Subsurface Model Research Articles

Two-Dimensional Modeling of Leachate Distribution in Batu Layang Landfill on Peat Soil using Geoelectric Method

The open dumping system implemented by the Batu Layang landfill can produce leachate, contaminating groundwater around the landfill. Groundwater contaminated with leachate, if used by the community, can cause health problems. Therefore, management efforts were needed, such as subsurface leachate modeling. This study aims to model the distribution of subsurface leachate in 2 dimensions. The modeling of leachate distribution below the soil surface used the Wenner configuration geoelectric method. The number of geoelectric measurement paths was two paths. The length of each track was 195 m, with a distance of 5 m between the electrodes. Data obtained from resistivity values were analyzed using the Res2Dinv application. The calculation of leachate discharge used the water balance method. Based on the inversion results using the Res2Dinv software, the resistivity value of leachate-contaminated soil in line 1 was 1.19-9.43 Ωm, and track 2 was 0.5-10.2 Ωm. The leachate resistivity value was <10 Ωm because the leachate contains inorganic minerals (metals), so the resistivity value was low. Land contaminated with leachate was estimated to be spread as far as 195 m. The most profound leachate depth was estimated at 39.4 m. Batu Layang Landfill leachate discharge in Zone E averages 4 m3/day.

A Universal Data Format for Wellbore Logs

This paper describes a proposed high-specification standard format that is ideally suited for the data management of definitive records of wellbore logs. For this reason, it is a good standard for data exchange between applications. The format is suitable for complex three-dimensional (3D) data, including those generated by deep azimuthal resistivity (DAR) and ultradeep azimuthal resistivity (UDAR) tools, acoustic borehole reflection images, vertical seismic profiles (VSP), borehole imaging tools, multifingered caliper logs, and array data with multiple depths of investigation. It is applicable for use with logging-while-drilling (LWD) and wireline-conveyed logging tools. The format also naturally collapses down when utilized to store simple conventional logs that contain one value per depth in the wellbore. The proposed format provides spatial details of every data point collected by or interpreted from a wellbore-logging tool. The position of each data point is defined by reference back to the measure point of the sonde, which in turn is defined by the wellbore deviation survey and its coordinate reference system (CRS). Each data point in space may have an unrestricted number of parameters. An example might be most likely horizontal and vertical resistivity, maximum value based on uncertainty, minimum value based on uncertainty, and flags indicating the data position with respect to depth of detection (DOD). The new proposed format is so versatile. It is suitable as an Open Group Open Subsurface Data UniverseTM (OSDUTM) standard to store and exchange all data measured by logging tools in a wellbore and can possibly be extended to include all well data (for example, core, cuttings, and more). The proposed format requires a detailed definition so that computer scientists can implement it in applications used for subsurface modeling. The OSDU will also require this detailed definition in order to adopt it as a standard.

An attempt to use convolutional neural network to recover layered-earth structure from electrical resistivity tomography survey

The Electrical Resistivity Tomography (ERT) method is a geophysical prospecting technique for determining subsurface structure in terms of electrical resistivity distribution. Direct current is injected into the ground through a pair of electrodes, and the potential difference is measured using another pair of electrodes. The estimated resistivity model of the subsurface is then obtained using a mathematical optimization algorithm called the inversion algorithm, which incorporates multiple measured potential differences from different electrode configurations as the input data. The inverted resistivity model serves as a resource for the interpretation process of the survey. Specialists employ the model and the prior geological information in the area to extract the subsurface geological structure under the data measurement profile. The inverted model can be treated as a two-dimensional image in which each element represents resistivity. The image can be visualized as a blurred or distorted version of the actual resistivity image of the subsurface. We, therefore, developed a post-inversion image enhancement code using a neural network aiming to recover the true subsurface model with predetermined features. We employed the convolutional neural network (CNN) that uses the inverted models as input and returns an enhanced model as output. We tested our developed neural network with a case study that utilized the proposed method to recover the layered earth structure which is often found in real surveys. The electrode spacing was 5 meters in the Schlumberger array configuration. The true subsurface resistivity models of training data and validating data for evaluating the neural network were randomly generated based on the predetermined feature of the layered structure. We then generated their respective inverted models from those data through simulated inversion routine. As a result, 94.75% of the enhanced models in the validation dataset exhibited a lower loss metric compared to the original inverted models in the same validation dataset. We also applied the proposed method to practical field data to recover the subsurface layers and compared them with stratigraphic information from a borehole in each area. The result showed that the proposed method gives well-resolved structures in the enhancement process after the typical inversion.

Penentuan Nilai Resistivity sebagai Langkah Awal Pemodelan Kedalaman Bidang Gelincir di Tempat Wisata Batu Tongkon

Landslides are the process of decreasing soil volume due to water deposits stored in the gaps in the soil due to the prolonged dry season. When the rainy season arrives, water will fill the gaps, causing the soil texture to soften briefly, causing landslides to occur. This research was conducted to determine the layers of slip planes in the Batutongkon tourist area, Battang sub-district as preliminary information on the volume and direction of material movement of landslide material by collecting data on 2 trajectories using the Wenner - Schlumberger configuration geoelectric method. Field data acquisition was analyzed using Microsoft Excel software to obtain apparent resistivity values. The principle of the resistivity method is to flow electric current into the earth through the contact of two current electrodes. Then the resulting potential distribution was measured and then processed using Res2Dinv software which produces 2D subsurface modeling, for 3D results using Microsoft Excel software as a medium for processing field data and continued using the surfer application to produce 3D modeling.

Geophysics‐Informed Hydrologic Modeling of a Mountain Headwater Catchment for Studying Hydrological Partitioning in the Critical Zone

AbstractHydrologic modeling has been a useful approach for analyzing water partitioning in catchment systems. It will play an essential role in studying the responses of watersheds under projected climate changes. Numerous studies have shown it is critical to include subsurface heterogeneity in the hydrologic modeling to correctly simulate various water fluxes and processes in the hydrologic system. In this study, we test the idea of incorporating geophysics‐obtained subsurface critical zone (CZ) structures in the hydrologic modeling of a mountainous headwater catchment. The CZ structure is extracted from a three‐dimensional seismic velocity model developed from a series of two‐dimensional velocity sections inverted from seismic travel time measurements. Comparing different subsurface models shows that geophysics‐informed hydrologic modeling better fits the field observations, including streamflow discharge and soil moisture measurements. The results also show that this new hydrologic modeling approach could quantify many key hydrologic fluxes in the catchment, including streamflow, deep infiltration, and subsurface water storage. Estimations of these fluxes from numerical simulations generally have low uncertainties and are consistent with estimations from other methods. In particular, it is straightforward to calculate many hydraulic fluxes or states that may not be measured directly in the field or separated from field observations. Examples include quickflow/subsurface lateral flow, soil/rock moisture, and deep infiltration. Thus, this study provides a useful approach for studying the hydraulic fluxes and processes in the deep subsurface (e.g., weathered bedrock), which needs to be better represented in many earth system models.

Seismic Facies Analysis, 3D Horizon Mapping, Source and Reservoir Rock Characteristics of the Paleocene Sequence in Potwar Basin, Pakistan: with Emphasis on Petroleum Potential Prospect

Abstract —This study presents a comprehensive analysis of the Balkassar oilfield in the Potwar Basin, Pakistan. It integrates seismic reflection data, a 3D subsurface horizon model, and geochemical and petrophysical data. Evaluating the Lockhart Formation’s geochemical properties using well cuttings reveals consistent patterns of relatively low to fair total organic carbon (TOC). S2 values suggest a fair generation potential with a mixed Type II–III kerogen composition. Seismic facies analysis identifies five distinct categories with parallel attributes. Horizons from lower Permian to Eocene show varying amplitudes and prevalent parallel to wavy internal configurations. Geometries, mainly sheet-to-wedge, enhance stratigraphic understanding. The 3D seismic interpretation reveals the Lockhart Formation’s structural traits, with pronounced northwest dip and gentler southeast inclination. Faults flanking the formation truncate its limbs. A significant contour closure in the northwest, confined by fault boundaries, signifies attractive hydrocarbon potential. Petrophysical analysis indicates an average 9.17% porosity. Dominated by limestone, the formation shows average water saturation of around 25.29% and hydrocarbon saturation of roughly 74.71%, indicating favorable reservoir properties. The Lockhart Formation holds promise as a reservoir rock within the Potwar Basin, though its source rock suitability is limited. This study enhances understanding of Balkassar’s geological complexities and contributes to knowledge of hydrocarbon exploration in the Potwar Basin.

Simulation of ground motion emissions from wind turbines in low mountain ranges: implications for amplitude decay prediction

The characterization and prediction of wind turbine (WT) emissions are important steps in reducing their impact on humans or sensitive technologies such as seismic stations or physics experiments. Here, WT ground motion emissions are studied along two measurement lines set up at two wind farms on the Eastern Swabian Alb, southwest Germany. The main purpose of the data analysis is to estimate amplitude decay rates from vertical component data and surface wave phase velocities excited by the permanent motion of the WT towers. Phase velocities as well as geological information serve as input to build realistic subsurface models for numerical wave field simulations. Amplitude A decay rates are characterized by b-values through Asim 1/r^b depending on distance r and are derived from peaks in power spectral density (PSD). We find an increase of b_text {PSD} with frequency from 0.5 to 3.2 for field data. For low frequencies (1.2 Hz and 3.6 Hz), b_text {PSD} ranges from 0.5 to 1.1, hence close to the geometrical spreading factor of surface waves (b_text {PSD}=1). Anelastic damping and scattering seem not to be significant at these frequencies which also shows in numerical simulations for quality factors Q=50-200. We also find that the emitted wavefields from several WTs interfere, especially in the near-field, and produce strong local ground motion amplitudes. The inclusion of a steep topography present in low mountain ranges adds more wave field distortions which can further increase the amplitudes. This needs to be considered when predicting WT induced ground motions.

Improved imaging of ground deformation and brine seepage around abandoned flooded salt mines by joint inversion of multiphysics data

Centuries of bad mine-flooding practice have left a legacy of underground salt mine collapse and contaminated discharges, the scale of which is only slowly being revealed now that ground processes have caused instability over decades to >100 years. Geophysical methods are used for mapping ground subsidence and groundwater contamination at such sites but suffer individually from non-uniqueness and with the underlying mechanism and processes not being well understood. Here, to reduce uncertainty and maximize accuracy we recover subsurface models with structural similarity enforced via crossgradients joint inversion of seismic refraction and dc resistivity data for the top 40 m at former mining areas in Carrickfergus region of Northern Ireland. The models in combination with multispectral image fusion, enable us to identify hitherto unknown mechanism of deformation and compositional changes consistent with the Chebotarev geochemical evolution of groundwater. We found evidence of concealed gravitational slump structures interpreted as being caused by water ingress weakening the gypsum-bearing marl bedrock (via gypsum dissolution and increased fluid pressures within marl) and leading to gravitational gliding deformation, expressed as curved subsidence bands and stepped thrusts coincident with surface zones of brine-mud seepage and sinkhole collapse. Our results have wide-ranging implications for best-practice assessment and management of abandoned flooded salt mines and the hazards they pose to ground stability and water resources world-wide.

Research note: Application of refraction full‐waveform inversion of ocean bottom node data using a squared‐slowness model parameterization

AbstractFull‐waveform inversion is a wave equation–based imaging technique for obtaining subsurface model parameters by matching modelled with field data. Full‐waveform inversion is often formulated as a local optimization problem in which the model parameterization influences the gradient preconditioner and the convergence rate associated with the full‐waveform inversion objective function. Model parameterization governs the radiation pattern of the so‐called secondary Born source. In this work, we assess model parameterization effects on the estimation of P‐wave velocities using a three‐dimensional acoustic time‐domain full‐waveform inversion procedure. These include the three commonly used parameterization: velocity, slowness and squared slowness. In this context, we consider a field data set from a deepwater Brazilian pre‐salt field using a recently introduced circular shot ocean bottom node acquisition which favours refracted waves. The results reveal that the squared slowness model parameterization provides a satisfactory trade‐off between the reconstruction of the deep pre‐salt target area and convergence rate, saving 50% of runtime compared to the velocity and slowness cases.

Prediction of acoustic impedance and density porosity using seismic inversion, and geostatistical methods on Krishna Godavari basin, India: A case study

Seismic inversion is a geophysical technique used to estimate subsurface acoustic impedance models from seismic reflection data. The basic concept behind seismic inversion is to establish mathematical assumptions that can relate seismic responses to geological formation properties. This study addresses two types of seismic inversion methods, namely Band-limited inversion (BLI) and Linear programming inversion (LPI) techniques for the delineation of gas reservoirs. The study presents the successful use of the inversion in a real example from a gas sand reservoir in KG-Basin, India. Inversion has led to the removal of ambiguity and revealing clear information about the target area. This work aims to use and compare the results of these two generic seismic post-stack inversion methods for characterizing the reservoir. The inversion reveals the presence of low acoustic impedance around 2320-2440 ms and is inferred as a sandstone layer with a potential hydrocarbon reservoir. One can improve the analysis of the elastic properties for reservoir characterization by using rock physics knowledge to understand better reservoir properties such as water saturation, porosity, and shale volume. This study also uses the Rock Physics Template (RPT) and plots it with elastic properties to aid the efficient interpretation. RPT helps to identify the lithology and fluid content in the area. Results from the seismic study suggest a comparatively better analysis done through the LPI technique over BLI. In the last, the Multi-attribute transform as a part of the Geostatistical method is used to predict porosity away from the borehole. The method shows a very high porosity section and clearly shows the distribution of porosity in and around the region. The interpretation shows that a very high porosity zone (15–25%) is present that is in cross-correlation with a low impedance zone (4000-8000 m/s*g/cc) and confirms the presence of a hydrocarbon zone in-between 2320 and 2440 ms two-way travel time. All seismic inversion and geostatistical methods can retrieve subsurface information with very high-resolution images but the analysis based on linear programming is found slightly better in each step and depicts good prediction results. The study finally gives a flowchart that combines seismic inversion and geostatistical methods to extract petrophysical parameters from seismic data and can help to interpret hydrocarbon-bearing formation in any virgin area.

Implementation of Asynchronous Distributed Gauss-Newton Optimization Algorithms for Uncertainty Quantification by Conditioning to Production Data

Summary Previous implementation of the distributed Gauss-Newton (DGN) optimization algorithm ran multiple optimization threads in parallel, employing a synchronous running mode (S-DGN). As a result, it waits for all simulations submitted in each iteration to complete, which may significantly degrade performance because a few simulations may run much longer than others, especially for time-consuming real-field cases. To overcome this limitation and thus improve the DGN optimizer’s execution, we propose two asynchronous DGN (A-DGN) optimization algorithms in this paper. The two A-DGN optimization algorithms are (1) the local-search algorithm (A-DGN-LS) to locate multiple maximum a-posteriori (MAP) estimates and (2) the integrated global-search algorithm with the randomized maximum likelihood (RML) method (A-DGN + RML) to generate hundreds of RML samples in parallel for uncertainty quantification. We propose using batch together with a checking time interval to control the optimization process. The A-DGN optimizers check the status of all running simulations after every checking time frame. The iteration index of each optimization thread is updated dynamically according to its simulation status. Thus, different optimization threads may have different iteration indices in the same batch. A new simulation case is proposed immediately once the simulation of an optimization thread is completed, without waiting for the completion of other simulations. We modified the training data set updating algorithm using each thread’s dynamically updated iteration index to implement the asynchronous running mode. We apply the modified QR decomposition method to estimate the sensitivity matrix at the best solution of each optimization thread by linear interpolation of all or a subset of the training data to avoid the issue of solving a linear system with a singular matrix because of insufficient training data points in early batches. A new simulation case (or search point) is generated by solving the Gauss-Newton (GN) trust-region subproblem (GNTRS) using the estimated sensitivity matrix. We developed a more efficient and robust GNTRS solver using eigenvalue decomposition (EVD). The proposed A-DGN optimization methods are tested and validated on a 2D analytical toy problem and a synthetic history-matching problem and then applied to a real-field deepwater reservoir model. Numerical tests confirm that the proposed A-DGN optimization methods can converge to solutions with matching quality comparable to those obtained by the S-DGN optimizers, saving on the time required for the optimizer to converge by a factor ranging from 1.3 to 2 when compared to the S-DGN optimizer depending on the problem. The new A-DGN optimization algorithms improve efficiency and robustness in solving history-matching or inversion problems, especially for uncertainty quantification of subsurface model parameters and production forecasts of real-field reservoirs by conditioning production data.

Abrasive machining induced surface layer damage behavior and formation mechanism of monocrystalline β-Ga2O3: A comparative study of nanoindentation and nanogrinding

Beta-phase gallium oxide (β-Ga2O3) has garnered significant attention in recent years as an ultra-wide bandgap semiconductor material. However, its surface layer damage behavior and machining deformation mechanism remain poorly understood. In this work, a systematic comparative study was conducted through nanoindentation and nanogrinding for monocrystalline β-Ga2O3 (−201) crystal plane. Transmission electron microscopy is employed as the primary characterization method. The results show that the microstructural evolution patterns of monocrystalline β-Ga2O3 can be categorized into four stages, i.e. stacking faults, twins, slip bands, and cleavage cracks are observed sequentially. According to the tests results, subsurface damage diagram models based on nanoindentation and nanogrinding are established, both of which exhibit the above evolution patterns as the indentation depth and grit depth of cut increases. However, there are some differences. In the nanogrinding process, nanocrystals and amorphous layers were found near the ground surface, which are related to the high strain rate grinding conditions and the unique material properties of monocrystalline β-Ga2O3 with low stacking fault energy. The results in this work contribute to an enhanced understanding of the deformation characteristics of monocrystalline β-Ga2O3 at macro and micro scales, while offering valuable insights for achieving damage control in ultra-precision machining processes.

The Comparison of Magpick Anomaly Contours toward Bouguer Anomaly Reduction

The subsurface structures model can be made by various methods, one of which is the gravity method. The basis of gravity modeling is the difference in density of materials. This can result in gravitational anomalies. Overall gravitational anomalies are known as bouguer anomalies. The gravity anomalies can be separated into local anomalies and regional anomalies. This research was in Mount Kelud, Kediri, Indonesia. The gravity data was processed using Surfer 9 software for the manufacture of two-dimensional contours. After that, researchers performed anomaly separation through MagPick software. In this study, researchers compared the two-dimensional contour results of MagPick gravitational anomalies toward the reduction of bouguer anomalies to regional anomalies. As a result, both show the same accurate contour.

A multi-scenario based micro seismic hazard assessment of the Bhuj City, western India incorporating geophysical and geotechnical parameters

The micro-seismic hazard assessment (micro-SHA) of the Bhuj city (highest seismic zone V) has been accomplished considering seismotectonic as well as shallow subsurface modeling through geophysical (multi-channel analysis of surface waves (MASW) survey at 19 sites) and geotechnical parameters from 28 boreholes distributed in the entire Bhuj city. The average shear wave velocity through the MASW survey at 30 m depth (VS30) is estimated in the range of 400–915 m/s (NEHRP C-type and B-type soil classes). Seismotectonically justified a total of 54 scenario-earthquakes (SEQs) along all major faults have been considered to evaluate the strong ground motion at generic soil level having a shear wave velocity of 760 m/s and depth of 12–28 m. The surface level strong motion (SM) has been estimated through ground response analysis preparing soil models along all boreholes. The PGA at the surface in Bhuj city has been estimated in the range of 528 cm/s2 to 1020 cm/s2; highest in the central-western part of the Bhuj city where clay with high plasticity is present. The mean-spectral acceleration over the study area for 0.2 s (corresponding to 1-2 story), 0.55 s (4-5 story), 1sec (high rise) and 1.25 s period (large structures) are prepared at the surface from all scenario earthquakes. The amplification of SM in the Bhuj city is computed in the range of 1.7–3.7.

Analyzing Seismicity Pattern of the Gorontalo Region and Its Surroundings in the Form of Hypocenter Relocation Using Double Difference Method

The level of seismic activity due to earthquake events in Gorontalo is strongly influenced by the tectonic system model of the Gorontalo region and the presence of a subduction zone in the Sulawesi Sea. These characteristics make the Gorontalo region have high seismic activity. To describe the pattern of seismicity in the Gorontalo region, it is necessary to conduct an analysis in the form of earthquake hypocenter relocation using the double difference (hypoDD) method. This method uses residual travel time data from each pair of hypocenters to the recording station. This study aims to analyze the results of hypocenter relocation and describe the subsurface model related to the seismicity pattern of Gorontalo and its surroundings. The data used are P and S waves for 7 years recorded at the Meteorology, Climatology and Geophysics Agency (BMKG) station. The earthquake hypocenters relocated in this study were 3,940 out of 4,598 earthquake events. The results of hypocenter relocation show a fairly good seismicity pattern obtained from good RMS values and distribution patterns following Gorontalo tectonic patterns. In general, the seismicity pattern of Gorontalo and its surroundings is strongly influenced by activity in the Sulawesi Sea subduction zone and the Gorontalo fault in the region. The seismicity caused by the subduction zone has a depth of 50 - 200 km, and due to the active faults in the Gorontalo region has a depth of 2 - 25 km.

Characterization of a naturally fractured ignimbrite reservoir: Subsurface model validated from worldwide analogue outcrops and production data

Ignimbrites are deposits found in sedimentary basins globally, commonly linked with evolved volcanism. Understanding fractured ignimbrites in sedimentary basins is crucial for various economic applications, including hydrocarbon extraction, CO2 storage, fractured aquifers, and geothermal resources. Fractured ignimbrite reservoirs typically consist of welded deposits with high transmissibility due to the fracture network, but with low matrix permeability. In this study, we demonstrate how a large-scale rhyolite eruption progressed through varying gas-particle ratios and magma discharge rates, which along with the influence of paleotopography, controlled the type and rate of deposition of pyroclastic density currents (PDCs). These factors determined the distribution and thickness of the ignimbrite unit, as well as the post-emplacement processes, such as cooling rate, welding and devitrification, which influenced the occurrence of discrete discontinuities defining the hydraulic system of the Naturally Fractured Reservoir (NFR). Fracture network is closely related to the cooling stage, composed of hybrid, fluid-assisted fractures and extensional fractures associated with fumarolic system and tensional thermo-elastic geomechanics, respectively. The subsurface model was validated through analysis of reservoir dynamic response and comparative studies of worldwide outcrop analogues, focusing on the determining factors of NFRs. In this case, tectonic fractures resulting from the structural evolution of basins may also influence reservoir conditions, albeit to a lesser extent, affecting fracture porosity and permeability of the NFR. However, these tectonic fractures played a significant role in fluid migration and reservoir charging. We conclude that an ignimbrite geobody, with a low porosity and permeability matrix (Øm ∼1 %; Km ∼0.1 mD), constitutes a NFR, where the fracture network provides a low porosity (Øf ∼0.08%) and a high anisotropic permeability of the reservoir (Kf max ∼ 2100 mD and, Kf Av ∼1500 mD). The obtained results have important implications for the study of fluids transmissibility and storage of ignimbrite reservoirs.

Soil property recovery from incomplete in-situ geotechnical test data using a hybrid deep generative framework

Geotechnical testing serves to assess the strength and stiffness of in-situ soils, for purposes such as informing foundation design. Despite its importance, time constraints, financial considerations, and site-specific limitations often restrict testing to isolated locations with limited horizontal resolution. Therefore, this paper presents a novel hybrid generative deep learning model designed to approximate soil properties across sites based on sparsely sampled geotechnical data. The model uses geological subsurface samples derived from random field theory as ‘a priori’ data for a conditional variational auto-encoder (CVAE) model. By doing so, it attempts to map the relationship between in-situ data and the corresponding spatial coordinates, as well as the inherent link between in-situ data and spatial distribution. Then, in the post-processing phase, a Kriging model interpolates minor discrepancies between the measured and predicted values. To demonstrate its practical application, this paper focuses on cone penetration testing (CPT) as the geotechnical test method. The model's development is thoroughly discussed, followed by the validation using in-situ data and an analysis conducted with synthetic data. It is shown that the uncertainty associated with CVAE-Kriging depends upon both the distance from the sample point and the site's inherent complexity. The proposed methodology not only offers refined subsurface modeling but also expands the understanding of uncertainty in geotechnical testing. Practically, it can assist geotechnical engineers with insights during the survey phase.

Feature-based magnetotelluric inversion by variational autoencoder using a subdomain encoding scheme

Magnetotelluric (MT) data inversion aims to reconstruct a subsurface resistivity model that minimizes the discrepancy between inverted and measured electromagnetic data. Conventional pixel-based minimum-structure inversion often yields a smoothed-out reconstruction with a relatively low resolution. A priori geophysical knowledge can be embedded into inversion and improve the reconstruction resolution through proper reparameterization. However, existing reparameterization approaches, such as model-based and parametric transform-based inversion, have limited ability to incorporate various a priori information. The effectiveness of existing deep generative model-based inversion algorithms is still debatable when applied to scenarios with complex backgrounds. We develop a feature-based MT data inversion method based on a variational autoencoder (VAE) with a subdomain encoding scheme. Instead of encoding the entire domain of an investigation, we adopt a 1D subdomain encoding scheme to encode the 1D resistivity-depth models using a single VAE. The latent variables for the 2D model are a combination of the latent variables for 1D models, and the encoded region of interest (ROI) can be flexibly determined. The latent variables of ROI and the pixels outside the ROI are simultaneously inverted using the gradient-descent method. Our 1D subdomain encoding scheme reduces the complexity and diversity of the data set, and it can flexibly embed a priori knowledge with various uncertainties. Synthetic data inversion and inversion of the Southern African Magnetotelluric Experiment field data validate our method’s ability to effectively improve inversion accuracy and resolution.

Simultaneous waveform inversion of seismic-while-drilling data for P-wave velocity, density, and source parameters

Full-waveform inversion (FWI), as an optimization-based approach to estimating subsurface models, is limited by incomplete acquisition and illumination of the subsurface. The incorporation of additional data from new and independent raypaths should be expected to result in significant increase in the accuracy of FWI models. In principle, seismic-while-drilling (SWD) technology can supply these additional raypaths; however, it introduces a new suite of unknowns, namely precise source locations (i.e., drilling path), source signature, and radiation characteristics. A new FWI algorithm is formulated in which the source radiation patterns and positions join the velocity and density values of the grid cells as unknowns to be determined. Several numerical inversion experiments are then conducted with different source settings using a synthetic model. The SWD sources are supplemented by explosive sources and multicomponent receivers at the surface, simulating a conventional surface acquisition geometry. The subsurface model and SWD source properties are recovered and analyzed. The analysis is suggestive that SWD involvement can enhance the accuracy of FWI models, with varying degrees of enhancement depending on factors such as trajectory inclination, source density, and drill path extension. The impact of SWD-FWI over standard FWI is reduced when low-frequency data are missing, but improvements over the models constructed with no subsurface sources remain. This formulation permits general source information, such as position and moment tensor components, to be independently obtained. This inversion scheme may lead to a range of potential applications for which medium properties and source information are required.

The utilization of ghost reflections retrieved by seismic interferometry for layer‐specific characterization of the shallow subsurface

AbstractSeismic interferometry (SI) retrieves the Green function between two receiver locations using their recordings from a boundary of sources. When using sources and receivers only at the surface, the virtual‐source gathers retrieved by SI contain pseudo‐physical reflections as well as ghost (non‐physical) reflections. These ghost reflections are the results of the cross‐correlation or auto‐correlation (AC) of primary reflections from two different depth levels, and they contain information about the seismic properties of specific layers in the subsurface. We investigated the application of ghost reflections for layer‐specific characterization of the shallow subsurface using SI by AC. First, we showed the technique's potential using synthetic data for a subsurface model with a lateral change in velocity, a gradient in depth for velocity, a thickness change and a velocity change of the target layer. Then, we applied the technique to shallow subsurface field data. We also focused on improving the retrieval of ghost reflections by removing the free‐surface multiples and muting undesired events in active‐source gathers before applying SI. Our results demonstrate that the ghost reflections can be used advantageously to characterize the layer that causes them to appear in the results of SI. Consequently, they can also provide valuable information for imaging and monitoring shallow subsurface structures.