 m/ns 4.2925

HP8753E CS 0.1486 m/ns 4.0757

Site Master CS Continuously estimated

around 0.115

59

Figure 4.27(b) Velocity spectrum of the

measurement by System with HP8753E Figure 4.27(a) Velocity spectrum of the

measurement by System with Site Master

Figure 4.27(c) Velocity spectrum of the

en odmeasurem t by CMP meth with HP8753E

60

.7 Summary

This chapter described data analysis of measurements and accuracy of different measurements. As

an initial test, we acquired many data sets by the Site Master, but these results could not give good

results for data analysis. The reason was the limitation of the operating frequency and the size of the

target. Therefore, we conducted more experiments by using a large scale sand pit and we used a

different network analyzer to check the validity of the developed method. The new data sets were of

good qualities and used for the further analysis. We carried out three measurements for checking the

developed system using two different network analyzers, applying two GPR methods.

We acquired data sets by Site Master and could estimate the velocity profile. But reflection of the

sand surface and reflection from the metal plate could not be clearly separated. This means that,

even the frequency range is limited, we could estimate the velocity of the subsurface layer. Velocity

spectrum using the Site Master gives the same result of the velocity spectrum using HP8753E.

Some difference in accuracy of data measurements was caused by the difference in frequency

bandw locity sp ot

clear all frequency bandwidth, but velocity spectrum

appears continuously.

4

idth of the network analyzer. As seen, ve

ly separated by layers because of the sm

ectrum acquired by the Site Master was n

61

Chapter 5 Conclusion

Chapter2, based on the principles of the theoretical fundamentals of data analysis, velocity

is almost the same as the CMP measurement without this equipment.

easurement time of CMP measurement without equipment is dependent on the measurement

terval, but CMP measurement with equipment is not dependent on the measurement interval. As

een, the designed equipment for CMP method needs further improvements.

Chapter3, the principles of GPR methods, developed GPR system for dielectric constant

easurement and calibration method of Site Master for array antenna measurements was described.

e designed the GPR system for determining the dielectric constant of soil and other materials and

eveloped calibration technique for the Site Master. Then we selected the CS method and Vivaldi

rray antenna, because this method had some advantages for this system. This developed GPR

ystem has to be calibrated for receiver array antennas. But Site Master can store only one

alibration data and cannot store calibrated data sets, which is needed for each antenna during the

easurement process. Therefore, we solved this problem improving calibration equation for system

nd other data sets can be calibrated by the improved equation. Each measurement is measured in

equency domain and calibration of channel1 sets was used for the final system calibration.

Chapter4, data analysis of measurements and accuracy of different measurements were described.

s former measurements, we acquired many data sets by the Site Master, but these results could not

ive good results for the data analysis. The reason was the limitation of the operating frequency and

e size of the target. Therefore, we conducted additional experiments by using a large scale sand pit

Determination of the soil dielectric constant is important for civil and environmental applications.

In this thesis, the development of a GPR system for estimation of dielectric constant and its

application to environmental studies were described. We developed a new GPR system and used it

for the dielectric constant estimation.

In

estimation of CMP method used by RAMAC GPR system and designed new equipment for CMP

method were derived. We carried out several measurement of groundwater level monitoring by

GPR. If we acquire the GPR data by locating the antenna positions very accurately, we can obtain

radar profiles with very high coherency. We analyzed data sets and estimated velocity spectrum

from the data sets. Therefore, we can estimate dielectric constant from the velocity spectrum from

CMP data sets. As seen, CMP measurements need too long time. Therefore, we designed new

equipment for CMP measurement to save a time. If we used this equipment then we can save time

and the data accuracy

M

in

s

In

m

W

d

a

s

c

m

a

fr

In

A

g

th

62

and we used a different network hod. The new data sets were of

good qualities and used for furth elocity of the subsurface layer.

elocity spectrum using Site Master is gives the same result of the velocity spectrum using

analyzer to check the developed met

er analysis. We could estimate the v

V

HP8753E. Some difference in accurate data measurements was caused by the difference in

frequency bandwidth of the network analyzer. As seen, velocity spectrum acquired by the Site

Master was not clearly separated by layers because of the small frequency bandwidth but velocity

spectrum appears continuously.

Considering the future work, the following works can be considered:

1- Developed GPR system still needs some improvements and our laboratory is now cooperating

with Anritsu cooperation in order to develop a new compact network analyzer.

2- More complex experimental configurations should be investigated.

63

] Hui Zhou, Motoyuki Sato, Archaeological Investigaion in Sendai Castle using GPR, 2000,

Fan cs, 2004, 3-9.

, Anritsu company, October

001.

] Web site, http://www.anritsuwiltron.com/downloads/files/11410-00232.pdf

References [1] Topp GC (2001) Electromagnetic wave measurements of soil water content: a state-of-theart. In:

Kupfer K, Hübner C (eds) Proceedings of the Fourth International Conference on Electromagnetic

Wave Interaction with Water and Moist Substances, Weimar, Germany, May 13-16, pp 327-335.

[2] Annan, A.P., 1973.Radio interferometry depth sounding: part I –theoretical discussion.

Geophysics 38 (3).pp 557-580.

[3

Archaeological Prospection , Apr.

[4] Motoyuki Sato, Yusuke Hamada, Xuan Feng, Fan-Nian Kong, Zhaofa Zeng and Guangyou

g ,. GPR using an array antenna for landmine detection, Near Surface Geophysi

[5] Site Master Model S251C Antenna and Cable Analyzer, user’s guide

2

[6

] Shie Qian and Dapang Chen, Joint time-frequency analysis, Prentice Hall, Upper Saddle River,

ew Jersey, 1996.

] Alfred Merting, Signal analysis, John Wiley & Sons Ltd, 1999.

] Yilmaz,O., Seismic Data Processing, Society of Exploration Geophysicists, 1987.

0] Taner, M.T., Koehler, F., 1969, Velocity spectra-digital computer derivation and applications of

elocity functions: Geophysics,39, pp 859-881.

1] Shie Qian, Introduction to Time-frequency and wavelet transform, Prentice Hall, Upper Saddle

iver, New Jersey, 2002.

2] Merill l. Skolnik, Introduction to Radar systems, third edition, Irwin/McGraw-Hill Ltd, 2001.

3] Lawrence B.Conyers, Dean Goodman, Ground Penetrating Radar: an introduction for

rchaeoloqists, A division of Sage publications, Inc, 1997.

4] D.J.Daniels, Surface-penetrating radar, The Institution of Electrical Engineering, London,

nited Kingdom, 1996

5] Liang Chi Shen, Jin Au Kong, Applied electromagnetism, third edition, PWS publishing

ompany, 1987.

6] David H. Staelin, Ann W. Morgenthaler, Jin Au Kong, Electromagnetic waves, Prentice hall

ternational editions,1994.

7] W. Nakata, H. Togawa, H. Yokoyama., Measurement of Snow Dielectric Constant Profile at

HF Band Using Probe Sensor Array, The IEICE transactions on electronics (Japanese edition),

ol.J87-B No6, pp.865-873, 2004.06

8] The Fundamentals of Signal Analysis: Application note243, Agilent technologies innovating

e HP Way, 2000

[7

N

[8

[9

[1

v

[1

R

[1

[1

a

[1

U

[1

c

[1

in

[1

U

V

[1

th

64

etwork Analyzer Basics, Hewlett-Packard Company, 1998

0] Shimon Coen, Velocity and density profiles of a layered acoustic medium from common

T&E International, International Symposium (NDT-CE 2003)

rnational, International Symposium (NDT-CE 2003)

er

nderson, N.L. and S.Cardimona, Forward Modeling of Reflection Seismic and Ground

, Federal Highway

otoyuki Sato, The application of migration to SAR-GPR system for landmine

2

PR system for

lando, Florida, USA, pp179-184

amage Detection of Civil Structures, IEEE Transactions on Antennas and

ty of Calgary, January 23, 2001

ity.

ICAMP, CP 6065, 13081-970 Campinas (SP), Brazil, March 4, 2003

of the Vivaldi Antenna. Proceedings of the IEE 135, Pt.H,

Dauvignac J., Pichot C. and Cashman J. 1988. A new design tapered slot antenna

[19] David Ballo, N

[2

source-point data, Geophysics. Vol.47. No6(June 1982), pp 898-905

[21] Qi Lu, 2002.Monitoring of Subsurface Environment by Ground Penetrating Radar, MSc Thesis,

Faculty of Engineering, Tohoku University, Japan.

[22] J Davis, Y Huang, SG Millard, JN Bungey, Determination of dielectric properties of insitu

concrete at radar frequencies, ND

[23] Imad L.Al-Qadi, Samer Lahouar, Amara Loulizi, GPR: From the State-Of-the-Art to the

State-Of-the-Practice, NDT&E Inte

[24] Christopher M. Smemoe, Processing and Visualization of Ground Penetrating Data for

Assessing Natural Hydrogeologic Conditions, Term paper, Brigham Yong University, Decemb

2000.

[25] A

Penetrating Radar Signals to Aid in Field Survey Design and Data Interpretation

Administration and Missouri Department of Transportation special publication, 2000.

[26] Xuan Feng, M

detection, Technical report of IEICE, SANE2003-66(2003-9), pp7-1

[27] Motoyuki Sato, Zhaofa Zeng, Guangyou Fang, Huan Fang, Stepped-frequency G

Landmine detection,Proceedings of SPIE vol.5089, Detection and Remediation Technologies for

Mines and Minelike Targets VIII, April 2003, Or

[28] Yoo Jin Kim, Luis Jofre, Franco De Flaviis, Maria Q.Feng, Microwave Reflection

Tomographic Array for D

Propagation, Vol.51. No.11, November 2003.

[29] Tan K.Wang, Prestack Inverse-Ray Imaging of A 2D Homogeneous Layer: A Tutorial Study,

TAO,Vol.13, No.4, pp 399-416, December 2002.

[30] Gary F. Margrave, Numerical Methods of Exploration Seismology: with algorithms in

MATLAB, The Universi

[31] Kamal Mansour Al-Yahya, 1987, Velocity Analysis by Iterative Profile Migration, Ph.D Thesis,

Stanford Univers

[32] Herve’ Perroud, Martin Tygel, Velocity estimation by the CRS method: A GPR real data

example, DMA-IMECC-UN

[33] Allen B. Cunningham, Harland H. Heffring, Interpretation of velocity spectra, Geophysics,

Vol.45, No.12 (December, 1980), pp 1741-1752

[34] Gazit E. 1988. Improved design

89-92

[35] Guillation E.,

65

, pp286-289

nes, Water Resour. Res. 16(1), pp.574-588

for ultra-wideband applications. Microwave and Optical Technology Letters 19

[36] Langley J., Hall P. and Newham P. 1993. Novel ultrawide-bandwidth Vivaldi antenna and low

crosspolarisation. Electronic letters 29, 2004-2005

[37] Topp, G.C., Davis, J.L., Annan, A.P., 1980. Electromagnetic Determination of Soil Water

Content: Measurements in Coaxial Transmission Li

[38] Dix, C.H., 1955. Seismic Velocities from Surface Measurements, Geophysics 20, pp.68-86

66

now.

ho

ccepted me to the study in his laboratory and supervised me through all studies. His valuable

uidance throughout my studies, both academically and personally, he offered me spareless

uidance, support, patience and understanding during the last 3 years.

also would like to thank my thesis committee members, Prof. H.Niitsuma and Associate Prof.

.Asanuma of Tohoku University for their suggestions and advice from them.

also wish to thank Dr. Damdinsuren Amarsaikhan, Dr. Timofei Savelyev, Dr. Takao Kobayashi, Dr.

uan Feng, Dr. Seong-Jun Cho, Dr. Zheng-Shu Zhou, Dr.S.Ebihara for their valuable suggestions

nd kind help during my research work.

am grateful for my previous and present colleagues, such as T.Abe, K.Murakami, E.Igarashi,

.Koike, K.Takahashi, Q.Lu, J.Zhao, T.Hamasaki, R.Tanaka, Y.Hamada, K.Iribe, K.Yoshida,

.Watamura, K.Masuzawa, U.Ramdaras, K.Baker, Ralf Hermann, Badrakhgerel, W.Urihan,

.Takayama, Y.Kado, Y.Nihei, K.Ishiro, M.Takahashi, S.Kusano, and N.Hayashi for their help and

upport throughout the years.

owe thanks to my parents and my family for their continual support and love.

y heartfelt thanks to all of you, again.

admidtseden Ganchuluun.

ugust 10, 2004 in Sendai

Acknowledgements

I have to express my thanks to many wonderful people who helped and supported me up to

First of all, I would like to express my special gratitude to my supervisor Prof. Motoyuki Sato, w

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Design of a GPR System for Dielectric Constant Measurement