Subsurface Imaging Research Articles

The ExoMars 2028 WISDOM antenna assembly: Description and characterization

While ground penetrating radars have been extensively researched on Earth, the high-resolution exploration and imaging of the shallow subsurface of celestial bodies in our solar system is still in its early stages, with only a handful of systems capable of the task.Designing high-resolution radar systems can be a complex task due to the large frequency bandwidth required by the antennas to achieve high vertical resolution. The WISDOM GPR, as part of the 2028 ExoMars mission, is a highly capable and challenging instrument in this context, given its fully-polarimetric setup and mission constraints on the operating environment, robustness, as well as mass and size budget.This paper outlines the development and characterization process of the WISDOM antenna assembly, which can serve as a model for future radar systems. Furthermore, it presents the results of the antenna characterization as the foundation for instrument calibration and optimal radar sounding outcomes.

Optimized survey design for the joint use of direct current resistivity and induced polarization: Monitoring of DNAPL source zone evolution at a virtual field site.

The combined application of direct current (DC) resistivity and induced polarization (IP) methods, referred to as combined DCIP method, has gained popularity for characterizing the critical zone dynamic processes such as dense non-aqueous phase liquids (DNAPLs) spreading at contaminated sites. Large-scale DCIP surveys typically require considerable durations, necessitating optimized survey designs to enhance survey resolution while controlling time and labor costs. However, to date, approaches to optimize geoelectrical survey design have focused solely on DC applications, and the efficiency of optimized survey designs for combined DCIP is yet to be investigated. Moreover, as subsurface heterogeneity would impact the geophysical observations, most field-scale numerical DCIP studies have still been conducted at artificial sites that lacked realistic aquifer heterogeneity, which could affect the validity of the DCIP survey evaluations. In this work, a virtual geoenvironmental field site based on high-resolution real aquifer analog was created to simulate a DNAPL evolution scenario with simultaneous monitoring by DCIP survey, employing both the optimized survey design and popular non-optimized survey designs (Wenner, Wenner-Schlumberger, Dipole-Dipole arrays). Results show that the optimized survey with prior information improves the monitoring accuracy of DNAPL source zone (SZ) by 8 to 19% with respect to different DCIP characteristics (conductivity, chargeability, normalized chargeability, and relaxation time). Another ideal numerical test indicates that the optimized survey shows up to an 83% reduction in measurement time compared to the conventional survey, while maintaining the same subsurface image resolution. Additionally, the optimized surveys designed without or with limited prior information were also shown to be more efficient than conventional survey for imaging the entire subsurface space. The findings in this study highlight the immense potential of optimized survey design methods for enhancing the efficiency of DCIP surveys on subsurface contaminants and hydrological processes.

Advanced ERT techniques for methane potential evaluation in controlled dump sites: A forward modeling approach

Methane (CH4) emissions from large controlled dump sites play a significant role in global warming. However, when captured efficiently, CH4 is a valuable energy resource. This study optimizes electrical resistivity tomography (ERT) for improved CH4 detection in controlled dump sites, focusing on refining electrode array configurations. We assessed three configurations: dipole-dipole, Wenner-Schlumberger (WS), and Wenner arrays. The results were compared with synthetic data simulated using the forward modeling technique, and the model's accuracy was evaluated using the Nash-Sutcliffe model efficiency coefficient (NSE), model sensitivity, and root mean square error (RMSE). Additionally, we analyzed the model's correlation with CH4 flux measurements. The WS array demonstrated the best performance, achieving a lower RMSE (23.4 %), a higher NES (0.90), and model sensitivity (0.90), along with a moderate negative correlation (-0.50) with CH4 flux measurements. This configuration effectively identified subsurface features, such as organic waste layers and leachate zones, which are crucial for CH₄ capture and landfill gas collection. Forward modeling confirmed the WS array's ability to provide high-resolution subsurface imaging, enhancing CH₄ hotspot detection. By improving the spatial and temporal accuracy of CH₄ detection, this approach addresses limitations in traditional methods and supports the design of more efficient gas capture systems. Applied in Thailand, these findings highlight the potential of optimized ERT techniques for enhancing CH₄ recovery in waste-to-energy systems, contributing to more sustainable waste management practices and reducing greenhouse gas emissions.

Elastic least‐squares reverse time migration from topography through anisotropic tensorial elastodynamics

AbstractLeast‐squares reverse time migration is an increasingly popular technique for subsurface imaging, especially in the presence of complex geological structures. However, elastic least‐squares reverse time migration algorithms face significant practical and numerical challenges when migrating multi‐component seismic data acquired from irregular topography. Many associated issues can be avoided by abandoning the Cartesian coordinate system and migrating the data to a generalized topographic coordinate system conformal to surface topology. We introduce a generalized anisotropic elastic least‐squares reverse time migration methodology that uses the numerical solutions of tensorial elastodynamics for propagating wavefields in computational domains influenced by free‐surface topography. We define a coordinate mapping assuming unstretched vertically translated meshes that transform an irregular physical domain to a regular computational domain on which calculating numerical elastodynamics solutions is straightforward. This allows us to obtain numerical solutions of forward and adjoint elastodynamics and generate subsurface images directly in topographic coordinates using a tensorial energy‐norm imaging condition. Numerical examples demonstrate that the proposed generalized elastic least‐squares reverse time migration algorithm is suitable for generating high‐quality images with reduced artefacts and better balanced reflectivity that can accurately explain observed data acquired from topography in a medium characterized by arbitrary heterogeneity and anisotropy. Finally, the computational cost of our method is comparable to that of an equivalent Cartesian elastic least‐squares reverse time migration numerical implementation.

Site selection for Proposed Construction Using the Electrical Resistivity Method and Some Geotechnical Tests of Soil At Al-Nahrawan Area, Baghdad, Iraq

The study site represents an investigation of a proposed construction site in Baghdad City, in the Al-Nahrawan area, using 2D electrical resistivity and some geotechnical tests for soil samples. A Wenner-Schlumberger configuration was used for 4 parallel lines, each line 120 m long, with 1m spacing between electrodes. Interpretation is made by RES2DINV software. The results showed areas with relatively high resistivity caused by mud cracks and low- resistivity caused by water content (wet soil). The groundwater depth ranges from 5.5 to 5.7 m; these values were matched by comparing with nearby wells using a sounder device. Soil samples from different depths ranging from 0.5 to 4 m were analysed, and the samples consisted mainly of clay > 88% with low liquid and plastic limits >50 (USCS). It is classified as CL, which means Low plasticity. The electric method and laboratory tests were effective in identifying the subsurface image.

Seismic Imaging of the Arctic Subsea Permafrost Using a Least-Squares Reverse Time Migration Method

High-resolution seismic imaging allows for the better interpretation of subsurface geological structures. In this study, we employ least-squares reverse time migration (LSRTM) as a seismic imaging method to delineate the subsurface geological structures from the field dataset for understanding the status of Arctic subsea permafrost structures, which is pertinent to global warming issues. The subsea permafrost structures in the Arctic continental shelf, located just below the seafloor at a shallow water depth, have an abnormally high P-wave velocity. These structural conditions create internal multiples and noise in seismic data, making it challenging to perform seismic imaging and construct a seismic P-wave velocity model using conventional methods. LSRTM offers a promising approach by addressing these challenges through linearized inverse problems, aiming to achieve high-resolution, subsurface imaging by optimizing the misfit between the predicted and the observed seismic data. Synthetic experiments, encompassing various subsea permafrost structures and seismic survey configurations, were conducted to investigate the feasibility of LSRTM for imaging the Arctic subsea permafrost from the acquired seismic field dataset, and the possibility of the seismic imaging of the subsea permafrost was confirmed through these synthetic numerical experiments. Furthermore, we applied the LSRTM method to the seismic data acquired in the Canadian Beaufort Sea (CBS) and generated a seismic image depicting the subsea permafrost structures in the Arctic region.

Exploring the dielectric loss of Martian regolith in the frequency domain using Zhurong radar data

Martian regolith is one of the primary science objectives of Mars exploration missions. The Rover Penetrating Radar carried by Zhurong rover allows for high-resolution subsurface imaging and in-situ measurements of Martian regolith dielectric properties, which are crucial to advance our understanding of Martian geology and hydrological evolution. While earlier studies have derived dielectric constants for the shallow subsurface, further characterization of subsurface materials requires the determination of attenuation properties. In this study, we applied the centroid-frequency shift method to explore the attenuation property of the Martian regolith in the frequency domain. Lateral attenuation variation was analyzed in detail by integrating subsurface radargram and navigation terrain images. The results show that, within a depth of ∼4 m, the attenuation of radar signal for Zhurong subsurface material is equal to a loss tangent of 0.0079, with a standard deviation of 0.001. Based on the loss tangent value, dielectric permittivity and ground characterization, we preclude the possibility that the regolith is predominantly igneous materials. The lateral variation of the attenuation property could likely be attributed to changes in the proportion of duricrusts, which are heterogeneously distributed along the rover traverse. Our findings offer valuable information for understanding the Martian regolith and its evolution, serving as a important reference for future Mars sample return missions.

A comparative study of using geophysical methods for imaging subsurface voids of various sizes and at different depths

Subsurface voids pose significant geohazards, underscoring the need for their timely detection in order to mitigate the associated hazard. We report on a field study aimed at the comparative assessment of electrical resistivity tomography (ERT), seismic refraction tomography (SRT), and the multichannel analysis of surface waves (MASW) for void mapping in karstic regions. The field surveys were conducted at a site in central Texas, of typical karstic geomorphology, and involved the co-located deployment of ERT, SRT and MASW arrays. Post-surveying, boreholes were drilled at select locations for verification purposes. It is shown that MASW demonstrated limited ability to resolve voids due to its inherent theoretical limitations. In contrast, ERT revealed high-resistivity air-filled zones, and low-resistivity soil-filled regions, which aligned well with post-survey borehole logs, although deeper voids remained largely undetected. SRT clearly delineated voids through velocity reductions, but smoothing effects overestimated void velocities. Using ERT and SRT jointly provided improved void characterization compared to single-method-based interpretations, with ERT determining void type and SRT delineating boundaries. Despite the relative success of the joint ERT-SRT application, it is evident that without the corroboration provided by invasive testing, definitive void localization and characterization under arbitrary site conditions remains elusive.

Nanoscale subsurface imaging by non-steady-state electron beam-driven scanning thermoelectric capacitance microscopy

Nanoscale subsurface characterization technologies based on the scanning electron microscope platform offer incomparable advantages of nondestructiveness and penetration depths up to the micrometer scale. However, the electron beam can serve not just as a mechanical/electrical excitation source but also as an excellent nanoscale thermal excitation source, which can facilitate the development of nanoscale subsurface imaging methods based on the Seebeck effect in semiconducting materials. In this work, a subsurface nondestructive imaging technology, scanning thermoelectric capacitance microscopy (STeCM), was developed based on the interaction between a non-steady-state electron beam and semiconducting materials, exploiting the Seebeck effect. In STeCM, a square wave-modulated hot electron beam with huge kinetic energy excites a “thermal wave” in the subsurface local region of the semiconducting sample. The heated local region, acting as a thermoelectric capacitor, undergoes cyclic charging and discharging, leading to the generation of periodic current due to non-equilibrium carrier migration. The second-order harmonic component of this current is demodulated to visualize embedded local thermal/thermoelectric inhomogeneities. Amazingly, for STeCM sample, only a smooth or polished surface is required, eliminating the need for any microfabrication, which will effectively decrease the configuration difficulty in the experiment. STeCM offers an alternative subsurface nondestructive imaging technology for more efficient, simple, and robust characterization.

Enhancing Subsurface Imaging: Optimization of Data Acquisition and Processing Parameters in Passive Roadside MASW Surveys

Enhancing Subsurface Imaging: Optimization of Data Acquisition and Processing Parameters in Passive Roadside MASW Surveys

Application of 2-dimensional tomography for subsurface lithology imaging and aquifer determination in Etim Ekpo, Southern Nigeria.

Application of 2-dimensional tomography for subsurface lithology imaging and aquifer determination in Etim Ekpo, Southern Nigeria.

Multiview Multistatic vs. Multimonostatic Three-Dimensional Ground-Penetrating Radar Imaging: A Comparison

The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality and acquisition time. In this paper, we adopt a Born Approximation-based full 3D approach, which can manage multiview-multistatic, multifrequency data and faces the imaging as a linear inverse scattering problem. The inverse problem is solved by exploiting the truncated singular value decomposition as a regularization scheme. The paper presents a theoretical study aimed at assessing how the reconstruction capabilities of the imaging approach depend on the adopted measurement configuration. In detail, the performance achievable in the standard case of multimonostatic, multifrequency data is compared with that provided by a multiview-multistatic, multifrequency configuration, where the data are gathered by considering a progressively increasing number of transmitting antennas. The comparison of the achievable imaging performance is carried out by exploiting the spectral content and the point spread function, which are general tools to foresee the achievable reconstruction capabilities. Reconstruction results related to a numerical experiment based on full-wave data are also provided.

Deep learning based Compton backscatter imaging with scattered X-ray spectrum data: A Monte Carlo study

Compton backscatter imaging (CBI) is a non-destructive testing (NDT) technique with the Compton effect. In CBI, the radiation source and detector are positioned on the same side of the object, which means that the imaging is almost not limited by the size of the object. The characteristic of single-sided imaging gives CBI substantial potential for applications in specific scenarios. In field archaeology, CBI can rapidly acquire subsurface images with the millimeter-level resolution, effectively enhancing archaeological excavation efficiency while better protecting artifacts. Therefore, CBI offers substantial potential in archaeological excavation. To address the problem of severe noise in CBI, we employed a scintillation detector to receive backscattered X-rays. Transformers directly reconstruct multi-channel projections to swiftly and accurately obtain precise reconstruction images. Monte Carlo simulation experiments confirm this method’s suitability for detecting a variety of common archaeological materials. Experimental results indicate that compared with count mode, multi-channel projections can effectively improve the reconstruction quality of CBI. Moreover, CBI can clearly image various materials of buried artifacts, proving the method’s versatility and practical value in NDT field.

Minimum entropy constrained cooperative inversion of electrical resistivity, seismic and magnetic data

Geophysical methods are widely used to gather information about the subsurface as they are non-intrusive and comparably cheap. However, the solution to the geophysical inverse problem is inherently non-unique, which introduces considerable uncertainties. As a partial remedy to this problem, independently acquired geophysical data sets can be jointly inverted to reduce ambiguities in the resulting multi-physical subsurface images. A novel cooperative inversion approach with joint minimum entropy constraints is used to create more consistent multi-physical images with sharper boundaries with respect to the single-method inversions. Here, this approach is implemented in an open-source software and its applicability on electrical resistivity tomography (ERT), seismic refraction tomography (SRT), and magnetic data is investigated. A synthetic 2D ERT and SRT data study is used to demonstrate the approach and to investigate the influence of the governing parameters. The findings showcase the advantage of the joint minimum entropy (JME) stabilizer over separate, conventional smoothness-constrained inversions. The method is then used to analyze field data from Rockeskyller Kopf, Germany. 3D ERT and magnetic data are combined and the results confirm the expected volcanic diatreme structure with improved details. The multi-physical images of both methods are consistent in some regions, as similar boundaries are produced in the resulting models. Because of its sensitivity to hydrologic conditions in the subsurface, observations suggest that the ERT method senses different structures than the magnetic method. These structures in the ERT result do not seem to be enforced on the magnetic susceptibility distribution, showcasing the flexibility of the approach. Both investigations outline the importance of a suitable parameter and reference model selection for the performance of the approach and suggest careful parameter tests prior to the joint inversion. With proper settings, the JME inversion is a promising tool for geophysical imaging, however, this work also identifies some objectives for future studies and additional research to explore and optimize the method.

Shallow Subsurface Imaging Using Challenging Urban DAS Data

Shallow Subsurface Imaging Using Challenging Urban DAS Data

Extracting Multimodal Surface-Wave Dispersion Curves from Ambient Seismic Noise Using High-Resolution Linear Radon Transform

Abstract In the past two decades or so, ambient noise tomography (ANT) has emerged as an established method for imaging subsurface seismic velocity structures. One of the key steps in ANT is to extract surface-wave dispersion curves. The predominant approach for subsurface shear-wave velocity structure inversion involves utilizing fundamental-mode surface waves in ANT. Nevertheless, a notable challenge encountered is the issue of nonuniqueness when employing the dispersion information of fundamental-mode surface waves to invert for shear-wave velocity models. The inclusion of higher-mode dispersion curves in the inversion offers several benefits, including the reduction of nonuniqueness, enhancement of inversion stability, and decreased dependence on the initial model. In this study, we illustrate the applicability of the high-resolution linear radon transform method (HRLRT) for extracting multimodal surface-wave dispersion energy from ambient seismic noise data. We apply HRLRT to both the synthetic noise data and real data recorded by USArray. Our results of applications show that the HRLRT method can extract multimodal surface-wave dispersion information. Compared with established methods such as the frequency–Bessel transform and multicomponent frequency–Bessel transform, the HRLRT exhibits an advantage in suppressing “crossed” artifacts and the second-/third-type artifacts caused by sparse spatial sampling, and the resulting dispersion energy from HRLRT has narrower peaks, meaning high resolution of dispersion curves based on the HRLRT method.

Multi-source wavefield reconstruction of distributed acoustic sensing data using compressive sensing and seismic interferometry.

Seismic data recorded by distributed acoustic sensing (DAS) interrogator units on deployed optical fiber are being used for a variety of subsurface imaging and monitoring investigations. To reduce the costs of active-source DAS surveying applications, seismic interferometry can be applied to estimate inter-sensor wavefields from DAS records. However, recording long-term records for ambient interferometry requires considerable data storage and sections of DAS optical fibers may be unusable because of broadside sensitivity considerations from the DAS fiber orientation and due to localized coherent energy sources with amplitudes significantly larger than the ambient signal of interest. Compressive sensing, a wavefield reconstruction technique, can mitigate the problems of large data storage and unusable data. We apply compressive sensing-based multi-source wavefield reconstruction to estimate correlograms of ambient DAS records from a fiber array in Perth, Australia. The multi-source method uses all available virtual-source gathers for simultaneous wavefield reconstruction and is different from the conventional single-source method that separately reconstructs individual virtual-source gathers. Using the Fourier and curvelet transforms to sparsify interferometric wavefields, we show that multi-source reconstruction is applicable to the DAS data and that the Fourier multi-source reconstruction can improve the recovered wavefields by approximately 5-10 dB, compared to the Fourier and curvelet single-source wavefield reconstructions.

CONTROLLED-SOURCE ELECTROMAGNETIC (CSEM) DATA PROCESSING WITH HIGH ELECTROMAGNETIC NOISE LEVELS

The Controlled-Source Electromagnetic (CSEM) method is one of the electromagnetic methods utilized in geophysical exploration. This method provides a subsurface image through the resistivity anomalies of materials encountered by electromagnetic waves. The research area is located near a major city, resulting in high electromagnetic noise. Electromagnetic noise can be categorized into two types of the noise namely periodic noise and sporadic noise. Eliminating noise is a crucial objective to enhance data quality, as it can introduce uncertainty into interpretations. Three noise removal techniques are employed: pre-stack to filter the harmonic noise, stacking to remove the sporadic noise, and post-stack for smoothing. The CSEM data used consists of signals in the time domain with a 10-second period and a 50% duty cycle. The results of applying these noise removal techniques indicate that all three methods are highly effective in noise reduction. The pre-stack technique can remove periodic noise, while sporadic noise is addressed by the stacking technique, and signal smoothing can be achieved using the poststack technique.

Joint Inversion of DC and TEM Methods for Geological Imaging

In this study, we present a new methodology for 2D joint inversion, or data fusion, of DC electro-resistivity and Transient Electromagnetic methods. These geophysical techniques have traditionally been used separately, but by combining them, we aim to decrease ambiguities and increase the robustness of the resulting subsurface model. The inversion process was conducted using the classical Occam method with smooth models and synthetic studies were also conducted to understand the limitations and advantages of the method. We also applied the algorithm to data obtained in groundwater exploration in Brazil, and the results showed that the 2D joint inversion is promising in increasing accuracy and reducing ambiguity in subsurface imaging.

Advances in rock physics for pore pressure prediction: A comprehensive review and future directions

Advances in rock physics have significantly enhanced pore pressure prediction, a critical aspect of subsurface exploration and drilling operations. This comprehensive review delves into the latest developments in rock physics methodologies, integrating empirical, theoretical, and computational approaches to predict pore pressure more accurately. Traditional pore pressure prediction methods often rely on well log data and seismic attributes, but recent advancements have introduced innovative techniques that leverage the physical properties of rocks to provide more reliable predictions. Key advances include the development of improved rock physics models that better account for the complexities of subsurface environments, such as heterogeneity and anisotropy. These models integrate data from various sources, including well logs, core samples, and seismic surveys, to create a more comprehensive understanding of the subsurface. Additionally, the application of machine learning and artificial intelligence to rock physics has opened new avenues for analyzing large datasets, identifying patterns, and refining predictive models. This review also examines the role of laboratory experiments and field studies in validating and calibrating rock physics models. High-pressure and high-temperature experiments have provided valuable insights into the behavior of rocks under different conditions, which are essential for accurate pore pressure prediction. Field studies, on the other hand, offer real-world data that help in fine-tuning models and methodologies. Future directions in rock physics for pore pressure prediction include the integration of advanced geophysical techniques, such as full-waveform inversion and distributed acoustic sensing, which offer higher resolution data and more detailed subsurface imaging. The use of cloud computing and high-performance computing platforms is also expected to enhance the processing and analysis of large datasets, making predictive models more efficient and scalable. The comprehensive review concludes by highlighting the importance of interdisciplinary collaboration in advancing rock physics methodologies. By combining expertise from geophysics, petrophysics, geomechanics, and data science, the field can continue to innovate and improve the accuracy and reliability of pore pressure predictions, ultimately enhancing exploration and production efficiency in the oil and gas industry. Keywords: Advances, Rock Physics, Pore Pressure, Prediction, Future Directions.