Borehole radar, due to its high resolution and extensive radial detection capability, has become an important geophysical tool for detecting complex subsurface fractures. The key to evaluating fracture detectability lies in accurate fracture modeling and numerical simulation. To overcome the limitations of conventional fracture modeling approaches, including geometric oversimplification, inadequate representation of aperture and fracture surface correlation, and incomplete characterization of roughness, we propose a multi-factor three-dimensional (3D) rough fracture modeling method. This method integrates two-dimensional (2D) image reconstruction with the Weierstrass-Mandelbrot (W-M) fractal function, which enables a comprehensive description of fracture geometry, surface roughness, aperture, and correlation between the surfaces of a fracture. Based on the developed models, full-wave electromagnetic simulations of borehole radar are conducted using the finite-difference time-domain (FDTD) method, and the effects of fracture attitudes on radar responses are systematically investigated. The simulation results indicate that variations in dip angle and dip direction significantly influence the characteristics of the borehole radar signals. Fracture surface roughness is also found to introduce perturbations in echo details. Furthermore, the radar migration imaging results are more conducive to the evaluation of fracture attitude, as systematic simulation analysis demonstrates a high morphological consistency between the radar migration imaging results and the geometric projection of the fracture onto the Borehole-Fracture Coupling Plane (BFCP). This is further confirmed by the centroid offset distance and the Intersection over Union (IoU). In addition, the “dip direction ambiguity” in omnidirectional borehole radar detection is revealed, where fractures symmetric about the BFCP generate highly similar radar responses, thereby increasing the difficulty of interpretation. The presented fracture modeling method and observed response patterns from fractures support the accurate detection of complex fractures using borehole radar.

In electrical resistivity tomography (ERT), the anomaly effects of different electrode arrays vary depending on the geological model. The appropriate combination of different electrode arrays can optimize detection performance and enhance the reliability of interpretation results. However, traditional inversion methods, constrained by single-array data, sparse observations, and ill-posed problem-solving, often yield low-resolution or inaccurate results. To address the resolution challenges in ERT inversion, inspired by the outstanding fusion and nonlinear mapping capabilities of multi-modal deep learning (DL) image methods, we propose the super-resolution ERT fusion network (SRERTF-Net), which utilizes traditional inversion results of multi-array as the initial models, efficiently leveraging and integrating prior physical information to achieve multi-array data joint super-resolution inversion. In SRERTF-Net, different down-sampling paths are employed to process the inversion results of various electrode arrays, while Inception modules are introduced to enhance feature extraction. Additionally, dense connections are implemented both within and across paths to effectively integrate complementary information from different arrays, ensuring robust multi-modal feature fusion. Finally, we designed training samples that include randomly generated typical structural models and comprehensive complex models, in order to enhance the practicality and adaptability of the network. Experiments on synthetic and field measured data indicate that SRERTF-Net outperforms other methods in terms of resistivity accuracy, resolution, and background performance.

Basal roughness is a crucial parameter for quantifying subglacial geomorphological landforms, which offers key insights into glacial geomorphic environments and ice sheet dynamics. Princess Elizabeth Land (PEL) in East Antarctica covers approximately 15% of the Antarctic Ice Sheet. However, to date, understanding the relationship between subglacial geomorphology and ice flow in the PEL has remained limited. In this study, we used airborne ice radar data from the Chinese National Antarctic Research Expedition (CHINARE) during the first five austral seasons and publicly available Antarctica’s Gamburtsev Province (AGAP) Project North data to calculate a two-parameter spectral roughness index of the subglacial topography. We analyzed the relationship between the spatial distribution of basal roughness and the speed and direction of ice velocity, while classifying the regional roughness results into four different combinations. We find that the subglacial environment in the PEL is more intricate than the one previously reported. The area near the polar record glacier (PRG) is characterized by locally rough geomorphology but fast ice flow. The beds in the slow ice flow area of PEL are characterized by both rough and flat landforms. Low-lying basins situated in the interior are of considerable interest because they may be characterized by preglacial active erosional landscapes.

Denoising is a crucial step in ground penetrating radar (GPR) data processing. Conventional denoising algorithms for GPR typically require selecting optimal processing parameters, which can be challenging to achieve in practical applications, resulting in unsatisfactory processing outcomes. In recent years, in order to address the issue of low accuracy in conventional GPR denoising algorithms, denoising neural networks have been applied in the field of GPR. Although conventional denoising neural networks have shown improvements in signal-to-noise ratio (SNR) in some cases, their performance is often inadequate when facing real GPR data with complex random noise, due to the training methods of the networks. To address the challenges in denoising of GPR data, a two-stage denoising method based on deep learning (DL) has been proposed. Initially, conventional GPR data processing is conducted, followed by training a denoising network model using both the processed and unprocessed signals. Leveraging the powerful nonlinear fitting capability of convolutional neural networks (CNNs), an end-to-end mapping relationship is established to obtain the final denoising network model, completing the two-stage denoising process. Finally, this letter validates the proposed two-stage denoising method using synthetic and field data. The radar data obtained through this two-stage denoising method not only improve mean squared error (mse) by 0.17 compared to conventional methods but also increase peak SNR (PSNR) by 8.1. Furthermore, there is a significant enhancement in the integrity of the waveform and the recovery of weak signals.

Traditional cross-hole radar tomography (CRT) usually cannot obtain high-resolution imaging results due to the nonlinearity and multisolution of inversion. To cope with these challenges, we propose a scheme to achieve high-resolution CRT for complex slowness models using deep neural networks (DNNs). Given the inherent difficulty in generating complex geophysical models in batches, by series of processing some remote sensing images from the remote sensing scene classification dataset, we create a real slowness model dataset. Then, we utilize 2-D U-Net to directly construct the mapping relationship between the low-resolution slowness model from the traditional method and the real slowness model. The superiority of our scheme is verified by both synthetic data and measured data. Our scheme can significantly suppress the false anomaly of traditional CRT results and accurately reconstruct the underground target’s geometry, position, and slowness value, and it has excellent accuracy and robustness. In addition, the response data of the slowness model reconstructed by our scheme are closer to the field data.

Noise attenuation is crucial in ground-penetrating radar (GPR) data processing. In recent years, deep learning (DL) methods have shown excellent performance in GPR denoising tasks, but they typically focus only on recovering the target signal, which can lead to over-denoising. To enhance the generalizability and the practicality of denoising networks, we propose a strategy to generate random dielectric models from natural image datasets, which can quickly construct model datasets with low redundancy and reasonable distribution. To enhance the fidelity of GPR denoising, we leverage the powerful nonlinear fitting capabilities of convolutional neural networks (CNNs) and introduce a closed-loop denoising network framework for GPR. The framework consists of a denoising sub-network and a noise extraction sub-network, effectively achieving signal-noise separation in noised GPR data. Specifically, the denoising sub-network is used to recover weak reflection signals and initially remove noise, while the noise extraction sub-network is used to restore the true noise, mitigating the problem of over-denoising. A key innovation of our approach is the integration of bandpass filtering, which enhances the robustness of network training and supports effective weak signal recovery. This network framework forms a closed loop through the residual loss between the signal-noise separation results and the noised GPR data, the closed-loop structure is capable of further refining the signal and noise prediction results of the two subnetworks, thereby enhancing the numerical accuracy of the signal-to-noise separation results. Finally, the effectiveness of the GPR closed-loop denoising network is verified from multiple perspectives using both synthetic and field measured data. The results indicate that our proposed method is more competitive in GPR denoising tasks.

Directional borehole radar (DBR) is a powerful tool for constructing the 3-D morphology of subsurface geological bodies. Direction of arrival (DOA) estimation is a key step in 3-D imaging. However, the existing DOA methods are constrained by factors such as DBR aperture, and can only identify one signal source within a time window. Consequently, when faced with multiple targets, DBR encounters challenges in effectively distinguishing them, especially when their echoes nearly overlap. In addition, the presence of interference waves makes echo overlap more likely to occur. So we propose a 3-D imaging method based on echo separation to solve this problem. This method first separates echoes of different targets through echo separation methods such as correlation method, deconvolution method, τ-ptransformation method, and moving window method. Subsequently, the azimuth is obtained through the multiple signal classification (MUSIC) algorithm, while the depth and radial distance are obtained through the inverse bi-static boundary scattering transform (IBBST), finally achieving the 3-D imaging of multiple geological targets. The effectiveness of the proposed method is demonstrated through synthetic data of both simple and complex fracture models, and its feasibility in practical applications is demonstrated through field data examination. This method improves the detection capability of DBR, opening up new possibilities for accurately mapping subsurface geological features.

The traditional electrical resistivity tomography (ERT) inversion methods typically produce low-resolution imaging results due to the nonlinear and bulk effect of two-dimensional inversion. In this paper, we propose to directly establish the mapping from the geoelectric model of traditional inversion results (input) to the actual geoelectric models (output) through the fully convolutional networks (FCNs), inspired by the robust nonlinear mapping capabilities of deep learning methods. We designed an ERT resolution enhancement network (ERTReNet) based on the prevailing U-Net architecture, which can conduct end-to-end training and enhance the resolution of traditional inversion imaging results. This methodology has been tested on both synthetic and field measured data. Resolution has been improved, and the resistivity value of both target and geological background are closer to the synthetic model comparing to the tradition method. This work aids in improving the accuracy of subsurface target identification in ERT and serves as a guide for more precise ERT inversion in the future.

The emergence of directional borehole radar (DBR) has made it possible to obtain the 3-D morphology of fractures through a single borehole. The use of a uniform circular array (UCA) as the array receiving antenna is an important way to achieve directional detection; however, the direction of arrival (DOA) method and 3-D imaging method with DBR still have limitations in the imaging capability of rough fractures. We, therefore, propose a method of performing migration first and then DOA estimation next, combined with the moving window method (MWM), to achieve multitarget 3-D imaging, where the DOA estimation adopts the idea of applying the multiple signal classification (MUSIC) algorithm directly. We use the multigrid finite difference time domain (FDTD) method for numerical simulation to verify the effectiveness of the proposed 3-D imaging method. Subsequently, the response of DBR to rough fractures was studied, and the results showed that the roughness characteristics of fractures would significantly change the imaging results but also bring more fracture feature information; we can even detect the rough fracture under unfavorable dip angle. The research provides a theoretical basis for the detection of fractures under complex conditions and has practical application value in the future.

The characterization of unconformities in wind-scoured areas of the Antarctic Ice Sheet (AIS) is important for the accurate estimation of Antarctic mass balance and can provide constraints on climate models. Here we apply radar data collected by the Chinese National Antarctic Research Expedition in the vicinity of the Dome A region in East Antarctica to trace six isochronous internal reflecting horizons. We identify 39 unconformities and map englacial stratigraphy 500 m below the ice surface through cross-validation among the data from several austral summer campaigns using different ice-penetrating radars. By estimating erosion time of buried unconformities using a one-dimensional ice flow model along local ice flowlines, we identify persistent wind-scoured zones on the scale of ten thousand years. We identify the earliest truncated climatic record through interpretation of the radar images and find that erosional activity can be dated back to approximately 100,000 years before present; all of the persistent wind scouring behaviors we present here began during the Last Interglacial- Last Glacial (128 ~ 10 ka B.P.). High-resolution records such as this that reveal englacial unconformities and englacial structural characteristics associated with erosion contribute to improved understanding of climate variability and ice sheet dynamics during the Late Pleistocene.

Borehole radar is a powerful tool for detecting subsurface fractures. Fracture modeling and numerical simulation are essential means to study fracture detectability. In this article, we first propose a method to construct a single fracture model, which combines the Baecher disk model and random rough surface and includes features, such as roughness, pinch-out, and irregularity. As the precise description of the fracture raises the requirement for accuracy of the simulation algorithm, the 3-D conformal finite-difference time-domain (CFDTD) method is used in this work. Numerical simulations of a sphere and rough fractures show that the CFDTD has higher calculation accuracy than conventional FDTD. Then, we analyzed how the fractures with different roughness affect the electromagnetic wave response. It is found that as the fracture surface becomes rougher, the wave scattered by it becomes stronger, more fracture contour-related information is obtained, and the fracture morphology is recovered better. Combined with accurate fracture modeling and high-precision numerical simulation, the electromagnetic response characteristics of different forms of fractures are obtained, which provides a basis for the accurate detection and interpretation of fractures in the future.

Directional borehole radar (BR) 3-D imaging is a challenging problem due to its limited observation space within a borehole. We present a linear prediction (LP)-based direction-of-arrival (DOA) estimation and a migration-based 3-D imaging algorithm for the directional BR, which is composed of a transmitting antenna and a uniform circular array including four small receiving antennas. Only the azimuth is estimated, while the elevation and the distance are included in the imaging process. LP is a useful tool for DOA estimation in the radar domain, but a single linear array (LA) has azimuth ambiguity in DOA estimation. The four receiving antennas are treated as two orthogonal uniform LAs. The combination of two arrays removes the ambiguity in DOA estimation. In addition, the moving window and the threshold-based estimation increase the calculation efficiency. According to the estimated azimuth angles, the measured signal at each depth is decomposed to the corresponding directions. Therefore, there is a time-series signal in each direction at each depth. After processing the measured signals at all depths, we obtain a 3-D data set that can be used for horizontal- and radial vertical-section displays. Then, the radial vertical-section data are processed by migration which transforms the time domain signal into a wavefield-based 3-D image. Simulated data from a fracture model are used to validate the algorithm, and the reconstructed image can reveal the real model well. It is concluded that the novel method we propose is an effective one with real-time processing capability.

In recent years, waveform inversion is one of the hot methods because it can provide sub-wavelength images. In the inversion of field data, waveform inversion requires a good estimation of the source wavelet to reach the global convergence. Traditional method is that the source wavelet is added into the inversion as a new unknown parameter and updated with iterations. When the results of inversion are same to the true models, the estimated source wavelet is same to the true source wavelet. The method is effective in the inversion of synthetic data, but it doesn't perform well and need lots of intervention in the field data inversion. In this paper, we realize a source-independent time-domain waveform inversion. A new objective function is based on the convolved wavefields. The observed wavefields are convolved with a reference trace from the modeled wavefields, and then the modeled wavefields are convolved with a reference trace from observed wavefields. In that case, the source wavelets of the field and modeled wavefields are equally convolved with both terms in the objective function, and thus, the effects of the source wavelet are removed. We test the algorithm on layered media with two embedded cylindrical inclusions. Permittivity and conductivity are simultaneously updated. Though the results of permittivity perform much better than the results of conductivity, the overall results are not good enough. This is because we have to do convolution and cross-correlation to compute the gradients. These convolution and cross-correlation operations increase the nonlinearity of the inversion. Therefore, the source-independent waveform inversion requires more accurate initial models.

GPR is famous for its accuracy and portability in local subsurface investigation. Its capability is limited when considering large-scale or dangerous survey. Air-borne GPR characterized with speediness and remote sensing (contactless measurement), can overcome these shortages. Current airborne GPR can be classified into three types. The first suspend conventional commercial GPR antennas and control unit under a helicopter. The second type is fabricated for airborne survey, and has special hardware and system. The third type is synthetic aperture radar with penetrating capability. After summarizing the current airborne GPR, we simulated two models with flat or rough surface, respectively, for air-borne GPR measurement. Both the ground surface and the subsurface can be imaged clearly in two situations. It is concluded that air-borne GPR is a potential tool for subsurface measurement will receive more attention in the future.

We perform a finite-difference time-domain numerical simulation for metal ore detection by borehole radar. The ore-body model is a practical Ni-Cu-Pt one which is a magmatic deposit located in Sudbury, Canada. We design three boreholes along a cross section perpendicular to the geological strike of the formation which is composed of overburden, ore zone, iron formation, peridotite, granite gneiss, and sediments. We analyze the simulated borehole radar profiles and find that some interfaces could be detected. The ore zone is very absorptive to electromagnetic wave. By combined interpretation of the data from several boreholes, the azimuth ambiguity of the borehole radar could be overcome with some a priori geological information, and the geological structure could be delineated clearly.

The dielectric parameters measurement is very important for not only practical applications such as geophysical prospecting, remote sensing, and material study, but also in theory. We choose open-ended coaxial method. The electromagnetic modeling is used to calculate the reflection coefficient. We collected 64 samples in this nickel-cupper mine and the samples are measured and the data is processed. These measured data show optimistic aspect for borehole detection for metal ore-body.

We perform finite difference time domain (FDTD) numerical simulation for metal ore detection by borehole radar. The ore-body model is adopted from a practical Ni-Cu-Pt ore body which is a magmatic deposit located in Sudbury, Canada. We design three boreholes along a cross-section perpendicular to the geological strike of the formation which is composed with overburden, ore zone, iron formation, peridotite, granite-gneiss, and sediments. We analyzed the simulated borehole radar profiles and found that some interfaces could be detected and some could not, also the ore zone is very absorptive to the wave.

Borehole radar is a special mode of ground penetrating radar. It has several distinguished features from surface radar. For examples, by means of borehole access to deep regions below the surface, the radar sonde can be located relatively close to the anomalies or targets to be measured, this results in more precise targets response than surface measurement. The experimental site is located on the top of a granite hill west of Beijing, China. There are a group of boreholes intersected by many fractures. The measured single-hole reflection data are processed and interpreted. The radial detecting range is more than 30 meters at this site. The subsurface fracture distribution can be imaged very clearly. Many fractures can be "seen", and their distance from borehole and their dip angle can be determined. The azimuth determinations for these fractures are possible in some situations. It is concluded that the borehole radar is an effective tool for subsurface imaging.

An electromagnetic logging technique based on borehole radar is introduced in this paper. The tool consists of one transmitter and two receivers, which can be used to cancel the effect of the antenna characteristics by taking the ratio of two receiver signals. Since receiver signals measured in the time domain can be converted into the frequency domain by Fourier transformation, the amplitude ratio and the phase difference between two receiver signals in a wide-frequency band are obtainable. The response of the tool to different formations is investigated, and the algorithm that converts the amplitude and the phase information to the conductivity and the relative permittivity of the surrounding medium is given by a three-dimensional finite-difference time domain. The effect of the borehole on measurement and the response of the tool to a formation interface are simulated and analyzed numerically. The validity of this technique is confirmed by experiment. This technique can be applied to detect physical properties, including the conductivity and the relative permittivity, of the surrounding medium and the locations of the fractures intersecting the borehole.