Ground Penetrating Radar Response Research Articles

The ground penetrating radar response recognition of hidden dam defects using multi-output convolutional neural network

The Ground Penetrating Radar (GPR) data interpretation of dam defects detection by manual process is a heavy task. Although the convolutional neural network (CNN) is applicable to detect road defects with the help of other auxiliary instruments and techniques, it is still a challenge for the dam defect detection by deep learning. To overcome this problem, a multi-output CNN model for GPR response recognition of dam defects is proposed. The training dataset and test dataset are produced from a large number of GPR responses with different types of dam defects forward modeling by GPRMax3.0 software. The results show that the highest recognition accuracy of the training dataset and test dataset is about 98% and 97%, respectively. To verify the effectiveness, the new proposed method is used to recognize the hidden GPR responses of dam defects in the real GPR data. The results show that the types and locations of dam defects in the real GPR data can be recognized and classified automatically, efficiently and accurately.

Influence of the sand-clay ratio of the burial material of forensic targets on ground-penetrating radar (GPR) responses–comparison of dry and rainy season data

This study aims to assess the impact of the sand-clay ratio on the detection of simulated forensic targets resembling human burial evidence using ground-penetrating radar (GPR). To achieve this goal, we established a controlled site consisting of four experimental graves (SEPs), with each SEP representing a distinct burial scenario. The project’s objectives encompass evaluating how varying the clay soil content influences the detection of swine targets using 2D, quasi-3D, and 4D GPR imaging techniques. We observed signal attenuation in the GPR data during the rainy season and after reflection from larger targets, primarily due to the conductive behavior of the soft tissues within the targets. We achieved more precise results in an SEP that was 85% sand and 15% clay material, regardless of seasonality. We obtained better results in the dry season due to the greater penetrability of electromagnetic waves and more explicit reflections. In comparison, we achieved more precise results for SEP03, which was 70% sand and 30% clay material. In conclusion, controlled sites are invaluable tools for geophysical investigations, as they provide a controlled environment where we can meticulously adjust various parameters, leading to a more precise and insightful analysis of the results. By systematically manipulating factors such as the soil composition, target depth, and environmental conditions, researchers can effectively calibrate their instruments and methodologies, enhancing the accuracy and reliability of their findings. Therefore, controlled sites not only facilitate comprehensive data collection but also serve as essential platforms for refining and validating geophysical techniques, ultimately advancing the field of geophysical forensics.

3D geophysical image translated into photorealistic virtual outcrop geology using generative adversarial networks

Outcrop analogues play a pivotal role in resolving meter-scale depositional facies heterogeneity of carbonate strata. Two-dimensional outcrops are insufficient to decipher the 3D heterogeneity of carbonate facies. Near-surface geophysical methods, notably ground-penetrating radar (GPR), can be employed to step into 3D and extend the dimensionality of the outcrops to behind the outcrop. However, interpreting geophysical images requires specific geophysical expertise, often unfamiliar to field geologists who are more familiar with the actual rock than the geophysical data. A novel generative adversarial network (GAN) application is presented that constructs a photorealistic 3D virtual outcrop behind-the-outcrop model. The method combines GPR forward modeling with a conditional generative adversarial network (CGAN) and exploits the apparent similarities between outcrop expressions of lithofacies with their radargram counterparts. We exemplified the methodology and applied it to the open-source GPR data acquired from the Late Oxfordian-Early Kimmeridgian Arabian carbonate outcrop. We interpret a 4 km long outcrop photomosaic from a digital outcrop model (DOM) for its lithofacies, populate the DOM with GPR properties, and forward model the synthetic GPR response of these lithofacies. We pair the synthetic GPR with DOM lithofacies and train them using CGAN. Similarly, we pair the DOM lithofacies with outcrop photos and train them using CGAN. We chain the two trained networks and apply them to construct an approximately 2 km long 2D and an approximately 60 m2 3D volume of photorealistic artificial outcrop model. This model operates in a visual medium familiar to outcrop geologists, providing a complementary instrument to visualize and interpret rock formation instead of geophysical signals. This virtual outcrop replicates the visual character of outcrop-scale lithofacies features, such as the intricate bedding contacts and the outline of reef geobodies.

Numerical modelling of bridge deck reinforcement corrosion based on analysis of GPR data

AbstractThis study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck.

Void Detection Behind Shield Tunnel Segments Using Ground Penetrating Radar: A Laboratory Experiment

As a recognized non-destructive testing (NDT) method, ground penetrating radar (GPR) has been applied for void detection in shield tunnel. However, due to the complex structures of shield segment with double-layer rebar meshes inside, the GPR responses of voids behind shield segment are still not clear. This paper presents a series of laboratory experiments to analyze and characterize the radar responses of voids based on a platform which is composed of two 1:1 scale shield segment. Results preliminarily show that the low-frequency radar signals from the near-segment voids are less obvious than the high-frequency signals. For the voids which are far away from the segment, the radar reflections appear as multi hyperbolas.

Research on GPR Response Recognition of Levee Hazards Based on Convolutional Neural Network

As a key part of water conservancy and flood control, the safe service of levee is a matter of national importance. Since the construction of early levee showing the disease phenomena such as breakage, leakage and hollowness, it has seriously threatened the safety of people’s lives and properties, and became one of the important livelihood problems that need to be solved imminently. In this paper, the network model for automatic classification of levee hazards is constructed using convolutional neural network algorithm. The data set of the ground-penetrating radar responses of different hazards types required for training is constructed by forward modeling. Then, the network model is used to predict the hazards types from the actual measured data of a levee in Jiangxi Province. The results show that the good recognition accuracy and effect of the convolutional neural network model established in this paper.

A framework for GPR‐based water leakage detection by integrating hydromechanical modelling into electromagnetic modelling

AbstractTimely and accurate detection of water pipe leakage is critical to preventing the loss of freshwater and predicting potential hazards induced by the change in underground water conditions, thereby developing mitigation strategies to improve the resilience of pipeline infrastructure. Ground‐penetrating radar (GPR) has been widely applied to investigating ground conditions and detecting pipe leakage. However, due to uncertainties of complex underground environments and time‐lapse change, a proper interpretation of GPR data has been a challenging task. This paper aims to leverage hydromechanical (HM) modelling to predict electromagnetic (EM) responses of water leakage detection in diverse leakage cases. A high‐fidelity 3D digital model of an actual pipeline network, hosting pipes with various sizes and materials, was reconstructed to represent the complex geometry and various mediums. The interoperability between the digital model and the numerical models utilized in HM and EM simulations was improved to better capture the irregular pipelines. Based on Kriging interpolation and the volumetric complex refractive index model, a linking technique was employed to replicate material heterogeneity caused by water intrusion. Thus, a framework was developed to accommodate the interoperability among digital modelling, HM modelling and finite‐difference time‐domain forward modelling. Moreover, sensitivity studies were conducted to evaluate the influences of different time stages, leak positions and pipe types on GPR responses. In GPR B‐scans, the presence of hyperbolic motion and horizontal reflections serve as indicators to estimate the location and scale of water leakage. Specifically, a downward‐shifting hyperbola indicates that the pipeline is submerged by leaked water, whereas the emergence of horizontal reflection is linked to the wetting front of saturated areas. The developed framework can be expanded for complicated applications, such as unknown locations and unforeseen failure modes of pipelines. It will increase the efficiency and accuracy of traditional interpretations of GPR‐based water leakage detection and thus enable automated interpretations by data‐driven methods.

Cement pavement void detection algorithm based on GPR signal and continuous wavelet transform method

The dimension of the void area in pavement is crucial to its structural safety. However, there is no effective method to measure its geometric parameters. To address this issue, a void size extraction algorithm based on the continuous wavelet transform (CWT) method was proposed using ground-penetrating radar (GPR) signal. Firstly, the finite-difference time-domain (FDTD) method was used to investigate the GPR response of void areas with different shapes, sizes, and depths. Next, the GPR signal was processed using the CWT method, and a 3D image based on the CWT result was used to visualize the void area. Based on the differences between the void and normal pavement in the time and frequency domains, the signal with maximum energy from the CWT time–frequency result was extracted and combined to reconstruct the new B-scan image, where void areas have energy concentration phenomenon. Based on this, width and depth of void detection algorithm was proposed to recognize the void area. Finally, the detection algorithm was verified both in numerical model and physical lab model. The results indicated that the CWT time–frequency energy spectrum can be used to enhance the void feature, and the 3D CWT image can clearly visualize the void area with a highlighted energy area. After fully testing and validating in numerical and lab models, our proposed method achieved high accuracy in void width and depth detection, providing a precise method for estimating void dimension in pavement. This method can guide DOT departments to carry out pre-maintenance, thereby ensuring pavement safety.

The suitability of using domestic pigs (Sus spp.) as human proxies in the geophysical detection of clandestine graves.

Research in many forensic science fields commonly uses domestic pigs (Sus spp.) as proxies for human remains, due to their physiological and anatomical similarities, as well as being more readily available. Unfortunately, previous research, especially that which compares the decompositional process, has shown that pigs are not appropriate proxies for humans. To date, there has not been any published research that specifically addresses whether domestic pigs are adequate human proxies for the geophysical detection of clandestine graves. As such, the aim of this paper was to compare the geophysical responses of pig cadavers and human donor graves, in order to determine if pigs can indeed be used as adequate human proxies. To accomplish this, ground penetrating radar (GPR) and electrical resistivity tomography (ERT) responses on single and multiple pig cadaver graves were compared to single and multiple human donor graves, all of which are in known locations within the same geological environment. The results showed that under field conditions, both GPR and ERT were successful at observing human and pig burials, with no obvious differences between the detected geophysical responses. The results also showed that there were no differences in the geophysical responses of those who were clothed and unclothed. The similarity of the responses may reflect that the geophysical techniques can detect graves despite what their contents are. The study implications suggest that experimental studies in other soil and climate conditions can be easily replicated, benefiting law enforcement with missing persons cases.

Synthetic modelling of railway trackbed for improved understanding of ground penetrating radar responses due to varying conditions

AbstractGround penetrating radar (GPR) is a commonly used tool for railway trackbed inspection due to its ability to collect information about subsurface materials at high resolution and high speed. Although GPR recording systems allow for the collection of vast quantities of data (hundreds of kilometres per day), accurate ground truth information is difficult to obtain. Models of trackbed can be used to generate synthetic radargrams to provide a better understanding and predictability of GPR responses to a wide range of trackbed conditions. In this research, we produced models of ballast using randomly shaped 3D particles, with a range of particle size distributions to represent various stages of ballast breakdown. Additionally, void spaces are partially filled with a constant dielectric material to represent ballast contamination. We used gprMax to simulate the GPR response for a 2 GHz horn antenna over the trackbed models. These simulations resulted in radargrams that are visually indistinct from real recorded data in known conditions. These radargrams, along with their formative models, have provided valuable insights into how variations in trackbed conditions can impact GPR data.

Ground penetrating radar applied to subsurface culverts

ABSTRACT The failures of culverts can result in water and soil loss in the surrounding subsurface, leading to collapses. In densely constructed urban areas, buried culverts are inaccessible and invisible, making Ground Penetrating Radar (GPR) inspection a widely adopted nondestructive and efficient method. Previous research has employed artificial intelligence to automate GPR interpretation by identifying special radargram textures, but the irregular GPR responses of degraded subsurface soils present a challenge. Therefore, this study proposes a method for preliminary diagnosing large-scale culverts based on GPR wave attributes. The method comprises three parts: (1) evaluating soil compactness by estimating the tortuosity of GPR responses at the culvert top; (2) investigating soil moisture using the attenuation rate of GPR responses; and (3) overlaying the results of the incompact and moist soils to pinpoint potential degradations. Two long-distance culverts were inspected and diagnosed using the proposed method, and three abnormal sections were identified and confirmed by excavation evaluation. The case studies indicate that the proposed method can quickly provide reference for further in-depth investigation, thereby improving GPR survey efficiency and reducing workload by narrowing the investigation scope. Consequently, this study can facilitate large-scale GPR culvert surveys and further safeguard the water system.

Quality evaluation of ground improvement by deep cement mixing piles via ground-penetrating radar

Deep cement mixing piles are a key technology for treating settlement distress of soft soil subgrade. However, it is very challenging to accurately evaluate the quality of pile construction due to the limitations of pile material, large number of piles and small pile spacing. Here, we propose the idea of transforming defect detection of piles into quality evaluation of ground improvement. Geological models of pile group reinforced subgrade are constructed and their ground-penetrating radar response characteristics are revealed. We have also developed ground-penetrating radar attribute analysis technology and established ground-penetrating radar technical system for evaluating the quality of ground improvement. We further prove that the ground-penetrating radar results integrating single-channel waveform, multi-channel section and attributes can effectively detect the defects and stratum structure after ground improvement. Our research results provide a rapid, efficient and economic technical solution for the quality evaluation of ground improvement in soft soil subgrade reinforcement engineering.

Estimation of the Soil Water Content Using the Early Time Signal of Ground-Penetrating Radar in Heterogeneous Soil

Ground-penetrating radar (GPR) is an important tool for measuring soil water content (SWC) at the field scale. The amplitude analysis of the early time signal (ETS) of GPR may provide a rapid way to estimate SWC. By assuming a homogeneous medium, various studies have been conducted on the relationship between the amplitude of ETS and the topsoil layer’s electromagnetic parameters (dielectric permittivity and conductivity) through numerical simulations, laboratory experiments, and field experiments. Soil is a typical inhomogeneous medium, and soil cultivation is a factor affecting its heterogeneity. In this context, we discuss the ability of the amplitude of ETS to estimate the water content of heterogeneous soil. First, we establish a multi-scale stochastic medium model with the inhomogeneous distribution of dielectric permittivity and conductivity and simulate the GPR response by the finite-difference time-domain (FDTD) method to observe the influence of medium heterogeneity on the GPR response. The heterogeneity of the soil models is evaluated by a geostatistical analysis described by two parameters, correlation length and variability. Then, we analyze the relationship between variability and the average envelope amplitude (AEA) of ETS. A strong soil heterogeneity increases the error of the AEA method in estimating SWC. Finally, the AEA method is used to estimate the SWC of two adjacent fields with different heterogeneities, which were caused by different cultivation methods. The results of the numerical simulation and field experiment indicate that the soil heterogeneity can have an impact on the estimation of SWC using EST, with an error lower than 3% within a depth range of 1/2 λ to λ (wavelength). This suggests that the EST of GPR can be applied to soil layers with relatively large lateral changes in water content.

Crosshole Ground‐Penetrating Radar in Clay‐Rich Quaternary Deposits: Toward Characterization of High‐Loss Media

AbstractKnowing the centimeter‐to meter‐scale distribution of sand in clayey deposits is important for determining the dominating water flow pathways. Borehole information has a high vertical resolution, on the millimeter‐ to centimeter‐scale, but provides poor lateral coverage. For highly heterogeneous deposits, such as glacial diamicts, this detailed borehole information may not be sufficient for creating reliable geological models. Crosshole ground‐penetrating radar (GPR) can provide information on the decimeter‐ to meter‐scale variation between boreholes, but has mainly been used in sandy environments. In this study, we investigate whether crosshole GPR can provide information on the structural relationship of diamicts and their material properties, such as water content, bulk density, and clay content. To achieve ground truth, we compare the crosshole GPR data with geological information from boreholes and an excavation at the field site. The GPR data was analyzed comprehensively using several radar wave attributes in both time‐ and frequency domain, describing the signal velocity, strength, and shape. We found small variations in signal velocity (between 0.06 and 0.07 m/ns) but large variations in both amplitude and shape (either order of magnitude variation or doubling/tripling of attribute values). We see that the GPR response from wetter and more clayey diamicts have both lower amplitudes and lower centroid frequencies than the response from their drier and sandier counterparts. Lastly, we find that the variation in amplitude and shape attributes is better correlated to the diamicts' material properties than the signal velocity.

Effects of Uniaxial Bianisotropic Media on Full-Polarimetric GPR Signatures

The polarimetric response of full-polarimetric ground penetrating radar (FP-GPR) from anisotropic media is a pressing issue to be investigated. It can improve the detection accuracy of the targets in the anisotropic media and can also provide effective ways to detect anisotropic targets. In this paper, we focus on the FP-GPR signals from uniaxial bi-anisotropic media, i.e. the complete case that both the permittivity and the conductivity of the background media are anisotropic. Based on the propagation characteristics of electromagnetic waves in the anisotropic media and the transmission coefficients on the surface of the media, we construct a mathematical relation between the measured scattering matrix affected by anisotropies and the real scattering matrix, derive a coefficient to characterize the polarization rotation(PR) effects, and proposed a diagonal matrix for target decomposition analyses. The computation results indicate that ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>z</i></sub> /ε <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> , σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>z</i></sub> /σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>x</i></sub> , and antenna interval have complex effects on the PR coefficient and the H-Alpha decomposition results of three types of typical targets. Multiple 3D FP-GPR simulations on a series of uniaxial bi-anisotropic models verified the results.

Drone-mounted ground-penetrating radar surveying: Flight-height considerations for diffraction-based velocity analysis

Recent studies highlight the potential of the drone platform for ground-penetrating radar (GPR) surveying. Most guidance for optimizing drone flight heights is based on maximizing the image quality of target responses, but no study yet considers the impact on diffraction traveltimes. Strong GPR velocity contrasts across the air-ground interface introduce significant refraction effects that distort diffraction hyperbolas and introduce errors into diffraction-based velocity analysis. The severity of these errors is explored with synthetic GPR responses, using ray- and finite-difference approaches, and a field GPR data set acquired over a sequence of diffracting features buried up to 1 m depth. Throughout, GPR antennas with 1000 MHz center frequency are raised from the ground to heights &lt;0.9 m (i.e., 0–3 times the wavelength in air). Velocity estimates are within +10% of modeled values (spanning from 0.07 to 0.13 m/ns) if the antenna height is within 1/2 wavelength in air above the ground surface. Greater heights reduce diffraction curvature, damaging velocity precision, and masking diffractions against a background of subhorizontal reflectivity. Field data highlight further problems of the drone-based platform, with data dominated by reverberations in the air gap and reduced spatial resolution of wavelets at target depth. We suggest that a drone-based platform is unsuitable for diffraction-based velocity analysis, and any future drone surveys are benchmarked against ground-coupled data sets.

An Improved GPR Method Based on BP and RPCA for Tunnel Lining Defects Detection and Its Application in Qiyue Mountain Tunnel, China

Tunnel lining defects are one of the most common problems that tunnels experience during operation, and they can pose severe safety risks. The most popular nondestructive testing method for detecting tunnel lining defects is ground penetrating radar (GPR), one of the basic geophysical applications. However, detection responses might differ significantly from the real shape of tunnel lining defects, making it challenging to identify and interpret. When data quality is poor, interpretation and identification become more challenging, resulting in a high cost of tunnel repairs. The improved back projection (BP) imaging and robust principal component analysis (RPCA) are used in this work to offer a GPR data processing method. Even in the event of poor data quality, our method could recover GPR responses, allowing the shapes and locations of tunnel lining flaws to be clearly depicted. With BP imaging, this approach recovers the tunnel defects’ responses to better forms and positions, and with RPCA, it further isolates the target imaging from clutters. Several synthetic data demonstrate that the approach presented in this work may successfully repair and extract the positions and forms of lining defects, making them easier to identify and comprehend. Furthermore, our technique was used to GPR data gathered from the Qiyue Mountain Tunnel in China, yielding more accurate findings than the traditional method, which was validated by the actual scenario to illustrate the efficiency of our method on real data.

Exploration of the Factors Affecting Ground Penetrating Radar Response in Bridge Inspection Applications

Ground Penetrating Radar (GPR) is a nondestructive evaluation technique that is recently employed in various civil engineering applications such as concrete quality evaluations, locating reinforcing rebar, and bridge deck integrity assessments. Previous research has shown that the chloride and moisture content of concrete, corrosion state, and depth of reflecting objects (e.g., steel reinforcing bars) have some impact on the strength of GPR signals. What is not yet clear, however, is to what extent these factors can affect GPR response. This research has sought to quantitatively determine the effects of these factors on the attenuation and the resulting response of GPR signals reflected from reinforcing bars in concrete. The effects of these factors were experimentally demonstrated by analyzing data from four reinforced concrete slabs. Analysis of the processed data showed that attenuation was highly attributed to the depth of the reinforcing bars as compared to the concrete chloride and moisture content, where the amplitude of the reflected signals decreased by 4.5dB for every 25 mm increase in depth as compared to about 1.0 dB reduction in signal amplitude due to the presence of chloride and moisture. Corrosion of reinforcement bars, on the other hand, slightly increased the amplitude of the reflected signals by 1.0 dB.

Ground Penetrating Radar as a Functional Tool to Outline the Presence of Buried Waste: A Case Study in South Italy

The ability of the ground penetrating radar (GPR) method as a rapid preliminary survey to detect the presence of illegally buried waste is presented in this paper. The test site is located in the countryside of “Sannicandro di Bari” (Southern Italy) and has a surface area of 1500 m2. A total of five parallel profiles were acquired in 2014 using a geophysical survey system instrument (GSSI) equipped with 400 and 200 MHz antennae in the monostatic configuration. Two of the five profiles were registered in a control area to compare a natural condition to a suspected waste buried zone. As a result of a processing and elaboration workflow, GPR investigations allowed us to interpret the signal qualitatively within a maximum depth of about 3 m, identifying many signal anomalies, whose characteristics can be considered typical of buried waste. The GPR response of the three profiles acquired in the suspected area showed substantial differences not found in the control’s profiles. Anomalies related to the presence of intense scattering, of dome structures not attributable to cavities, but rather to a flattening and compacting of different layers, therefore, less electrically conductive, were identified in the suspected area. The interpretation of the results obtained by the GPR profiles was confirmed by excavations carried out with bulldozers. Large quantities of solid waste illegally buried (e.g., waste deriving from construction and demolition activities, bituminous mixtures, discarded tires, glass, plastic, municipal waste) were revealed in all the sites where anomalies and non-conformities appeared compared to the control natural soil.

Characterization of sub-seismic resolution structural diagenetic heterogeneities in porous sandstones: Combining ground-penetrating radar profiles with geomechanical and petrophysical in situ measurements (Northern Apennines, Italy)

Deformation bands and structurally-related diagenetic heterogeneities, here named Structural Diagenetic Heterogeneities (SDH), have been recognized to affect subsurface fluid flow on a range of scales and potentially promoting reservoir compartmentalization, influencing flow buffering, and sealing during production. Their impact on reservoir hydraulic properties depends on many factors, such as their permeability contrast with respect to the undeformed reservoir rock, their anisotropy, thickness, geometry as well as their physical connectivity and arrangement in the subsurface. We used the Ground Penetrating Radar (GPR) for detection and analysis of the assemblage of deformation bands – carbonate nodules, in high-porosity arkose sandstone in Northern Apennines of Italy. 2D GPR surveys allowed the description of the SDH spatial organization, their geometry, and their continuity in the subsurface. Petrophysical (air-permeability) and mechanical (uniaxial compressive strength) properties of host rock, deformation bands, and calcite-cement nodules were evaluated along a 30-m thick stratigraphic log to characterize the permeability and strength variations of those features. The assemblage “deformation bands – nodules” degrade porosity and permeability and produce a strengthening effect of the rock volume, imparting a strong mechanical and petrophysical heterogeneity to the pristine rock. Different textural, petrophysical, and geomechanical properties between deformation bands, nodules, and host rock result in different GPR response (permittivity). Thus, GPR response could be used to extend outcrop data (petrophysical and geomechanical) of SDH to 3D subsurface volumes in a way to reconstruct realistic and detailed outcrop analogs of faulted aquifers and reservoirs in porous sandstones. • GPR effectively detects deformation bands and diagenetic heterogeneities in sandstones. • Deformation bands and cement nodules show high compressive strength and low permeabilities. • GPR response correlates with petrophysical and geomechanical rock properties. • An improved reservoir analog is obtained from integrating outcrop-based and GPR data. • Deformation bands and nodules impart anisotropy to the host rock.