Ground Penetrating Radar (GPR) has the ability to map subsurface geological structures and detect variations in moisture that could help understand geothermal exploration. However, GPR is limited to shallow depths of less than 50 meters, where most geothermal reservoirs are located. This study uses GPR data from Beijing to create digital models of the subsurface and identify potential geothermal indicators like quartz sinters. The results demonstrate GPR's capability to detect geochemical markers associated with geothermal activity and map prospective geothermal reservoir locations. While GPR has limitations for deep exploration, it shows potential as a new efficient tool for initial geothermal prospecting.

1. Ground Penetrating Radar in the Context
of Geothermal Reservoir Exploration
Jodutt Basrawi
University of California - Los Angeles, Department of Earth, Planetary, and
Space Sciences; Peking University, Department of Geophysics
joduttbasrawi@jodutt.com — +1 (949) 332-0077
Abstract
Ground Penetrating Radar (GPR) has had nascent interactions with
geophysical exploration methods in the context of geothermal energy.
With GPR’s ability to map the lateral and vertical extent of geologic
bodies, it can be used as a means to prospecting geothermal reservoirs.
The problems associated with GPR usage for geothermal exploration
include GPR’s inability to map beyond the shallow subsurface (i.e., less
than 50 meters), which is where most prospective geothermal reser-
voirs are located. Another GPR problem lies in the need to balance
frequency (which is directly proportional to image clarity) and the ex-
tent of depth (higher frequency entails lower extents of depth for craft-
ing images) in GPR operation. This research aims to display GPR’s
capabilities in scouting geothermal energy sources within any given
landmass through the use of digital models, raw data associated with
subsurface bodies, and GPR relationships with a given area’s geochem-
istry (e.g., sinters). What geoscientists know of GPR is its capability to
characterize subsurface flow networks, map rock fractures, and detect
variances in moisture content, all of which contribute to understanding
geothermal exploration phases. Additionally, data from GPR, such as
permeability values, can be compared with MATLAB created models
in order to relate electromagnetic waves with permeability values of
heterogeneous bodies. Ultimately, likelihoods exist for GPR to be a
new and efficient geothermal exploration vehicle, notwithstanding its
inability to map deep subsurfaces.
Introduction
Ground Penetrating Radar (GPR) uses radar waves to map the
shallow subsurface (i.e., less than 50 meters). GPR devices dif-
ferentiate subsurface bodies (including soil/rock strata, circular
bodies, and plant material) by calculating the velocity of radar
waves reflected off of subsurface bodies alongside the amount
of time passed since that same wave was released by the GPR
device. GPR devices can also measure the dielectric constant
through the following relationship:
ξr =
c
v
2
(1)
where ξr is relative dielectric constant (unitless), c is the speed
of light (≈ 3.0 ∗ 107 meters/second), and v is the velocity of an
incoming radar wave (meters/second). Many subsurface materi-
als each have their own dielectric constant or range of dielectric
constants as a result of their unique material properties.
Table 1: Table of material dielectic constants and the usual velocity(s) of
reflected radar waves
Geothermal energy is thermal energy that is stored and released
from the earth. Enhanced Geothermal Systems (EGS) are flow
networks that are in contact with hot dry rock (HDR). Fluids that
flow through EGS settings can obtain geothermal energy and
bring that energy to the surface for the purpose of generating
electricity. Geothermal energy is an alternative energy source
that is available 24 hours a day, 7 days a week.
Purpose
The purpose of this project is to qualitatively and quantitatively
outline shortcomings of GPR in geothermal contexts. Addition-
ally, the project will show ways of circumventing these short-
comings and thus render GPR into a potentially useful tool for
the geothermal industry.
Methods
GPR profiles were obtained from the Suiyuan Residential Dis-
trict in Beijing.
Figure 1: Location from which raw data was collected relative to Peking
University
The raw data was then processed via MATGPR software. The
processing steps used for this raw data set are as follows:
1. Set statics, polarity, and topographic corrections
2. Remove low frequency components (i.e., de-wowing)
3. Set a standard automatic gain control (standard AGC) to em-
phasize 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)
4. Remove global background noise (i.e., signals that remain
constant across a GPR profile of interest)
5. Trim the travel-time window to remove unclear signals from
deep depths
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
2-D models (finite-difference time-domain and split-step
model methods provided by MATGPR) were constructed and
made to resemble the processed GPR data as much as possible.
Results: Raw and Processed GPR Profiles
Figure 2: A raw GPR profile showing normal displacement.
Figure 3: A processed GPR profile showing multiple anomalies (the
parabolic lines) and a soil layer with a step on its right half.
Figure 4: Centroid frequency plot showing water contents
2-D Models
Figure 5: Model of figure 2, which includes three quartz sinters as anoma-
lies and a clay aquifer through the middle of the profile. The model points to
the field area possibly possessing sinters, which are geochemical precursors
to geothermal reservoirs. This points to likelihoods of the Suiyuan District
possessing accessible geothermal reservoirs.
Figure 6: A model of a trench with two pipes as anomalies (the pipes could
pose as enhanced geothermal flow pathways).
Discussion & Conclusion
Figure 7: A satellite map of China showing sinter sites and documented hot-
spots useful for geothermal energy extraction.
GPR’s ability to distinguish dielectric constants enables GPR
devices to detect sinters, which are geochemical products of hot
alkali water flow that has been in contact with geothermal sys-
tems. Additionally, flow networks and soil roughness can also be
detected, both of which indicate soil water content in prospective
geothermal sites. With this information, prospective geother-
mal sites can be mapped more easily than before the advent of
GPR’s application to geothermal exploration. Chinese reservoir
locations based on GPR data are oftentimes nearby documented
sinter sites, which points to a correlation between geochemical
markers detectable by GPR and geothermal reservoir locations.
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