Subsurface Imaging Research Articles

Seismic Waveform Inversion Capability on Resource-Constrained Edge Devices.

Seismic full wave inversion (FWI) is a widely used non-linear seismic imaging method used to reconstruct subsurface velocity images, however it is time consuming, has high computational cost and depend heavily on human interaction. Recently, deep learning has accelerated it's use in several data-driven techniques, however most deep learning techniques suffer from overfitting and stability issues. In this work, we propose an edge computing-based data-driven inversion technique based on supervised deep convolutional neural network to accurately reconstruct the subsurface velocities. Deep learning based data-driven technique depends mostly on bulk data training. In this work, we train our deep convolutional neural network (DCN) (UNet and InversionNet) on the raw seismic data and their corresponding velocity models during the training phase to learn the non-linear mapping between the seismic data and velocity models. The trained network is then used to estimate the velocity models from new input seismic data during the prediction phase. The prediction phase is performed on a resource-constrained edge device such as Raspberry Pi. Raspberry Pi provides real-time and on-device computational power to execute the inference process. In addition, we demonstrate robustness of our models to perform inversion in the presence on noise by performing both noise-aware and no-noise training and feeding the resulting trained models with noise at different signal-to-noise (SNR) ratio values. We make great efforts to achieve very feasible inference times on the Raspberry Pi for both models. Specifically, the inference times per prediction for UNet and InversionNet models on Raspberry Pi were 22 and 4 s respectively whilst inference times for both models on the GPU were 2 and 18 s which are very comparable. Finally, we have designed a user-friendly interactive graphical user interface (GUI) to automate the model execution and inversion process on the Raspberry Pi.

Room and elevated temperature sliding wear of high velocity oxy-fuel sprayed Diamalloy3001 coatings

CoMoCrSi alloys exhibit high hardness and excellent wear resistance due to the presence of intermetallic Laves phases in a metallic Co-matrix. In this study, we deposited Diamalloy3001 coatings using high velocity oxy-fuel (HVOF) spraying process and evaluated their tribology at temperatures 25ᵒC, 200ᵒC and 350ᵒC. At room temperature, formation of tribolayers composed of oxides of Co, Cr, and Mo resisted localized shear deformation and resulted in low wear rates. At elevated temperatures, detachment of the tribolayers, and formation of cavities during reciprocating sliding resulted in high wear rates. Electron channel contrast imaging (ECCI) of the worn subsurface showed that at elevated temperatures, oxidation of splat boundaries led to interparticle debonding during wear and decreased the stability of the tribolayers.

Imaging the Subsurface Using Acoustic Signals Generated by a Vessel

The acoustic wavefield originating from a vessel has historically not been considered as a source of signal in the imaging of marine seismic data and is therefore treated as a source of noise. In this work, the feasibility of acquiring seismic data without an active source and instead using the acoustic wavefield generated by a vessel for imaging the subsurface has been investigated. There are areas around the world where the use of active marine seismic sources is not permitted throughout the year, or only permitted during short time periods. In such areas, acquiring seismic data using the acoustic signals generated by a vessel as a source may be an alternative. Using a vessel as a seismic source may also offer a low cost and low impact 4D monitoring solution with an opportunity for much more frequent acquisition of time lapse data especially over permanent receiver installations. This work demonstrates that it is possible to obtain very high-resolution seismic images of the shallow subsurface by using acoustic signals generated by a vessel. This outcome is due to the broadband signals that the vessel generates combined with the fact that these signals are generated continuously while the vessel is moving, allowing for extremely dense source-side sampling along the vessel path.

Point-of-care device for Graves’ disease detection using Infrared Thermography

IR Thermography (IRT) provides real-time, non-invasive sub-surface images of the thyroid region without any hazardous radiation exposures. Thermography represents thermal radiation in the form of an image and provides information about important physiological parameters using skin temperature distribution. Graves' disease is the most frequently diagnosed disease of women in India. Patients with graves' disease have different temperature distribution relative to the healthy ones. This paper aims to use thermography with deep learning and machine learning techniques to develop a GUI/non-invasive method for detecting graves' disease. A CNN-based app is developed and deployed in a clinical environment to add to the analysis so as to diminish sparsity in the available data set. For our study IR Images of the sixty persons with different age groups and gender has been taken and showed that control and hyperthyroid patient have different behaviour and lie in different quadrants, which can help clinicians to conclude whether the patient is healthy or not. It is expected that this imaging biomarker test will reduce the ethical burden on clinicians' shoulders before recommending scintigraphy and standard thyroid blood tests, thus saving costs at patients' end and pathology labs in populated countries such as India. An affordable Thermal plug-and-play attachment can be connected to any smartphone for faster diagnosis at the patient end, converting the smartphone into a point of care device.

Learning-Based Subsurface Quantitative Imaging via Near-Field Scanning Microwave Microscopy

Accurate characterization of sample dielectric property is essential for scientists and engineers to analyze and evaluate the performance of the invested sample. This article proposes a learning-based (LB) method for quantitative subsurface imaging via coaxial-resonator-based near-field scanning microwave microscopy (CR-NFSMM) in a nondestructive way. To efficiently generate the database, a well-designed forward solver is critical since many samples will be included in the database, each sample needs a scanning procedure, and each scanning point involves solving the forward problem once. A fast forward problem solver is used to evaluate the tip–sample interaction in CR-NFSMM that avoids repeated meshing during the scanning process and it can apply to an arbitrary tip shape. Once the neural network is trained, it generates the reconstructed image within 1 s. Numerical and experimental results show that the proposed LB method could improve the resolution of the image and recover the dielectric properties of the subsurface perturbation pixel-by-pixel. Moreover, the proposed method outperforms the traditional objective function approach in terms of image resolution and time cost. The proposed method is promising to realize a nondestructive and real-time local dielectric evaluation of the subsurface object.

Microwave Subsurface Imaging Method by Incorporating Radar and Tomographic Approaches

Due to its high resolution and deep penetration depth, microwave ultrawideband radar is a promising tool for the non-destructive testing (NDT) of transportation infrastructure. Microwave radiation can also be used to reconstruct the dielectric properties of objects and therefore can be used to detect an air cavity or metallic rust in concrete. We used contrast source inversion (CSI) as one of the most promising inverse scattering schemes. To resolve the observation domain limitations of NDT, radar imaging method, also known as range points migration (RPM) method, was first incorporated into the inverse scattering algorithm based on CSI as a region of interest (ROI) estimator, which substantially improves the accuracy of complex permittivity reconstruction. In addition, the ROI optimizing scheme based on the CSI cost function is used to enhance ROI accuracy. The effectiveness of the proposed methods is validated via finite-difference time-domain (FDTD)-based numerical simulation, which assumes typical NDT model.

Complete identification of complex salt geometries from inaccurate migrated subsurface offset gathers using deep learning

Delimiting salt inclusions from migrated images is a time-consuming activity that relies on highly human-curated analysis and is subject to interpretation errors or limitations of the methods available. We develop the use of migrated images produced from an inaccurate velocity model (with a reasonable approximation of sediment velocity, but without salt inclusions) to predict the correct salt inclusions shape using a convolutional neural network. Our approach relies on subsurface common-image gathers to focus the sediments’ reflections around the zero offset and to spread the energy of salt reflections over large offsets. Using synthetic data, we train a U-Net to use common-offset subsurface images as input channels and the correct salt masks as the output of a semantic segmentation problem. The network learns to predict the salt inclusions masks with high accuracy; moreover, it also performs well when applied to synthetic benchmark data sets that were not previously introduced. Our training process successfully tunes the U-Net to learn the shape of complex salt bodies from partially focused subsurface offset images.

Image-domain DAS 3D VSP elastic transmission tomography

SUMMARY Full-wavefield elastic imaging of active-source seismic data acquired by downhole receivers commonly offers higher-resolution subsurface images in the vicinity of a borehole compared to conventional surface seismic data sets, which can lack higher-frequency wavefield components due to longer travel paths and increased attenuation. An increasingly used approach for downhole acquisition is vertical seismic profiling (VSP), which has become more attractive when coupled with distributed acoustic sensing (DAS) using optical fibres installed in wells. The main difficulty for generating high-quality images with full-wavefield imaging tools for DAS VSP data, though, is the need for an accurate velocity model. To build plausible velocity models using active-source DAS VSP data, we adopt a 3-D image-domain elastic transmission tomography technique, originally developed for surface-recorded passive (microseismic) data, by exchanging the source and receiver positions (i.e. reciprocity) to mimic a passive-seismic surface monitoring scenario. The inversion approach exploits various images for each source constructed through time-reverse imaging (TRI) of downgoing P- and S-wave first-arrival waveforms. The TRI process uses the kinetic term of the (extended) PS energy imaging condition that exhibits sufficient sensitivity to velocity model errors. The method automatically updates the P- and S-wave velocity models to optimize image focusing via adjoint-state inversion. We illustrate the efficacy of the adopted elastic inversion technique using an active-source DAS 3-D VSP field data set acquired in the North Slope of Alaska. The numerical experiments demonstrate that the inverted elastic velocity models can be further used in full-wavefield acoustic/elastic imaging algorithms to obtain accurate subsurface images.

A robust migration velocity analysis method based on adaptive differential semblance optimization

Migration velocity analysis (MVA) is an efficient tool to reconstruct low wavenumber components of the model. Compared with data domain methods, the MVA methods are implemented in image domain by processing imaging results directly through minimizing moveouts and improving the coherence of the common image gathers (CIGs), such as angle domain common image gathers (ADCIGs) or subsurface offset domain image gathers (ODCIGs). As one of the wave-equation based migration velocity analysis (WEMVA) methods, differential semblance optimization (DSO) can automatically detect the moveout existed in the CIGs and is convenient to implement. However, referring to the CIGs contaminated by spurious imaging artefacts caused by uneven illumination and irregular observation geometry, the traditional DSO method may produce poor velocity update with oscillations, which can prevent rapid convergence to a correct velocity model. To deal with this issue and extend the solution space, we introduce an adaptive DSO (ADSO) method by replacing the traditional image matching with energy distribution matching. By reducing dependence on the imaging results and introducing a variable weighting function in the calculation of the adjoint sources, the amplitude of the inverted model can be evenly distributed and free of artefacts. Three numerical examples show that the ADSO method is robust with little artefacts presenting in the gradients. Combined with the improved gradient, the ADSO can lead to a stable update.

Application of six-component ambient seismic noise data for high-resolution imaging of lateral heterogeneities

SUMMARY We develop a novel approach for imaging subsurface lateral heterogeneities using six-component (6C) ambient seismic noise data, consisting of three translational components and three rotational components. We first derive the 6C cross-correlation functions (CCFs) from ambient seismic noise data for surface waves and then apply the modified frequency Bessel (MF-J) transform to the 6C CCFs for surface wave dispersion curves. The phase velocities of Rayleigh and Love waves can be directly calculated from the 6C CCFs of a single pair of stations. Traditional array-based surface wave methods derive the surface wave phase velocity based on the presumption of a horizontally layered model. When this assumption breaks down due to the presence of, for example, dipping layer or heterogeneities, the resulting phase velocity can be severely smeared. By contrast, the proposed approach is not limited by the layered model assumption because it relies on single-point measurements to calculate the localized dispersion relations of the formation right beneath a receiver. Our numerical modelling results demonstrate that this approach is applicable to heterogeneous models and can image small-scale subsurface anomalies with very high lateral resolution.

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends.

Linear infrastructures, such as railways, tunnels, and pipelines, play essential roles in economic and social development worldwide. However, under the influence of geohazards, earthquakes, and human activities, linear infrastructures face the potential risk of damage and may not function properly. Current monitoring systems for linear infrastructures are mainly based on non-contact detection (InSAR, UAV, GNSS, etc.) and geotechnical instrumentation (extensometers, inclinometers, tiltmeters, piezometers, etc.) techniques. Regarding monitoring sensitivity, frequency, and coverage, most of these methods have some shortcomings, which make it difficult to perform the accurate, real-time, and comprehensive monitoring of linear infrastructures. Distributed acoustic sensing (DAS) is an emerging sensing technology that has rapidly developed in recent years. Due to its unique advantages in long-distance, high-density, and real-time monitoring, DAS arrays have shown broad application prospects in many fields, such as oil and gas exploration, seismic observation, and subsurface imaging. In the field of linear infrastructure monitoring, DAS has gradually attracted the attention of researchers and practitioners. In this paper, recent research and the development activities of applying DAS to monitor different types of linear infrastructures are critically reviewed. The sensing principles are briefly introduced, as well as the main features. This is followed by a summary of recent case studies and some critical problems associated with the implementation of DAS monitoring systems in the field. Finally, the challenges and future trends of this research area are presented.

Exploiting temporal data reuse and asynchrony in the reverse time migration

Reverse Time Migration (RTM) is a state-of-the-art algorithm used in seismic depth imaging in complex geological environments for the oil and gas exploration industry. It calculates high-resolution images by solving the three-dimensional acoustic wave equation using seismic datasets recorded at various receiver locations. Reverse Time Migration’s computational phases are predominantly composed of stencil computational kernels for the finite-difference time-domain scheme, applying the absorbing boundary conditions, and I/O operations needed for the imaging condition. In this paper, we integrate the asynchronous Multicore Wavefront Diamond (MWD) tiling approach into the full RTM workflow. Multicore Wavefront Diamond permits to further increase data reuse by leveraging spatial with Temporal Blocking (TB) during the stencil computations. This integration engenders new challenges with a snowball effect on the legacy synchronous RTM workflow as it requires rethinking of how the absorbing boundary conditions, the I/O operations, and the imaging condition operate. These disruptive changes are necessary to maintain the performance superiority of asynchronous stencil execution throughout the time integration, while ensuring the quality of the subsurface image does not deteriorate. We assess the overall performance of the new MWD-based RTM and compare against traditional Spatial Blocking (SB)-based RTM on various shared-memory systems using the SEG Salt3D model. The MWD-based RTM achieves up to 70% performance speedup compared to SB-based RTM. To our knowledge, this paper highlights for the first time the applicability of asynchronous executions with temporal blocking throughout the whole RTM. This may eventually create new research opportunities in improving hydrocarbon extraction for the petroleum industry.

Random and coherent noise attenuation for 2D land seismic reflection line acquired in Iraq

ABSTRACT Noises are common events in seismic reflection data that have very striking features in seismograms, affecting seismic data processing and interpretation. Noise attenuation is an essential phase in seismic processing data, usually resulting in enhancing the signal-to-noise ratio (SNR) and improving the subsurface seismic image. Groundroll presence is a major fashion of significant noise in land seismic surveys. It is a type of coherent noise present in seismograms that appears as linear events, in most cases overlapping the reflections and probably making it challenging to recognise. There are several domains used in noise attenuation techniques. The domain transformations are a complex algorithm used commonly during the processing of seismic data; Therefore, a large number of methods have been developed to attenuate these types of noise to preserve the frequency bandwidth and enhance the SNR of the seismic data. In the time-offset domain, the noise wave such as groundroll and random noises overlap over time; a different domain makes it easier to successfully isolate coherent, random noise and reflection events. We have used effective algorithms in different domains such as (shot, receiver, t-x, f-x, f-k, R-T, and offset class) to attenuate coherent and random noises present in the data. The results indicate that the different domains can reveal features and geological structures that have been masked by the noises present in current data. Because these filtering techniques encourage significant improvements in the final image quality in the 2D seismic section, possibly giving the interpreter an advantage, particularly in structural and stratigraphic interpretation.

Subsurface Imaging of Microfabricated Silicon Membrane–Cavity Structures Using Near‐Infrared Interferometry

Subsurface Imaging of Microfabricated Silicon Membrane–Cavity Structures Using Near‐Infrared Interferometry

Layered subsurface in Utopia Basin of Mars revealed by Zhurong rover radar

Exploring the subsurface structure and stratification of Mars advances our understanding of Martian geology, hydrological evolution and palaeoclimatic changes, and has been a main task for past and continuing Mars exploration missions1–10. Utopia Planitia, the smooth plains of volcanic and sedimentary strata that infilled the Utopia impact crater, has been a prime target for such exploration as it is inferred to have hosted an ancient ocean on Mars11–13. However, 45 years have passed since Viking-2 provided ground-based detection results. Here we report an in situ ground-penetrating radar survey of Martian subsurface structure in a southern marginal area of Utopia Planitia conducted by the Zhurong rover of the Tianwen-1 mission. A detailed subsurface image profile is constructed along the roughly 1,171 m traverse of the rover, showing an approximately 70-m-thick, multi-layered structure below a less than 10-m-thick regolith. Although alternative models deserve further scrutiny, the new radar image suggests the occurrence of episodic hydraulic flooding sedimentation that is interpreted to represent the basin infilling of Utopia Planitia during the Late Hesperian to Amazonian. While no direct evidence for the existence of liquid water was found within the radar detection depth range, we cannot rule out the presence of saline ice in the subsurface of the landing area.

Least-squares reverse time migration via deep learning-based updating operators

Two common issues of least-squares reverse time migration (LSRTM) consist of the many iterations required to produce substantial subsurface imaging improvements and the difficulty of choosing adequate regularization strategies with optimal hyperparameters. We investigate how supervised learning can mitigate these shortcomings by solving the LSRTM problem through an iterative deep learning framework inspired by the projected gradient descent algorithm. In particular, we develop an image-to-image approach interlacing the gradient steps at each iteration with blocks of residual convolutional neural networks (CNNs) that capture the prior information in the training phase. By including the least-squares data-misfit gradient into the learning process, we force the solution to comply with the observed seismic data, while the CNN projections implicitly account for the regularization effects that lead to high-resolution reflectivity updates. After training with 900 randomly generated instances, our network ensemble can estimate accurate reflectivity distributions in only a few iterations. To demonstrate the effectiveness and generalization properties of the method, we consider three synthetic cases: a folded and faulted model, the Marmousi model, and the Sigsbee2a model. We empirically find that it is possible to obtain an improved reflectivity model for out-of-distribution instances by using the learned reconstructions as warm starts for the conjugate gradient algorithm and bridging the gap between the learned and conventional LSRTM schemes. Finally, we apply the proposed network with transfer learning on a 2D towed-streamer Gulf of Mexico field data set, producing high-resolution images comparable with traditional LSRTM but drastically reducing the required number of iterations.

GEOMETRY OF RECENT RUPTURE ZONE AT СAPE RYTYI BASED ON GROUND PENETRATING RADAR (NORTHWESTERN COAST OF LAKE BAIKAL)

In here, we present the results of the GPR study of the modern rupture zone discovered earlier in the periphery of the Rita River delta flowing into Lake Baikal. The research was aimed at subsurface imaging of dislocation geometry using the Logis-Geotech OKO-2 radar equipped with the ABDL Triton antenna. As a result, the characterizations have been obtained for six 73 to 197 m long profiles across the rupture zone. All disturbances visible on the aerial photography materials are highlighted on the radargrams by the oblique reflection event due to a decrease in the signal amplitude. The rupture zone has two large segments – northeastern and submeridional, – which differ in zone width, number of discontinuities, dip angles, and displacement amounts. The deformation features can be attributed to different amounts of a plastic loamy aggregate of coarse deposits in different parts of the Rita River delta that is indirectly confirmed by weaker signal amplitudes on the northeastern segment. Based on present and previous studies, we proposed that the M=5.2 earthquake occurred on August 13, 1962, in Lake Baikal initiated the gravitational sinking in the Rita river delta edge by subsidence along gently sloped rupture surfaces on one segment and along steeply sloped ones on the other. It is necessary to identify and monitor such phenomena to prevent the development of emergencies associated with the collapse of the coasts of large water reservoirs.

Subsurface imaging of fluvial deposits of the Vistula River valley in Kraków (southern Poland) by 2D ERT survey

We describe the application of 2D ERT (electrical resistivity tomography) surveys to investigate the spatial complexity of fluvial deposits of the Wisła River valley in the eastern part of Kraków (southern Poland). All ERT survey lines were completed within the industrially influenced floodplain of the Wisła River at two research sites. Due to the transformation of the natural state of the environment through many years of industrial activity of the ArcelorMittal Kraków plant, some of the geomorphological elements analysed have been irretrievably transformed and hidden by anthropogenic accumulations such as waste landfills and engineering structures. Hence, many years of soil contamination have changed the primary resistivity characteristics of the subsurface. For this purpose, the measurement array applied combines standard arrays, i.e., Wenner-Schlumberger and Dipole-Dipole, which gave improved results (higher resolution) in comparison to the standard single array. The data interpretation method was supported by the calculation and visualization of the vertical and horizontal gradients of the interpreted resistivity within the resistivity sections. This approach allowed accurate determination of resistivity boundaries on the ERT resistivity sections and thus helped lithological interpretation of the fluvial deposits in the research area. The resistivity of water in a channel located within one of the analysed areas has impacted some of the research results. Furthermore, 2D ERT forward modeling was implemented to generate synthetic datasets. The synthetic data allowed investigation of the influence of groundwater contamination on the resistivity distribution within superficial layers, and also tested the ability of the 2D ERT model to recognize the detailed spatial distribution of palaeomeander (meander scar) infills. All methods have provided new information on the industrially influenced floodplain of the Wisła River in Kraków.

Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.

Acquisition and Preparation of 3D Seismic Data for Pre-Stack Inversion at Siba Oilfield, Southeastern Iraq

The current study includes preparing a geometric proposal of the main parameters that must be worked within a seismic reflection survey to prepare a three-dimensional subsurface image. This image represents the Siba oil field located in Basra, southern Iraq. The results were based on two options for selecting these approved elements to create a three-dimensional image of the Mishrif, Zubair and Yamama formations as well as the Jurassic and Permian Khuff and the pre-Khuff reservoir area. The first option is represented in the geometry in option -1 is 12 lines, 6 shots, and 216 chs. The receiver density is 66.67 receivers / km2, so the shot density is the same. Total shots are 21000, which is the same number of receiving points. The survey area (in km2) and full fold area (in km2) are 317and 328, respectively. However, the second option in the geometry are16 lines, 16 shots, and 208 chs. Receiver density and shot density are 50, while the total shots are 16200 and the total receiving points are 16000; with survey area and full folded area are 198 and 198 km2. The aim of the current study is to characterize the structure and reservoir quality of Yamama and Zubair intervals and to image potential exploration targets such as the Mishrif, Jurassic, Permian Kuff and Pre Khuff formations. The present study succeeded in preparing the geometric proposal and getting at a subsurface image of the reservoir in three dimensions.