1. Applications of Ground Penetrating Radar (GPR) in the
Context of Geothermal Reservoir Exploration
By: Jodutt Basrawi
PKU Advisors: Professor Qinghua Huang, Mr. Wan Wei, and Dr. Li Zhanhui
Date: August 26, 2016
1
2. Abstract
This project investigates shortcomings and possible solutions of Ground Penetrating Radar
(GPR) in the context of geothermal reservoir exploration. The primary pitfall of GPR within
the geothermal industry is its inability to map deeper than 50 meters below the surface. Most
accessible geothermal reservoirs, on the other hand, are at least 1 kilometer below the ground
surface. The following hypothesis was proposed — GPR may need to be used in conjunction
with non-geophysics based exploration methods in order to render GPR as a cost-effective
exploration tool relative to more burdensome exploration methods (e.g., transient electromag-
netic surveying, drilling, etc.). The approach of this project comprised the processing of GPR
profiles (obtained from Beijing’s Suiyuan Residential District) in-line with procedures that
ultimately identified materials through their dielectric constants and flow-network features.
Two answers followed the data processing: (a) GPR poses as a successful geothermal tool
when in conjunction with flow network mapping (e.g., trace tests), micro-drilling, sedimentary
mapping, and geochemical mapping; (b) Northern Beijing, particularly among the foothills of
the Yanshan mountains, can host Enhanced Geothermal Systems (EGS). GPR’s utility in the
geothermal industry would increase investments for the geothermal industry and to the field
of geophysics. The results from this project can potentially add value to an already priceless
instrument that was initially not designed for geothermal exploration (i.e., the GPR device
itself). Overall, by means of the two aforementioned answers, the objectives and hypothe-
sis of this project have met affirmative answers (i.e., educated guesses were deemed correct),
though additional fieldwork, research time, and professional/academic feedback are necessary
to follow-up with the findings and notions of this project.
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4. 1 Introduction and Purpose
1.1 Background
Geothermal energy is thermal energy that disseminates through the earth’s subsurface. The
source of this energy is the earth’s inner core, which hosts radioactive reactions that generate
heat 24 hours, 7 days a week. This heat can be stored in underground reservoirs, called geother-
mal reservoirs (figure 1). Finding accessible geothermal reservoirs is a priority among many
geologists and environmental engineers. Unfortunately, there are many obstacles to finding
geothermal reservoirs, despite advances in technology and knowledge in geophysics (Jeanloz
and Stone, 2013).
Geothermal reservoirs are located at least 1 kilometer below the earth’s surface. One rea-
son why these reservoirs do not appear at shallower depths stems from insufficient geothermal
energy (i.e., not enough energy to boil water or generate steam in geothermal plants) being able
to reach the surface (Leng and Mao, 2015). Geothermal power plants are usually installed near
or on-top of geothermal reservoirs that have been fully mapped/characterized (Brzozowski,
2011). The water, upon being heated in the reservoir, drives conventional electricity-generating
turbines (figure 2). Like a water wheel, these turbines convert kinetic energy to electrical en-
ergy (figure 3).
If taken to its full projected potentials, geothermal energy can power cities, electric ve-
hicles, and all other electronic devices without any dependence on or conjunction with other
energy-generating means (e.g., coal, wind, solar, etc.). For example, the recoverable amount
of geothermal energy in the United States is 200 ∗ 1021 joules, and the average annual domes-
tic energy consumption of the United States is 100 ∗ 1018 joules. This amount of energy, if
reached, can create competitive pricing scenarios for geothermal energy relative to other alter-
nate energy resources (figure 4) (Jeanloz and Stone, 2013).
There are several ways to locate geothermal reservoirs. One way is to map naturally-
occurring hot springs at the earth’s surface (e.g., Yellowstone National Park, Hubei hot springs,
northern California geysers, etc.) and subsequently map the flow paths of said hot springs.
Surface mapping can be done by geologic mapping, and flow path mapping can be done by a
configuration of trace tests (i.e., tracing flow paths with geochemical markers), seismic map-
ping methods, and geologic cross-sections (figure 5). Another way is to conduct a transient
electromagnetic survey in an area presumed to have high heat-flux values. Such areas are usu-
ally located at tectonically-active areas (e.g., the ring of fire, the Tibetan Plateau, etc.) in which
tectonic forces either thrust or spread magma towards the earth’s surface (Li et al. 2015). A
final notable way to locating geothermal reservoirs is through geochemical mapping (figure 6).
Geochemists and geologists investigate outcrops and determine the mode of minerals across
certain land areas in order to determine types and histories of flow-networks. The geochemical
data pertaining to the flow-networks assists scientists in stating likelihoods for the existence of
accessible geothermal reservoirs (Liu, 2015).
All of the aforementioned exploration methods have two traits in common: they all require
drilling as a means of confirming findings and they require large investments in terms of time,
money, and man-power. These costs have frequently discouraged exploration of potentially
fruitful reservoirs.
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5. Ground Penetrating Radar (GPR) may bring solutions in terms of costs.
GPR uses radar waves that penetrate the earth’s surface and reflect off of subsurface layers
at varying reflection velocities. A reflection velocity of a given radar wave depends on the
dielectric constant(s) of materials that the wave encounters. This relationship can be mathe-
matically represented as follows:
vr =
c
√
ξr
(1)
where vr is the reflection velocity (length/time), c is the speed of light (about 3 ∗ 108 me-
ters per second), and ξr is the relative dielectric constant of a material (unitless). Since most
subsurface materials each have their own ranges of dielectric constants, GPR can differentiate
subsurface materials based on their relative dielectric constants (Clayton, 2016). A table of
subsurface materials and their dielectric constant(s) can be seen in figure 7. Note that in this
figure, water, regardless of whether it is salty or not, has the highest relative dielectric constant.
Also note that wet soils have higher dielectric constants than dry soils. Finally, the dielectric
constants of many sedimentary and igneous rocks are nearly the same.
GPR is able to map the shallow subsurface (i.e., less than 50 meters). GPR profiles are
initially mapped as time vs. horizontal-distance plots and subsequently processed to create
depth vs. horizontal-distance plots. GPR devices can be operated by 1, 2, or 3 individuals and
can be walked along lines of interest to obtain profiles. Because of low demand of man-power
and equipment relative to many other , constructing GPR profiles is not as exorbitant relative
to constructing many other geophysical profiles (Lynne, 2014).
With the benefits of GPR come some costs. First, unlike other electromagnetic exploration
methods, GPR devices cannot map subsurfaces deeper than 50 meters, and most effective
geothermal reservoirs are located at least 1 kilometer in depth. Second, GPR devices some-
times lack the ability to map areas that experience frequent rainfall, since the radar waves do
not present predictable behavior when they travel through large bodies of surface and ground-
water. Third, GPR devices depend on the frequency of radar waves to map clear subsurface
pictures. Higher frequency denotes clearer pictures; however, the higher the frequency of a
radar wave, the less depth it can penetrate, which leads to shallower GPR profiles (Zhu, 2015).
1.2 Motivation
By expanding applications in any alternative energy setting, harmful dependencies on fossil
fuels would eventually become less valuable for everyone. These dependencies not only per-
petuate environmental degradation, but also geopolitical and socioeconomic turmoils (e.g., the
Iraq War, Saudi-Iranian relations, and recessions in Russia and Venezuela). Rendering GPR
as a versatile tool in the geothermal industry could alleviate problems associated with long-
standing geologic and resource-extraction obstacles (e.g., characterizing flow networks, iden-
tifying ideal drill sites, etc. Additionally, GPR can assist geoscientists with ridding mysteries
in the geologies of developing countries, which have all contributed to economic hindrances.
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6. 2 Project Description
2.1 Problem Definition
The current arsenal of exploration methods used by the geothermal energy industry pose only
exorbitant means of geothermal prospecting. The expensive nature of geothermal exploration
has discouraged investments in the geothermal industry that would have otherwise enabled
thorough research on geothermal systems that are at least 100 kilometers away from tectonic
plate boundaries (most geothermal power plants are located at tectonic plate boundaries) (SIG,
2016). The investments would likely assist with locating geothermal reservoirs that have never
been prospected before.
2.2 Problem Description
Drilling, seismic surveying, and geologic fieldwork provide a plethora of information about
geothermal landscapes at high costs. Alternative and cheaper exploration means are slim in
number and associated with high-risks, which leaves authorities in geothermal industry and
geothermal academia to rely on investors daring enough to take financial and scientific risks.
Additionally, most geothermal sites at least 100 kilometers from major tectonic boundaries
have not been mapped at all due to the lack of incentives associated with the lack of investments
(T&A, 2015). Therefore, the world’s energy arena has been overlooking the capabilities of
geothermal energy, which is a renewable energy source that needs more exploration options
with less expenses/risks attached to them.
2.3 Explanation of Materials Used
The materials used in this project include geologic maps of the United States and China, aca-
demic papers from geophysics conferences, papers from world-renowned faculty members,
Adobe Illustrator, MATLAB, MATGPR, bh tomo (borehole tomography program), LATEX, and
online finite-difference time-domain method (FDTD) simulators.
2.3.1 Geologic Maps
Geologic maps show the location, type, and deformation of rocks in an area of interest. Ge-
ologists can interpret geologic sequences (e.g., deposition, faulting, unconformities, etc.) and
make educated guesses about an area’s resources (i.e., allocation, resource mode, etc.). Since
geothermal reservoirs influence their surrounding rocks, minerals, and soils in signature ways
(e.g., flow-networks, heat and its trajectory, the chemical composition of melts, etc.) (Becker,
2015) geologists can make inferences about geothermal reservoirs from geologic maps as well.
2.3.2 Academic/Faculty Papers
Papers written by geoscientists and news establishments were read in order to understand the
successes and pitfalls of geothermal research. There were only two papers read for this project
that directly tackled the question of GPR’s relationship with geothermal reservoir exploration.
Dr. Bridget Y. Lynne of the University of Auckland wrote both of these papers, and she was
contacted for an academic interview to supplement this project. Both papers conclude that
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7. GPR is a useful tool in conjunction with other exploration methods in the context of geothermal
reservoir exploration.
2.3.3 Programming
MATLAB was used to construct plots, velocity models, and split-step GPR models (a less
accurate, yet faster, modeling process relative to FDTD modeling). Split-step modeling splits
frames of a model and constructs those frames in a step-by-step fashion. MATLAB also served
as the base program for MATGPR — a GPR program that interprets, processes, and displays
GPR data — and bh tomo — a borehole-data based program that extrapolates subsurface char-
acteristics between a number of boreholes. Since no boreholes were drilled for this project,
bh tomo was only used for creating borehole models.
2.3.4 LATEX
LATEX is a writing tool that allows users to construct and display mathematical equations and
scientific diagrams. LATEX also fits and edits images to be placed in PDF files.
3 Explanation of Methods Used
GPR profiles were obtained from the Suiyuan Residential District (figure 8) in Beijing by
Peking University undergraduate and graduate students. The profiles were obtained before the
commencement of this project. About 100 profiles were processed in this project, though only
a few of the profiles are displayed in this project.
The raw data was processed in MATGPR software (Tzanis, 2016). The processing steps
used are as follows:
1. Set statics, polarity, and topographic corrections (raw GPR data does not display cor-
rect topography and polarity — these corrections paint a more accurate profile. Statics
correction(s) remove unnecessary static signals).
2. Remove low frequency components (i.e., de-wowing) to emphasize high-frequency sig-
nals that could indicate subsurface anomalies.
3. Set a standard automatic gain control (standard AGC) to emphasize low amplitudes and
de-emphasize high amplitudes of the radar waves (we care about the frequency of the
waves and their velocities, not their amplitudes, and we try to ”equal them out” to avoid
misinterpretations and create to create a less-cluttered profile).
4. Remove additional global background noise (i.e., signals that remain constant across a
GPR profile of interest; similar to statics correction).
5. Trim the travel-time window to remove unclear signals from deep depths (usually, the
deeper one looks at a profile, the less clear the profile appears).
6. Convert time to depth by means of a velocity model based on the earth materials in a
given GPR profile.
7. Apply a centroid-frequency calculation algorithm across a given GPR profile (not all of
the profiles in this project show the centroid-frequency calculation for the purpose of
displaying how this calculation changes the data’s appearance).
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8. Following these steps, 2-D models were constructed with intentions for them to resemble the
processed GPR data as much as possible. The models include subsurface anomalies that have
similar characteristics to the anomalies found in the data. The data and models were finally
compared to data, models, and maps from existing literature.
Summary of Week-by-Week Progress
Between June 18 - July 29 (Week 1 to Week 5), in order of sequence, I created and had my
reading list approved; my research proposal was approved; I requested raw GPR data from
an arbitrary rural area in Northern Beijing; and I completed the research proposal and the
midterm report. Additionally, throughout those weeks, I familiarized myself with the pro-
grams and maps at hand.
Following the midterm report submission, I digitized some GPR plots, outlined trends
in the processed data, and reached affirmative answers for my hypothesis’s questions. From
hereon until the submission of this report, the poster and presentation will be proofread and I
will continue to read relevant literature.
4 Discrepancies with Week-by-Week Schedule
I expected to obtain geothermal anomaly coefficients and to utilize meeting-minutes from the
International Conference on GPR. However, I realized the coefficients I found have no bearing
on this project and I thus ceased to analyze them or search for more. Furthermore, the minutes
from the GPR conferences had little relevance to my research and are thus barely referred to
in this research.
Another discrepancy stemmed from my inability to connect with the desired entities for
academic interviews. Only Dr. Bridget Y. Lynne replied to my interview request.
Throughout the program, I expected to be in contact with Professor Huang and Dr. Li Zhan-
hui at least twice a week either through email or in-person. Nevertheless, Professor Huang has
been preparing for numerous conferences and Dr. Li Zhanhui has been conducting fieldwork
these past few weeks. Both of them have prioritized their works to the extent that they grad-
ually became less available as time passed. As a result, following the half-way mark of the
program, my scheduled appointments with them did not take place. Thankfully, my lab-mates,
all of whom are PhD students in geophysics, have reviewed my work and offered their feed-
back. They have also offered me times to practice my presentation with them.
5 Experimental Results
5.1 Data and Result Collection Process
Raw GPR data sets were obtained by Peking University students. The data sets were then
compiled into a personal computer (the file types of the data sets include DZT and RD3) and
organized on the basis of radar frequency.
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9. 5.2 Metric Used
The relative dielectric constant was the primary metric used to distinguish earth bodies and
water bodies. Water contents were also measured (the ratio of the mass of water in a soil
volume and the mass of solids in the same soil volume), though those measurements were also
based on the dielectric constant (i.e., water content and dielectric constant values are directly
proportional).
5.3 Discussion of Figures and Results
5.3.1 Raw Data and Normal Faulting
The raw GPR profiles from this project show a conspicuous normal offset (i.e., a vertical dis-
placement of rock bodies) near the profiles’ centers (figure 9). Large amounts of static and
background data provide unclear images at deep depths/high travel times and ultimately show
negligible anomalies. There may be some small offsets at the edges of these profiles, though
further processing would be required to confirm this notion. Given the offset, we can infer
that there is a normal fault in the study area. Normal faults provide efficient flow pathways if
such faults overly geothermal reservoirs (this is illustrated in figure 1, if the offset is treated as
a graben, a.k.a a land area bounded by normal faults). Another profile showing normal fault-
ing can be seen in figure 10. Normal faults are also indicative of tensile stresses in the area.
Geothermal reservoirs often occur in tensile-stress landscapes, for these stresses expand and
thin the lithosphere. Expansion and thinning of the lithosphere is a mechanism that drives heat
towards the surface. This mechanism is illustrated in figure 11.
GPR devices disseminate radar waves and retrieve radar waves through the principles posed
by Maxwell’s equations (figure 12). The dielectric constant influences the electric properties
of GPR’s radio waves by means of the relationship shown in equation 1.
5.3.2 Processed Data/Profiles
After processing the profiles and their data sets, a clear group of anomalies and soil bodies be-
came visible (figure 13). The raw data had more noise and the processed data not only has less
noise, but also emphasized features on anomalies. Two notable features — an aquifer (likely
to be clay) and multiple rock bodies (likely to be quartz; indicated by parabolas in figure 13)
— point to a subsurface setting that can possibly host an enhanced geothermal system (EGS).
EGS systems are geothermal systems that extract heat from hot dry rock (HDR). Typically,
water is used to bring bring heat from HDR to a geothermal power plant. The identities of
the two features resemble the identities in one of the split-step models for this project (figure
14). If the quartz bodies are found to be quartz sinters (i.e., rocks formed by precipitation of
hot alkali minerals originating from hot water flows), then the study area has recently (i.e.,
less than one million years ago) experienced a large exchange(s) of heat (i.e., at temperatures
above 200 degrees Celsius; enough heat to power a geothermal plant) between the surface and
subsurface. If the aquifer were to be made of clay, it can pose as a building block for a future
anthropogenic flow network.
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10. 5.3.3 Centroid Frequency
An additional processing step, called centroid frequency processing, calculates the weighted
mean of frequencies across a given GPR profile. The calculation is performed through the
following equation:
fc =
∞
0 fS(f) df
∞
0 S(f) df
(2)
where fc is the centroid frequency, f is the frequency of a signal, and S(f) is the spectral
centroid associated with a given frequency f. Overall, this equation presents a weighted mean
of frequencies present in a signal. It offers a synopsis of the spectral content of a data set (i.e.,
a measure of changes in propagation conditions).
This renders a profile of interest into a “moisture map” in which the profile is color-coded
on the basis of rock/soil moisture content (blue implies high moisture content, red implies
low moisture content, and intermediate colors between blue and red imply an intermediate
spectrum of moisture contents). In figure 21, moisture is ubiquitous across the profile, possibly
as a result of recent rainfall. Figure 21 does not follow any lines of travel or outline any
flow-network paths. Thus, if EGS were to be installed in this area, geoscientists would likely
recommend that either anthropogenic flow networks be constructed or that additional mapping
would be necessary to find naturally-occurring flow networks. Moreover, centroid frequency
values correlate with soil roughness values, which in turn relate to soil moisture contents (high
soil roughness implies low moisture content, and vice-verse; figure 20 by Lambot, 2006).
5.3.4 Converting Travel Time to Depth
Through velocity models that are unique to each GPR profile displayed in this report, travel
time was converted to depth (i.e., nanoseconds to meters). The maximum depth that is por-
trayed among the profiles is 10 meters at a frequency of 100 megahertz (a relatively low fre-
quency value). The profile with the highest resolution is shown in figure 22. This profile also
shows the aforementioned normal offset.
5.3.5 Models
Models serve as references for comparison purposes (figure 14, components of models 15, fig-
ure 23, and figure 24), though all models ever made by humankind are wrong (for they always
make assumptions). Figure 23 was used to quantify basin depths and figure 24 was used to
compare soil roughness. Unfortunately, the basins presented no major features pertinent to
EGS. Rough bedrock, based on comparisons between figure 20 and figure 24 is likely deeper
than what the GPR device can map.
The mathematical infrastructure overview for the models of this project is in the following
equations and explanations.
In the frequency domain, total current density flowing in the earth is related to the electro-
magnetic (EM) field.
JT (ω) = σE(ω) + iω (ω)E(ω) = σ ∗ (ω)E(ω) = iω ∗ (ω)E(ω) (3)
10
11. σ ∗ (ω) = ω + iω (ω) (4)
∗ (ω) = −i
σ ∗ (ω)
ω
(5)
Here, JT is the total current density flowing through the earth, ω is frequency, E is electric
field, is resistivity, and σ is stress. This current density is used as a comparison point to
detect anomalies in a model setting. Then, the ratio of imaginary to real numbers is related to
the loss tangent:
tan(δ) =
σ + ω σ(ω)
ω i
(6)
where i is the imaginary part of resistivity. The quality factor of the model (Q) is the
inverse of the loss tangent.
Q =
1
tan(δ)
(7)
The quality factor acts as a threshold that decides what anomalies to include a split-step
model.
Finally, phase velocity V (ω), non-dispersive velocity V0, power-law term Pω, and loss
tangent exponential n can be used to obtain velocities for a split-step GPR model.
V (ω) = V0P(ω) (8)
V0 =
1
√
µK 0cos(π
4 (1 − n))
(9)
Here, K is a reference relative permittivity number, 0 is permittivity of free space, and µ
is magnetic permeability.
P(ω) =
ω
ωc
1−n
2
(10)
Here, ωc is a reference frequency of light.
n =
2
π
tan−1
Q (11)
1 In figure 15, the anomalies appear as parabolas. This is because this project did not perform
migration analysis (i.e., analysis that accounts for the delay in reflection and the parabolic
shape of the reflection in order to construct a shape that matches the shape of the anomaly in
the GPR profile) due to time constraints and due to the need for either more information or
more assumptions (there is another whole field dedicated to the study of migration, in which
hundreds of papers have been published). The clay layer does not appear as a parabola because
the layer spans the entire profile; the delay is negligible when taken across a whole profile and
the parabolas mix with each other like ripples in a pond mix from multiple splash sources to
cancel out extraneous noise, thereby creating a shape that resembles the actual anomaly itself.
Some parabolas appear stronger (i.e., darker) than others. The left-most parabola is strongest
likely due to the left-most sinter’s position above the clay layer. The clay layer weakens the
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12. signal for the remaining sinters. The right-most sinter has an intermediate signal for there is
less clay above it relative to the sinter in the middle.
The parabola phenomenon is more obvious in figure 16.
Figure 15 was built in steps, which are shown in figures 17, 18, and 19.
5.3.6 Material Properties
Figure 25 displays the exponential decay of velocity as the dielectric constant of a material
increases. It was found that igneous rocks typically yielded higher reflection velocities as op-
posed to sedimentary rocks. This is likely due to sedimentary rocks usually having higher
moisture contents than igneous rocks (sedimentary rocks usually possess fluid flow features).
High moisture contents slow down reflection velocities.
5.3.7 Comparison with Existing Literature
Dr. Bridget Y. Lynne applied GPR methods for the purpose of geochemical bodies derived
from geothermal fluid flows. One of her works, titled “Ground Penetrating Radar and Its Suc-
cessful Application in Imaging USA and New Zealand Siliceous Sinters”, tested whether GPR
devices could detect both trace-element rich sinters (i.e., quartz bodies rich with miscellaneous
minerals) and trace-element poor sinters. Her group color-coded soils (red) and sinters (white)
in their results (figure 26). Lynne reached a conclusion; GPR’s can map both trace-element
poor and trace-element rich sinters and distinguish them among profiles accurately. On the
other hand, the GPR results obtained for this project (figures 9 – 18) confirm the presence of
quartz bodies that are either sinters or have yet to become sinters via diagenesis (diagenesis
is a process in which traveling sediments are compressed by overburden stresses and eventu-
ally formed into sedimentary rocks). Additionally, Dr. Lynne’s group confirmed their findings
with fieldwork, whereas this project included no fieldwork. Nevertheless, unlike Dr. Lynne’s
results, this project’s profiles display aquifers that can potentially act as future EGS flow net-
works. Thus, Dr. Lynne’s piece merely takes an approach to GPR’s geochemical mapping
abilities, whereas this project not only considers geochemical markers, but also flow networks
and depth components of EGS systems.
The Chinese Geological Survey measured seismic velocities across China (figure 27). Ar-
eas with low velocity values (i.e., in red) are areas with either large underground water bodies
or a thinning lithosphere, both of which are favorable characteristics for EGS systems. Al-
though seismic surveying sufficiently maps these areas, additional exploration methods, such
as GPR, would be needed to identify detailed nuances in study areas as well as observe whether
changes occur in a subsurface of interest with time. The seismic survey approach and GPR can
act as complements to each other to decide study areas and potential drilling sites.
Provenance studies (i.e., the study of how rocks and minerals change between a starting
point and an ending point in time and space) performed by Yuan et al., 2009, on Xinjiang’s
Tianshan Mountains present an approach to finding and characterizing quartz bodies in eastern
China (figure 28). GPR and provenance studies can mutually benefit one another in their re-
spective fields. Additionally, given that the Tianshan provenance graphs showcase a plethora of
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13. quartzo-feldspathic (i.e., quartz and feldspar composition of rocks) origins with strong paleo-
flows (i.e., high energy flows of high temperatures), the quartz bodies found in east China’s
floodplains may be geochemical markers derived by a geothermal environment. In general,
provenance studies are rarely used in the geothermal exploration context. If combined with
economic geology in the tectonic context (figure 29), provenance studies can be valuable for
geothermal explorers.
6 Conclusions
6.1 Summary of Research Findings
First, GPR can be used in conjunction with geochemical markers (e.g., quartz sinters), soil
roughness (due to water content correlation) and flow-network mapping to search for geother-
mal reservoirs at relatively cheap rates. Second, there are spatial correlations between sinter
occurrence and geothermal occurrence (figure 30). Finally, by using this set of methods on
data from Suiyuan Residential District, it was discovered that Beijing can host EGS systems.
6.2 Major Milestones Achieved
• Imported and processed a plethora of raw GPR profiles from the Suiyuan Residential
District.
• Created tens of models to compare and contrast processed profiles with quantities and
identities set in the models.
• Identified anomalies in all GPR profiles.
• Integrated readings on sinters with readings about subduction and relative dielectric per-
mittivity (a.k.a the relative dielectric constant).
• Discovered and used a host of literature on the tectonics of China. The tectonic frame-
work of Northeastern Beijing supports the conclusions and EGS possibilities mentioned
in this project.
• Associated tectonics of sedimentary basins with tectonics of geothermal reservoirs. Sed-
imentary basins host a wide array of reservoirs (petroleum, natural gas, minerals, metals,
etc.).
• Converted time-distance profiles to depth-distance profiles following implementation of
general and unique 1-D velocity matrices.
• Discovered relationships between soil effective stress, GPR data, and geothermal reser-
voir networks.
• Discovered from geotechnical readings that low pore water pressures can lead to high
effective stresses and thus higher soil stabilities that make soils easier to map with GPR
equipment.
• Realized the frontiers of drilling and mapping technologies related to the geothermal
industry (e.g., Potter Drilling).
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14. • Successfully contacted a scientist (Dr. Bridget Y. Lynne) who is pioneering research on
GPR’s relationship with geothermal reservoir exploration. Her research group is the only
group who has published material similar to this project’s topic.
• Created a step-by-step guide to general GPR processing for MATGPR software (please
contact me via joduttbasrawi@jodutt.com if you would like a copy).
6.3 Difficulties Encountered
• As time passed, the frequency of interaction between me, Professor Huang, and Dr. Li
Zhanhui decreased dramatically. This led to me depending on lab-mates for feedback
and academic inquiries.
• There is, in general, a lack of research behind GPR’s roles in the geothermal industry. Dr.
Bridget Y. Lynne was the only established scientist I found who is working on a topic
similar to this project’s topic. I thus had to embark on interdisciplinary research that
involved me making some inferences that have not been fully tested in a lab or at another
professional setting. I may pursue this research project in another setting in which I can
obtain more feedback, conduct fieldwork, and subsequently publish confirmed findings
in a journal.
• I was discouraged from visiting the land area where the GPR profiles were taken. It can
only be reached by a car that can handle unpaved roads. Since I was not able to visit the
site, I was unable to obtain my own pieces of geologic evidence and confirm some of my
inferences.
• Lack of borehole data prevented me from using bh tomo to its full potential in this
project. Also, without borehole data, I was unable to extrapolate the geologic geometries
of the study area, which ultimately provided me with mere “snippets” of the study area.
• I oftentimes needed a lot of feedback about my work. Today, much of my requests for
feedback have gone unanswered, though such requests may become futile as I continue
to pursue this project’s topic.
6.4 Lab Experience
Days in the lab were routine. I arrived my office between 9am-11am and either worked on
reports, readings, and/or programming. I would then have lunch with lab-mates between
11:30am to 12:00pm, followed by more afternoon work, readings, videos, emails, and pro-
grams. Occasionally, I would play cards, play video games, and chat with the lab-mates in the
afternoons and evenings. Dinner time was between 5:30pm to 6:00pm. On some days, I would
either go back to the hotel to continue work, stay in the office to continue work, or go out for
the evening with friends. Overall, my experience at the lab is likely to be different from the
experience of others in the program (particularly the life-science based labs). My lab did not
include any testing on materials, any sort of chemistry equipment, and any sort of machinery.
My lab was the office outfitted with powerful desktop computers.
6.5 Social Experience
In order to learn the most about living in China, one has to be proactive and pop his/her own
comfort bubble. Most of the Chinese I know stems from my own studies and my own exposure
14
15. to China. I embarked alone on my own journeys to many corners of the China. Unfortunately,
the rest of my cohort decided not to tag along, which developed my arms-length relationship
with the cohort. The cohort, regardless, always plans to do things together on many days.
WeChat was used often to arrange plans, such as movie nights, hang outs, tour plans, and
walks around the city.
With my lab-mates, I played badminton (which was first introduced to me by my grand-
parents in Syria, and it is a favorite sport among my lab) and tried a popular card game (“Wu
Shi K” (5,10,K)) that most of the lab-mates play. In return, I introduced the lab-mates to card
games like KENT and Texas Hold’em (no gambling was performed, of course).
I played snooker with lab-mates, played online snooker against other PKU students, played
China’s most popular video game (League of Legends), and played at an internet cafe. All of
these plays were done with intentions to learn more about life in Beijing and China.
I reconnected with Chinese friends that I had met in California before I came to Beijing. I
also made new friends (thanks, WeChat) through badminton, snooker, and train-rides.
I wish the UCLA cohort was able to experience some of the same social experiences I had
with Chinese students and Chinese residents. I was able to hang out with the cohort many
times; however, my objectives to expose myself to Chinese social lifestyles partially prevented
me from spending most of my time with the UCLA cohort.
7 Figure Captions
Figure 1: An example of a geothermal reservoir with a ready supply of hot water stemming
from rainwater.
Figure 2: A typical turbine encased in a generator that generates electricity as a result of fluid
(e.g., water) flow.
Figure 3: A water wheel that rotates and generates electricity as a result of encountering water
flow.
Figure 4: The cost of power (megawatt per hour) for a variety of energy sources. Geothermal
power can fall in commercial (and thus competitive) price ranges.
Figure 5: An example of a geologic cross section that outlines flow vectors that may be of use
for geothermal prospecting.
Figure 6: A geochemical map of China (mapping of silver in parts per billion).
Figure 7: A table of subsurface materials and their associated dielectric constant(s).
Figure 8: Location, marked with 001, from which GPR data was obtained. The blue path is
the walking path from Peking University to the GPR location (about 26 kilometers).
Figure 9: A raw GPR profile under a jet color scheme (jet color schemes color-code portions
of the profile based on reflection velocities).
Figure 10: A normal fault present in a raw GPR profile.
Figure 11: Heat being driven to the surface as a result of an expanding lithosphere.
Figure 12: Maxwell’s equations, which are the governing equations for the electromagnetic
waves disseminated by GPR devices.
Figure 13: A processed GPR profile that possibly includes anomalies and a simple flow-
15
16. network (i.e., the wet clay layer). Note that the profile is in time vs. horizontal distance
(i.e., scan axis).
Figure 14: A split-step model based off of figure 13, which confirms likelihoods of the pres-
ence of flow-networks and rock anomalies in the study area.
Figure 15: The components of the model in figure 14. Orange is wet clay layer, and the circles
are sinters of slightly varying dielectric constants (blue = 4, dark pink = 4.5, light pink = 5).
Figure 16: 8 circular anomalies create 8 parabolas.
Figure 17: A first step that includes a clay aquifer similar to the one used in figure 15.
Figure 18: A second step that includes a sinter similar to the one used in figure 15 on its left-
hand side.
Figure 19: A third step that includes two of the right sinters from 15.
Figure 20: A graph of soil surface height versus horizontal distance (i.e., soil roughness),
where lower surface height shows lower moisture content (given the dB readings, where high
dB readings imply low water/moisture content). Taken from Lambot et al., 2006.
Figure 21: A processed GPR profile following a centroid-frequency calculation of the entire
profile’s frequencies. Blue spots are spots of higher moisture contents relative to red spots.
Intermediate colors of blue and red are areas of intermediate moisture contents.
Figure 22: A processed GPR profile that shows a normal offset near its center. The profile
displays depth instead of travel time. Additionally, 1 meter is about 50 traces (compare this
figure to figure 13 to see differences in axes).
Figure 23: A model of a trench with two pipes as anomalies (the pipes could pose as enhanced
geothermal flow pipes). This model also can be compared to basins present in the study area.
Figure 24: A model of granite bedrock with rough soil overlying it. Soil roughness can be
used to estimate subsurface water contents.
Figure 25: A graph of material reflection velocities versus material dielectric constants.
Figure 26: A GPR profile of a sinter with overlying soil horizon. Color-coded based on dielec-
tric constant.
Figure 27: Seismic velocities throughout China. Low velocity implies a thin lithosphere or a
lithosphere with high water contents, both of which are useful characteristics for future EGS
systems.
Figure 28: Provenance strata of sedimentary rocks from the Tianshan Mountains in Xinjiang.
These strata are likely where sinters in Eastern China come from (i.e., what they were origi-
nally before being eroded).
Figure 29: Simplified tectonic landscape of Beijing and northeastern China, which shows the
complex configurations that are likely thinning the lithosphere in Northeastern China.
Figure 30: A satellite map of China displaying sinter locations, geothermal locations, and the
movement of rocks/tectonic plates relative to Beijing.
16
35. 9 References
1. Becker. Beckers Statement. California State University, Long Beach, January 2015.
web.csulb.edu/sites/newsatthebeach/2015/01/1-million-in-support-for-geothermal-energy-
research/.
2. Brzozowski, Olivier. Non-Contact Drilling Technology for Geothermal Wells. Industrial
presented at the Potter Drilling Inc., Redwood City, CA, 2011. http://www.energy.ca.gov/research/notices/
2012-02- 29 workshop/presentations/Geothermal/Potter Drilling Presentation.pdf.
3. Clayton, Rob. Notes on Ground Penetrating Radar. Rob Clayton, 2016. Distributed by
Lingsen Meng.
4. Ingersoll, R.V. (1988) Tectonics of sedimentary basins. Geological Society of America
Bulletin, 100, 17041719.
5. Irving, James. Numerical Modeling of Ground-Penetrating Radar in 2-D. PowerPoint
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Dr. Li Zhanhui.
6. Jeanloz, R., and H. Stone. Enhanced Geothermal Systems. Technical. Department of
Energy Research on Geothermal. 7515 Colshire Drive, McLean, Virginia 22102: US De-
partment of Energy: Energy Efficiency and Renewable Energy, December 2013. DOE.
http://www1.eere.energy.gov/geothermal/pdfs/jason.final.pdf.
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the Inversion of offGround Monostatic GPR Signal for Noninvasive Quantification of
Soil Properties. Water Resour. Res., Water Resources, 42, no. W03403 (2006): 42.
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9. Liu, Lanbo. Borehole Radar Imaging Techniques for Subsurface Fracture Detection.
PowerPoint, Xian Shiyou University, July 17, 2015. Distributed by Dr. Li Zhanhui.
10. Lynne, Bridget Y. Exploration Techniques. PDF presented at the Geothermal Institute
Meeting, Santiago de Chile, May 26, 2014. www.irena.org/DocumentDownloads/events/2014/June/
TechnicalTraining/14 Lynne.pdf.
11. Lynne, Bridget Y., and Cheng Yii Sim. Ground Penetrating Radar and Its Successful Ap-
plication in Imaging USA and New Zealand Siliceous Sinters. New Zealand Geothermal
Workshop 2012 Proceedings 1, no. 1 (November 19, 2012): 18.
12. Pinard, H et al. 2D Frequency-Domain Full-Waveform Inversion of GPR Data: Per-
mittivity and Conductivity Imaging. Laboratoire Jean Kuntzmann (LJK), Institut Des
Sciences de La Terre (ISTerre), GrenobleAlpes Univ., CNRS, France 1, no. 1 (2015):
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13. SIG. SIG Uses Geothermal Energy in Geneva to Supply Energy to the GeniLac and GLN
Projects. SIG Utilities, 2016. www.sig-ge.ch/en/about-sig/energy/renewable-energy/geothermal-
energy.
14. T&A Survey. Ground Penetrating Radar (T&A Survey). T&A Survey Group, 2015.
http://www.tasurvey.nl/page.php?id=324&lang=EN. Tzanis, Andreas. MATGPR Man-
ual. Manual and Technical Reference. University of Athens, March 2016.
35
36. 15. Yuan et al., 2009. Age and Provenance of Loess deposits on the Northern Flank of
Tianshan Mountain. ACTA Geologica Sinica, 83: 648-654
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from oceanic subduction zones to collisional orogens. Science China: Earth Sciences,
58: 1045-1069, doi: 10.1007/s11430-015- 5097-3
17. Zhu, Lieyuan, and Peter Joeston. Borehole-Radar Logging at Beach Hall Backyard.
PowerPoint, Beach Hall, June 2005. Distributed by Dr. Li Zhanhui.
18. Zhu, Weiqiang. Progress in FWI. PowerPoint, Beijing, China, October 30, 2015. Dis-
tributed by Dr. Li Zhanhui.
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