Subsurface Imaging Research Articles

A New B-Scan Interpretation Model in Complex Environments

In the scenario where the echo hyperbolas of different targets intersect or the opening structure of the hyperbola is destroyed, the current B-scan image interpretation model has the problems of missing detection and unabling to make full use of the hyperbolic information. The hyperbola needs to be identified manually or by utilizing neural network methods. The recognition accuracy is difficult to guarantee. In order to solve the above problem, a new Ground-Penetrating Radar (GPR) B-scan interpretation model based on hyperbolic trend is proposed in this paper. The model consists of four parts, including preprocessing method, hyperbolic trend clustering algorithm (HTCA), restricted algebraic distance fitting and cluster center algorithm (RADFCC) and Hierarchical Filtered Back Projection (HFBP). Firstly, the GPR B-scan image is converted into a binarized image based on the ratio of the main lobe and side lobe of the echo. Secondly, the points in the binarized image are classified into hyperbolic column-segment clusters based on the hyperbolic trend, and further denoised according to the main lobe width of the ricker wave. Thirdly, by analyzing the characteristics of the GPR in the ground coupling mode, the restricted hyperbola fitting algorithm is used to fit the classified hyperbola column-segment clusters to speed up the convergence of the fit and avoid the result from falling into a local optimum. Fourthly, the target’s location is preliminarily estimated by utilizing the parameters of the hyperbola and the cluster center is established through the location and radius of the target to obtain the accurate estimation of the target and the dielectric constant of the underground. Finally, an imaging algorithm is applied to invert the subsurface image based on the dielectric constant of the subsurface medium. The effectiveness of the proposed model has been demonstrated on both simulated and real datasets.

A Subspace Projection Approach for Clutter Mitigation in Holographic Subsurface Imaging

The holographic subsurface radar (HSR) is recognized as an effective remote sensing modality for the detection of shallowly buried objects with a high-resolution image in plain view. However, subsurface detection with HSR is prone to be impaired by clutter contamination, which often obscures the target response. In this letter, a novel clutter mitigation method combining singular value decomposition (SVD) and response cross correlation analysis is presented. The proposed method first applies SVD to decompose the radar data matrix to a number of singular components. Furthermore, the signal cross correlation characteristics are analyzed to demonstrate that the variance of left singular vectors is directly proportional to the target proportion in radar data. Then, target and clutter subspaces can be identified by maximizing the defined weighted target-to-clutter ratio (WTCR). Results of numerical simulation and laboratory experiments corroborate the effectiveness of the proposed method in reducing clutter while preserving the target image.

3-D Q-Compensated Image-Domain Least-Squares Reverse Time Migration Through Point Spread Functions

Least-squares reverse time migration (LSRTM) has the potential to retrieve a high-resolution subsurface image. However, the standard acoustic LSRTM approach may produce a blurred image, if directly applying it to attenuated seismic recordings. In this letter, we developed a novel 3D Q-compensated image-domain LSRTM approach, denoted as Q-IDLSRTM. The Hessian matrix in the proposed approach is efficiently estimated from the point spread functions (PSFs) which are calculated by a combination of viscoacoustic Born modeling and reverse time migration (RTM) based on the generalized standard linear solid (GSLS) wave equation. The major advantage of the proposed image-domain inversion is that it is much faster than data-domain inversion. The L1 norm constraint and total variation (TV) regularization are used to produce a sparse solution and maintain the structural continuity of the inverted image. We determine the effectiveness of the proposed approach with a part of the 3D Overthrust model and the resulting images demonstrate the ability of our approach to image subsurface structures with enhanced resolution and balanced amplitude relative to the RTM image and inverted image from the acoustic image-domain LSRTM approach.

Structure-Oriented Mapping of the Subsalt Fractured Reservoir by Reflection Layer Tomography From a Perspective of the Zero-Offset Vertical Seismic Profiling

The pre-/sub-salt fractured networks provide key clues to understanding the tectonic history and the subsurface reservoir evolution. Yet, they are among the most complicated targets for geophysical investigations. We develop a structure-oriented mapping technique to delineate the high-resolution dipping layers across the borehole region using the zero-offset vertical-seismic-profiling (ZVSP) survey, which is conventionally used to provide one-dimensional (1-D) wave propagation information. The key information for the single-shot VSP mapping, which is the structural dip across the borehole, is unreliable from the poor sub-salt surface seismic image. Alternatively, we obtain the structural dips via reflection layer tomography. Taking advantage of the accurate interval velocities and the high-fidelity identifications from the reflection events, we manage to invert for the geometry of the main reflectors identified across the wellbore. Based on such information, we propose an effective processing strategy and achieve a high-resolution image of the dipping structures around the borehole region from the ZVSP. The current result compares reasonably well with the corresponding surface seismic profile but supplies higher-resolution details of the dipping structures and fault networks below the thick evaporite caprock where the surface seismic image degrades sharply. The enhanced subsurface image encourages better structural evaluation, geologic interpretation, and future 2-D/3-D VSP survey design.

Consistent Least-Squares Reverse Time Migration Using Convolutional Neural Networks

The data-consistency item is a necessary condition for a reliable solution to the inverse problem. However, the current supervised-based deep-learning reconstruction approaches generally lack the data-consistency item, which directly leads to unreliable subsurface images for field data. To resolve this problem, we have developed a consistent least-squares reverse time migration (CLSRTM) approach using convolutional neural networks (CNNs), which is referred to as CNN-CLSRTM. The key point is that we have enforced that the predicted recording via the inverted image from the CNN model is consistent with the observed recording in the least-squares sense. We utilize the standard reverse time migration (RTM) image of single-shot recording as the input of the constructed CNN model. As a result, the optimal reflection image can be obtained by iteratively updating the parameters of CNN by minimizing the data residuals. Benefiting from the similarity of RTM images of adjacent recordings and the representation ability of the well-trained CNN model, we can directly predict the optimal reflection image for the testing datasets in a very fast way, which can greatly improve computational efficiency. Through synthetic and field data sets, we have determined that the proposed CNN-CLSRTM approach can retrieve high-resolution images with balanced amplitudes and continuous events. At the same time, our approach has better antinoise ability inherited from the benefit of CNN model compared to the standard LSRTM approach. In addition, we analyze the generalization ability of the CNN model for synthetic and field datasets.

A Robust Learning Method for Low-Frequency Extrapolation in GPR Full Waveform Inversion

Full-waveform inversion (FWI) plays a significant role in producing high-resolution subsurface imaging in seismic prospecting and ground penetrating radar (GPR). However, FWI faces various challenges in practice. For example, the lack of low-frequency information due to acquisition limitations will make the FWI prone to falling to the local minimum. In this project, a deep learning-based approach is proposed to extrapolate the low-frequency data. Specifically, we propose a robust progressive learning (RPL) algorithm that combines physics-guided FWI and data-driven deep learning technology. The proposed method is robust against the choice of the initial model. Experimental results show that our method can achieve high efficiency and accuracy by using a limited amount of training data. The subsurface structures are successfully reconstructed with our extrapolated low-frequency data.

A Consensus Equilibrium Approach for 3-D Land Seismic Shots Recovery

Physical and budget constraints often result in inadequate sampling for accurate subsurface imaging. Preprocessing approaches, such as missing trace interpolation, are typically employed to enhance seismic data in such cases. The compressed sensing (CS) framework has been applied for modeling missing seismic data, which is estimated by sparsity-based computational algorithms. While existing work mainly focuses on recovering missing traces resulting from receiver subsampling, source subsampling has greater economical advantages, as sources are more expensive than receivers. Moreover, stronger image models different from sparsity have not been explored for source recovery. This work presents a consensus equilibrium (CE) approach to recover missing seismic shots, which enables to incorporate various regularization operators modeling different data priors. Simulation results from a real 3-D land seismic dataset demonstrate that the CE approach provides more accurate estimations of the linear and hyperbolic events in the recovered shots, compared with pure sparsity-based reconstructions.

First high-resolution subsurface images of faults in the Imperial Valley

First high-resolution subsurface images of faults in the Imperial Valley

Deep-Learning-Based Calibration in Contrast Source Inversion Based Microwave Subsurface Imaging

A deep-learning (DL)-based data calibration technique applied to quantitative microwave inverse-scattering analysis is presented. This technique aims at subsurface inspection for the buried object under a concrete road or soil. The inverse-scattering analysis provides a complex permittivity profile, which is useful for object identification such as air gap or water. Contrast source inversion (CSI) is one of the most promising inverse-scattering methods. This method is capable of avoiding the iterative use of highly computational forward solvers. However, when applied to the measured data, an appropriate calibration capable of converting measured data to simulation data is required. In this work, a DL-based calibration suitable for nonlinear inverse problems is proposed. Its efficiency is experimentally demonstrated using a concrete cylinder containing water with different salinities.

Updating Subsurface Image of Khashim Al-Ahmer Gas Field Using the Interpretation for Anisotropic Velocity Model North-Eastern Iraq

This research deals with structural interpretation of Khashim Al-Ahmer Gas Field North-Eastern Iraq in Diyala Province, using the interpretation of inhomogeneous velocity data. The specific target in this field is the gaseous Jeribe reservoir representing the L. Miocene-Tertiary period. A very thick layer of evaporates Al-Fatha Formation is overlap the Jeribe Formation in the gas field and play as a sealed bed and transition zone for faults movement as a thrust fault. The thrust fault with gas content negatively affected the seismic energy, causing a high attenuation below the level of Al-Fatha Formation in the dome of the Khashm Al-Ahmer structure. Using the interval velocities derived from the sonic logs of five surrounding wells that represent the inhomogeneous behavior of the seismic wave velocity within the rock layers, a model of the velocity behavior in the field was built and the extent of the Jeribe gas reservoir was reconstructed according to the new velocities interpretation data.

A reduced-order variational Bayesian approach for efficient subsurface imaging

SUMMARYThis work considers the reconstruction of a subsurface model from seismic observations, which is known to be a high-dimensional and ill-posed inverse problem. Two approaches are combined to tackle this problem: the discrete cosine transform (DCT) approach, used in the forward modelling step, and the variational Bayesian (VB) approach, used in the inverse reconstruction step. VB can provide not only point estimates but also closed forms of the full posterior probability distributions. To efficiently compute such estimates of the full joint posterior distributions of large-scale seismic inverse problems, we resort to a DCT order-reduction scheme with a VB approximation of the posteriors, avoiding the need for costly Bayesian sampling methods. More specifically, we first reduce the model parameters through truncation of their DCT coefficients. This helps regularizing our seismic inverse problem and alleviates its computational complexity. Then, we apply a VB inference in the reduced-DCT space to estimate the dominant (retained) DCT coefficients together with the variance of the observational noise. We also present an efficient implementation of the derived VB-based algorithm for further cost reduction. The performances of the proposed scheme are evaluated through extensive numerical experiments for both linear and nonlinear forward models. In the former, the subsurface reflectivity model was reconstructed at a comparable estimation accuracy as the optimal weighted-regularized-least-squares solution. In the latter, the main structural features of the squared slowness model were well reconstructed.

Imaging subsurface geological complexity (2D/3D) beneath the Greater Srinagar region of the Kashmir basin, Northwest Himalaya

ABSTRACTA high‐resolution microtremor measurement in Greater Srinagar city of the Kashmir valley has been analysed to image 2D and 3D subsurface geological complexities. This region is located in the highly seismogenic Himalayan belt and sits atop a deep sedimentary lake bed with a laterally varying thickness of soft sediments. Srinagar region is a major economic centre and the capital city of the Kashmir valley with 2 million inhabitants living at high seismic risk. To assess the subsurface complexity beneath the city, we present: (1) high‐resolution subsurface shear wave velocity Vs structure using the horizontal‐to‐vertical spectral ratio inversion; (2) shear wave velocity for top 30 metres of soil column (VS30) map with National Earthquake Hazards Reduction Program site classification; (3) comparison of VS30 maps calculated from horizontal‐to‐vertical spectral ratio inversion and topographic slope methods; and (4) azimuthal behaviour of horizontal‐to‐vertical spectral ratio peaks, all of which unravel the subsurface spatial heterogeneity and suitability for the building of engineering structures in the study area. In addition, a new matlab code is applied to generate 3D subsurface Vs slices in the study region in different directions using its pre‐generated 2D Vs profile data. The presented potentiality of microtremor horizontal‐to‐vertical spectral ratio technique in Srinagar region, which lies on the eastern edge of the basin with significant topographic irregularities, indicates an uneven distribution of local site effects (primary and secondary) in the case of strong ground motion. The comprehensive results can be promising in engineering analyses of local ground and structural responses in order to mitigate the impact of earthquake occurrence and seismic risk in the city and adjoining regions.

Application of crosshole electrical resistivity tomography measurements under the influence of horizontally slotted plastic cased boreholes

ABSTRACTIn this work, we study the performance of cross‐hole electrical resistivity tomography measurements by employing different electrode array configurations in plastic polyvinyl chloride (PVC) cased and horizontally slotted observation boreholes by inserting a multi‐electrode cable directly into the borehole. Preliminary cross‐hole electrical resistivity tomography measurements in PVC cased boreholes related to an underground tunnel construction showed poor data quality. This was attributed to the borehole‐fluid effect caused by the PVC casings. An experimental study was conducted to support this hypothesis by setting up various simulations in a water tank, using different PVC casings with various slot densities, and different electrode array configurations. We conclude that the applicability of various measurement setups depends mainly on the acquisition protocol and, to a lesser extent, on the slot density of the PVC casing. Among the different array configurations considered, the pole–dipole array with the potential measuring electrodes being placed in a separate borehole to the current electrodes provide the most robust and reliable results, even for low slot density PVC casings. Besides, denser borehole slot configurations result in better data quality, though to a different extent for the examined protocols. A minimum slot density criterion of at least six slots/electrode spacing is proposed, regardless of the electrode array. The experimental findings are finally evaluated against real field measurements associated with the construction of an underground tunnel of the new Thessaloniki Metro, verifying the pole–dipole array's superior behaviour for this type of measurement configuration. Finally, for those cases where the aspect ratio (hole depth/hole separation) is limited, we propose a modified borehole‐to‐surface configuration with the current electrodes placed outside the boreholes. The overall results indicate that slotted PVC cased observation boreholes (e.g., conventional piezometers), typically constructed as part of many infrastructure monitoring projects, can be efficiently employed for electrical resistivity tomography mapping, generating a new perspective for geoelectrical prospecting. This measuring approach exhibits a significant advantage. The use of pre‐existing boreholes reduces the overall survey costs, reliability, and effort, while also providing high‐resolution subsurface images, especially in urban environments.

Subsurface imaging shows scale of the tragedy of Indigenous children

Archaeologists and geophysicists in Canada are trying to pinpoint the signatures of unmarked burials by using geophysical methods in varied terrains.

Nondestructive evaluation of rebar corrosion in concrete structures using ultrasonics and laser-based sensing

ABSTRACT Corrosion is a serious affliction for reinforced concrete (RC) structures and the development of advanced inspection techniques is crucial for maintaining serviceability and durability. In this study, a novel proof-of-concept is presented to monitor corrosion in the concrete by employing contact-based ultrasonic excitation and non-contact laser-based sensing. Accelerated corrosion of rebars in RC slabs is implemented, and ultrasonic scanning is performed on specimens at various corrosion levels. The excitation source is a contact ultrasonic transducer, which enables better penetration of the waves into the concrete medium. For the reception, a Doppler laser vibrometry based sensing is used, which expedites the inspection by circumventing the requirement of coupling. The Synthetic Aperture Focusing Technique (SAFT) is employed for subsurface image reconstruction. Rebar images obtained by hybrid sensing technique, are qualitatively similar to those obtained via traditional contact-based source and receiver system. A histogram-based qualitative analysis is presented to correlate the characteristics of the rebar images to the progress of corrosion. A planar SAFT imaging algorithm is presented to demonstrate the rebar on a horizontal plane for better visualisation. The proposed technique is significantly faster in comparison to the contact-based approach and, therefore, has the potential of being applied for inspection of larger concrete infrastructure.

Imaging Complex Subsurface Structures for Geothermal Exploration at Pirouette Mountain and Eleven-Mile Canyon in Nevada

Accurate imaging of subsurface complex structures with faults is crucial for geothermal exploration because faults are generally the primary conduit of hydrothermal flow. It is very challenging to image geothermal exploration areas because of complex geologic structures with various faults and noisy surface seismic data with strong and coherent ground-roll noise. In addition, fracture zones and most geologic formations behave as anisotropic media for seismic-wave propagation. Properly suppressing ground-roll noise and accounting for subsurface anisotropic properties are essential for high-resolution imaging of subsurface structures and faults for geothermal exploration. We develop a novel wavenumber-adaptive bandpass filter to suppress the ground-roll noise without affecting useful seismic signals. This filter adaptively exploits both characteristics of the lower frequency and the smaller velocity of the ground-roll noise than those of the signals. Consequently, this filter can effectively differentiate the ground-roll noise from the signal. We use our novel filter to attenuate the ground-roll noise in seismic data along five survey lines acquired by the U.S. Navy Geothermal Program Office at Pirouette Mountain and Eleven-Mile Canyon in Nevada, United States. We then apply our novel anisotropic least-squares reverse-time migration algorithm to the resulting data for imaging subsurface structures at the Pirouette Mountain and Eleven-Mile Canyon geothermal exploration areas. The migration method employs an efficient implicit wavefield-separation scheme to reduce image artifacts and improve the image quality. Our results demonstrate that our wavenumber-adaptive bandpass filtering method successfully suppresses the strong and coherent ground-roll noise in the land seismic data, and our anisotropic least-squares reverse-time migration produces high-resolution subsurface images of Pirouette Mountain and Eleven-Mile Canyon, facilitating accurate fault interpretation for geothermal exploration.

Could fiber strains affect DAS amplitude response?

Absolute wavefield amplitude information is essential to amplitude-based distributed acoustic sensing (DAS) studies such as attenuation analysis, source inversion, and subsurface imaging. Here, we report the results of a pilot experimental study aimed at exploring whether and how the strain present in a fiber-optic cable would affect the amplitude response of a DAS system. Using a precise strain-controlled setup, we find that DAS amplitudes drop sharply as the fiber shifts from a loose to a strained state. This is followed by an exponential decrease in amplitude as a function of tensile strain up to 16,000 με. Additionally, the observed strain effect weakens with increasing vibration intensity. Overall, the revealed fiber strain dependence of DAS amplitude measurements may have implications for the accurate interpretation of DAS recordings acquired by preexisting telecommunications cables. It is also relevant to the design and optimal deployment of fit-for-purpose DAS cables in oil and gas wells, tunnels, and other engineering systems.

High‐performance computing strategies for seismic‐imaging software on the cluster and cloud‐computing environments

ABSTRACTModern subsurface imaging techniques allow obtaining high‐quality images but with high computational costs. Nonetheless, depending on the amount of data, their execution is limited by memory in the current generation's hardware. However, with the advancement of new hardware and cloud‐based solutions, these problems are mitigated but still with the risk of work loss and instability. To mitigate the execution problems in memory‐limited and fail‐prone environments, we propose two high‐performance computing techniques. The first is based on independent checkpointing alongside a fault‐tolerant framework to store an execution state and recover from that state in case of failures. Besides, for memory‐limited graphics processing units, we present a technique to reduce the amount of memory requirement that we call the hybrid strategy. The experiments showed that the independent checkpointing alongside the fault‐tolerant framework is able to mitigate the performance penalty of node failures, with the independent checkpointing technique being more relevant when multiple nodes are terminated. Furthermore, the hybrid strategy technique has shown the possibility of execution of larger models that could make the graphics processing unit run out of memory otherwise. Finally, our implementation is scalable, allowing a significant improvement in performance when adding new nodes. In conclusion, our techniques can be used to deliver fast, high‐fidelity subsurface imaging in unstable and memory‐limited environments, such as the cloud.

True-amplitude migration through regularized extended linearized waveform inversion

The ability to create subsurface images whose amplitudes are proportional to the elastic wavefield variations recorded within seismic data as a function of reflection angle is fundamental for performing accurate amplitude-variation-with-offset (AVO) analysis and inversion. A process that generates such images is commonly referred to as true-amplitude migration. We have determined how the extended subsurface-offset image space is able to preserve the elastic behavior of the primary reflections when these events are acoustically migrated with a reverse time migration approach performed in a least-squares fashion. Using a single-interface model, we determine how the angle-domain image amplitude variations from an extended-offset acoustically migrated image closely follow the theoretical elastic Zoeppritz response even at the critical angle. Furthermore, we develop a subsalt synthetic test in which 1C ocean-bottom-node (OBN) data are used within a regularized linearized waveform inversion procedure. In this test, we highlight the ability of the acoustic extended-angle image domain to preserve the correct elastic amplitude variations of the reflected events from three subsalt sand lenses. Our method allows accurate inversion of elastic-wave data for subsurface parameter variations that are critical for reservoir characterization in oil and gas exploration and production. We determine its performance on an OBN field data set recorded in the Gulf of Mexico in which the AVO response of a potential gas-bearing prospect is correctly retrieved.

Angular and radial sampling criteria for monostatic and bistatic radar tomography of solar system small bodies

Low-frequency radar tomography is an important subsurface imaging method for future planetary missions to solar system small bodies. We derive angular and radial sampling criteria for monostatic and bistatic radar tomography algorithms that are based on monochromatic free-space backprojection and spherical apertures. We use the vector Born approximation to highlight the degeneracy of monochromatic bistatic source/receiver direction pair measurements in k-space. Analytical expressions are then derived for the scalar point target response of different spherical sampling geometries. We also derive the angular sampling step and total number of sampling points required to fully reconstruct the point target response for monostatic, bistatic, and non-degenerate k-space spherical apertures. These are evaluated for object sizes and radar operating frequencies expected in small body tomography. We also analyze and derive expressions for the coherence loss of spherical apertures due to random errors in a sensor’s radial position, which provides requirements on the a posteriori ephemeris knowledge. Finally, we derive a vector backprojection algorithm suitable for focusing quad-pol scattering matrix (S-matrix) data that is tested using full-wave S-matrix simulations of dielectric point targets. This work is intended to aid radar instrument performance analysis and inform the design and architecture of future instruments and missions.