Subsurface Structure Research Articles

Seismic Design of Underground Structures’ Vulnerability – Review.

This article examines the vulnerability of underground structures to seismic activity, with a particular focus on design and mitigation strategies. Underground buildings are essential to the development of modern infrastructure and must include tunnels, subway stations, and storage facilities. Because of its deep location, seismic design and earthquake performance are both more challenging. In this study of seismic susceptibility, topics such as ground motion characterization, soil-structure interaction, structural reaction, and failure mechanisms specific to underground structures are discussed. According to the paper, their susceptibility can be affected by geotechnical conditions, the kind of structures they have, and the construction methods that are used. The seismic stability design and analysis process involves measuring the dynamic response of subsurface structures to seismic loads. The goal is to keep earthquake and seismic forces from toppling structures in any way that is possible. Advanced analytical and numerical approaches are used in seismic stability design. Some examples of these methods are finite element analysis (FEA), discrete element modeling (DEM), and the boundary element method (BEM). In addition to that, it investigates contemporary methodologies for assessing seismic vulnerability as well as international design standards. In addition, seismic retrofitting and design advances for underground structures are analyzed and discussed in this paper. This document compiles and evaluates previous research to gain an understanding of the seismic susceptibility of subsurface structures and to promote more seismic engineering research on this extremely important topic.

In vivo evaluation of complex polyps with endoscopic optical coherence tomography and deep learning during routine colonoscopy: a feasibility study.

Standard-of-care (SoC) imaging for assessing colorectal polyps during colonoscopy, based on white-light colonoscopy (WLC) and narrow-band imaging (NBI), does not have sufficient accuracy to assess the invasion depth of complex polyps non-invasively during colonoscopy. We aimed to evaluate the feasibility of a custom endoscopic optical coherence tomography (OCT) probe for assessing colorectal polyps during routine colonoscopy. Patients referred for endoscopic treatment of large colorectal polyps were enrolled in this pilot clinical study, which used a side-viewing OCT catheter developed for use with an adult colonoscope. OCT images of polyps were captured during colonoscopy immediately before SoC treatment. A deep learning model was trained to differentiate benign from deeply invasive lesions for real-time diagnosis. 35 polyps from 32 patients were included. OCT imaging added on average 3:40min (range 1:54-8:20) to the total procedure time. No complications due to OCT were observed. OCT revealed distinct subsurface tissue structures that correlated with histological findings, including tubular adenoma (n = 20), tubulovillous adenoma (n = 10), sessile serrated polyps (n = 3), and invasive cancer (n = 2). The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.984 (95%CI 0.972-0.996) and Cohen's kappa of 0.845 (95%CI 0.774-0.915) when compared to gold standard histopathology. OCT is feasible and safe for polyp assessment during routine colonoscopy. When combined with deep learning, OCT offers clinicians increase confidence in identifying deeply invasive cancers, potentially improving clinical decision-making. Compared to previous studies, ours offers a nuanced comparison between not just benign and malignant lesions, but across multiple histological subtypes of polyps.

Subsurface microbial community structure shifts along the geological features of the Central American Volcanic Arc.

Subduction of the Cocos and Nazca oceanic plates beneath the Caribbean plate drives the upward movement of deep fluids enriched in carbon, nitrogen, sulfur, and iron along the Central American Volcanic Arc (CAVA). These compounds fuel diverse subsurface microbial communities that in turn alter the distribution, redox state, and isotopic composition of these compounds. Microbial community structure and functions vary according to deep fluid delivery across the arc, but less is known about how microbial communities differ along the axis of a convergent margin as geological features (e.g., extent of volcanism and subduction geometry) shift. Here, we investigate changes in bacterial 16S rRNA gene amplicons and geochemical analysis of deeply-sourced seeps along the southern CAVA, where subduction of the Cocos Ridge alters the geological setting. We find shifts in community composition along the convergent margin, with communities in similar geological settings clustering together independently of the proximity of sample sites. Microbial community composition correlates with geological variables such as host rock type, maturity of hydrothermal fluid and slab depth along different segments of the CAVA. This reveals tight coupling between deep Earth processes and subsurface microbial activity, controlling community distribution, structure and composition along a convergent margin.

Evaluating the Effect of Subsurface Structures on Freshwater Lenses in Coral Reefs

Evaluating the Effect of Subsurface Structures on Freshwater Lenses in Coral Reefs

Hybrid optimization for DC resistivity imaging via intelligible-in-time logic and the interior point method.

Geophysical DC resistivity imaging is crucial in subsurface exploration, environmental studies, and resource assessment. However, traditional inversion techniques face challenges in accurately resolving complex subsurface features because of the inherent nonlinearities in geophysical data. To overcome these challenges, we propose a hybrid optimization approach that combines incomprehensible but intelligible-in-time (IbI) logic with the interior point method (IPM). The IbI logic framework leverages complexity and temporal intelligibility, allowing for a dynamic interpretation of subsurface phenomena. By integrating IbI with IPM, our approach benefits from global exploration and local refinement, leading to improved convergence speed and solution quality. The objective function formulated for the inversion process includes data misfit and model regularization terms, which promote accurate and smooth solutions. Our methodology involves the use of the IbI logic algorithm (ILA) for initial global search, which identifies promising regions in the search space. This is followed by the application of IPM for local optimization. This synergy between the two algorithms ensures robustness and efficiency in handling large datasets and complex geological models. We conducted tests using synthetic and real DC resistivity data to validate our approach. The synthetic test demonstrated the accurate reconstruction of subsurface anomalies, whereas the real data test successfully identified fault zones, which is consistent with previous studies. The hybrid optimization algorithm significantly improves the resolution of subsurface structures and enhances geophysical data inversion practices. It balances the exploration and refinement phases effectively, optimizing the computation time and ensuring precise model delineation.

The new insight of tectonic setting in Sunda–Banda transition zone using tomography seismic. Case study: 7.1 M deep earthquake 29 August 2023

Abstract The Sunda–Banda arc transition zone features the collision of the Indo-Australian oceanic plate and the Australian continent, resulting in intricate geological and geodynamic conditions. Tectonic activity in this region is shaped by the convergence of multiple major plates, including the Indo-Australian oceanic plate and the Eurasian plate. The crustal structure along the Sunda–Banda arc transition zone is complex and influenced by various factors such as subduction, continental collision, and volcanic activity. The tectonic complexity of the region in eastern Indonesia makes it an interesting area for study. In this research, International Seismological Centre-Engdahl-van der Hilst-Buland catalogue data from 1964 to 2020 were used, which include recorded information on 69.705 earthquake events from 1.185 recording stations and consist of 2.943.974 P phases. Resolution testing was performed using various velocity grids, and optimal results were obtained with a medium resolution of ∼100 km × 100 km × 80 km for the inversion process. The tomographic inversion analysis provided valuable insights into subsurface structures within Earth’s crust and mantle up to a depth of approximately 750 km. The occurrence of deep earthquakes in the study area has provided valuable insights into complex dynamics associated with subduction and plate tectonics. The results of the tomographic inversion analysis reveal that earthquakes are concentrated in areas with high-velocity anomalies, indicating intense tectonic activity near the subduction zone. This study offers the perspective on the structural complexities and earthquake origins in the Sunda–Banda arc transition zone following the 2023 Mw 7.1 Bali Sea earthquake, which occurred on August 29, 2023, at 02:55:32 UTC + 7, approximately 163 km northeast of Lombok, Indonesia. This earthquake was caused by slab pull activity from the Australian Plate and involved a combination of downward and oblique-normal movement. These characteristics indicate the convergence and interaction between tectonic plates in the subduction process occurring in the Bali Sea area. As a result, there have been frequent occurrences of various tectonic and volcanic activities including earthquakes of different magnitudes. These results highlight the significance of the high-velocity anomaly connected to this occurrence, offering valuable insights into seismic behaviour and tectonic phenomena in the region. The findings of this study indicate that the deep earthquakes in the Bali Sea may be induced by faulting due to the transformation of metastable olivine into denser spinel at significant depths, along with shear instability caused by phase transitions within Earth’s mantle layers. This theory proposes that stress-induced changes in phase can initiate shear instabilities and subsequently lead to deep earthquakes.

Galvanic distortion decomposition of magnetotelluric impedance tensors in 1-D electrical anisotropic media

SUMMARY The electromagnetic (EM) local distortion of the transfer function due to shallow small-scale inhomogeneities hinders the accurate interpretation of magnetotelluric (MT) data. Under the assumption that regional subsurface structures are electrically isotropic, decomposition techniques of the MT impedance tensor that focus on the galvanic distortion of the electric field have been well developed. In this paper, we present a decomposition method of MT impedance tensors over a regional 1-D conductivity anisotropic Earth, in which the galvanic distortion of both the electric and the associated magnetic fields are taken into account. An eigenparameter analysis is introduced to evaluate the intrinsic indeterminacy of magnetic distortion parameters. Regional anisotropic responses and EM distortion parameters are resolved by using a modified BFGS (Broyden–Fletcher–Goldfarb–Shanno) quasi-Newton algorithm combined with phase tensor analysis and the trust region method. In the presence of near-surface anisotropic inhomogeneities, synthetic 2-D data show an accentuated magnetic galvanic effect in the response tensors especially in the diagonal components probably due to the infinite extension in the strike direction. However, the magnetic galvanic distortion is not significant in synthetic 3-D models. The decomposition scheme is also applied to the analysis of the BC87 data set collected in southeastern British Columbia to investigate both the electric and magnetic field galvanic distortion.

Imaging Complex Structures of the Los Angeles Basin via Adjoint-State Travel-Time Tomography

ABSTRACT In this study, we present high-resolution seismic velocity models for the Los Angeles basin (LAB) and its adjacent area using adjoint-state travel-time tomography, fitting an extensive database of P- and S-wave travel times accumulated from 1980 to 2021. We select 151,193 first P-wave travel times and 149,997 first S-wave travel times from local earthquakes archived in the Southern California Earthquake Data Center to determine the velocity models, with earthquake locations updated at each iteration. With seismic stations spaced more than 3.5 km apart, our dataset has limited resolution in the uppermost 1–2 km. However, starting from three different initial models, our VP models, which are optimally imaged between 3 and 15 km, show similar velocity heterogeneity and provide a better fit to the observed first travel-time data compared to the Community Velocity Model-Harvard 15.1.0 and Community Velocity Model-Southern California Earthquake Center 4.26. Our models provide a detailed delineation of the subsurface structure beneath the LAB, revealing significant velocity variations across active faults, a 10-km-thick sequence of sedimentary rocks within the basin, and a distinct basin margin marked by transitions from low to high-velocities. In addition, these models highlight basement structures with elevated VP and VS located at depths of 9 to 12 km and beyond. Specifically, beneath the northeastern part of the basin, the models demonstrate improved accuracy and reliability in reflecting the linear relationship between VP and VS in mafic rocks. The accurate delineation of the basin’s structure provided by our models could also offer robust constraints for seismic response modeling and seismic hazard assessment in the region.

Multidisciplinary high resolution Geophysical Imaging of Pantano Ripa Rossa Segment of the Irpinia Fault (Southern Italy)

The Irpinia Fault, also known as the Monte Marzano Fault System, located in the Southern Apennines (Italy), is one of the most seismically active structures in the Mediterranean. It is the source of the 1980, Ms 6.9, multi-segment rupture earthquake that caused significant damage and nearly 3,000 casualties. Paleoseismological surveys indicate that this structure has generated at least four Mw ~ 7 surface-rupturing earthquakes in the past 2 ka. This paper presents a comprehensive, high-resolution geophysical investigation focused on the southernmost fault segment of the Monte Marzano Fault System, i.e., the Pantano-Ripa Rossa Fault, outcropping within the Pantano di San Gregorio Magno intramontane basin. The project, named TEst Site IRpinia fAult (TESIRA), was supported by the University of Napoli Federico II to study the near-surface structure of this intra-basin fault splay that repeatedly ruptured co-seismically in the past thousands of years. Our imaging approach included 2D and 3D electrical and seismic surveys, gravimetry, 3D FullWaver electrical tomography, drone-borne GPR and magnetic surveys, and CO2 soil flux assessment across the surface rupture. This multidisciplinary investigation improved our understanding of the basin shallow structure, providing an image of a rather complex subsurface fault and basin geometry. Seismic data suggest that fault activity at the Pantano segment of MMFS is characterized by a near-surface cumulative displacement greater than previous estimations, calling into question earlier assumptions about the timing of its activation. Despite some challenges with our drone-mounted survey equipment, the integrated dataset provides a comprehensive and reliable image of the subsurface structure. This work demonstrates the utility of developing an integrated approach at high-resolution geophysical imaging and interpretation of fault zones with weak morphological expressions.

Upscaling the contribution of crab burrows to mangrove ecosystem functioning in French Guiana (South America)

Burrow characteristics and bioturbation activities of benthic organisms play a key role in mangrove ecosystem biogeochemical and sedimentary functioning. In this study, we aimed to understand how small-scale topographic variations in a mud bank might influence burrow morphology and distribution in a French Guiana pioneer mangrove system (Sinnamary estuary), and to upscale spatial patterns using remote sensing. We used burrow resin casting and sediment conductivity measurements to depict subsurface 3D burrow structures. We found that the spatial heterogeneity of burrow sizes (small, medium, large) and morphologies (simple I- and J-shape, complex geometries) depended on the geomorphic units within mudflats (platform, channel, depression). The aperture areas of burrow casts were used to predict the volume and complexity of each burrow type, enabling us to use drone-derived burrow opening distribution maps to calculate burrow volumes and complexity at the mudflat scale. There are clear associations between tidal channels and depressions and voluminous, multi-aperture, complex and multi-species burrows. In contrast, simple I- and J-shaped burrows inhabited by single species were mainly found on platforms. These relationships lead to a comparatively large volume of tidal-irrigated and deeply aerated sediments connected to channels and depressions compared to platform areas. We suggest that, depending on their morphology and connectivity with the topography, burrows may exhibit differences in biogeochemical functioning depending on the geomorphic unit. We warn against generalizing the functioning of mangrove ecosystems across geomorphic units where impacts may differ. Further studies are needed to understand how different burrow morphologies and life cycles may alter hydrological, sedimentary and biogeochemical functioning.

Characterizing Subsurface Structures From Hard and Soft Data With Multiple‐Condition Fusion Neural Network

AbstractAccurately inferring realistic subsurface structures poses a considerable challenge due to the impact of morphology on flow and transport behaviors. Traditional subsurface characterization relies on two primary types of data: hard data, derived from direct subsurface measurements, and soft data, encompassing remotely sensed geophysical information and its interpretation. Existing deep‐learning‐based methodologies predominantly focus on the transition from multiple observations to subsurface structures. However, implicit non‐linear correlations among diverse data sources often remain underutilized, leading to potential bias and errors. In this study, we introduce a multiple‐condition fusion network (MCF‐Net) to characterize subsurface structures based on both hard and soft data. To harness the full potential of multiple‐source subsurface observations, two distinct neural networks extract implicit features from hard and soft data. The integration of these features is achieved through multiple‐condition fusion blocks, designed to capture representative characteristics. These blocks are also adept at reconstructing heterogeneous structures and facilitating hydrological parameterization. MCF‐Net exhibits accuracy in estimating subsurface structures across various types of subsurface observations. Experimental results underscore the utility and superiority of MCF‐Net in applications of hydrogeological modeling.

Analysis of Earthquake Vulnerability of The Demak Coastal Area based on the HVSR (Horizontal To Vertical Spectral Ratio) Method

<p>Most of the Demak Coastal area, especially on the north coast, has a subsurface structure which is quite thick sediment. The impact of an earthquake will cause damage to subsurface structures and buildings above it, so a method is needed that can determine earthquake vulnerability in an area to take mitigation steps. This research aims to analyze vulnerability to earthquakes based on natural frequency (fo), amplification (Ao) and seismic vulnerability index (SVI). The research was carried out by measuring the microseismic signal response at 89 locations using a 3-component seismograph and data logger. The research results showed that the dominant frequency varied from 0.26 – 5.26 Hz, the amplification factor varied from 0.51 – 3.56 and the seismic susceptibility index varied from 0.14-14.77 micro cm<sup>2</sup>/s. The study area was classified into low SVI (SVI < 5), medium SVI (5 < SVI < 10), and high SVI (SVI > 10). The potential earthquake hazard described by SVI with a value range of 10 < SVI (high classification) is found at observation station 60 (Bedono Hamlet) and observation station 70 (Karangwaru Hamlet Cemetery)</p>

A two-dimensional magnetotelluric deep learning inversion approach based on improved Dense Convolutional Network

Magnetotelluric (MT) inversion is an important means of MT data interpretation. The use of deep learning technology for MT inversion has attracted much attention because it is not limited to the initial model, avoids falling into local optimal solutions, and has the strong ability to process large amounts of data. However, obtaining highly reliable deep learning inversion results remains a challenge. In this paper, we have proposed a two-dimensional (2-D) MT inversion method based on the improved Dense Convolutional Network (DenseNet), with the aim of improving the reliability of the 2-D deep learning MT inversion results. First, the MARE2DEM is used to compute the 2-D MT forward responses when establishing the sample set. Then, an improved DenseNet is proposed by incorporating depthwise separable convolution in lieu of standard convolution within dense connection blocks, and embedding the attention mechanism. Depthwise separable convolution splits the standard convolution operation into depthwise and pointwise convolution, effectively capturing spatial features of input data and correlations between channels. Meanwhile, attention mechanism allows the network to assign varying degrees of importance (or attention) to different elements in a sequence of data, thus enhancing its ability of key feature extraction. This design not only retains the inherent feature reuse and alleviates gradient vanishing of DenseNet but also further enhances network performance. The optimized network parameters for the improved DenseNet are obtained by training on the training set, while the validation set is used to adjust hyperparameters and evaluate model performance. Finally, the proposed 2-D deep learning approach is verified by using both synthetic and field data. Experimental results with synthetic data show that the reliability of inversion results obtained by using the proposed algorithm is improved, and the inversion results obtained by using both TE- and TM-mode data is more accurate than those obtained by using the single mode data. The inversion results of field data show that the proposed 2-D MT deep learning inversion approach can effectively detect the subsurface resistivity structure and has a good application prospect.

Development of a 3D Geologic Model Used in the Seismic Hazard and Liquefaction Hazard Analysis of Madison County, Tennessee

ABSTRACT Madison County of western Tennessee is approximately 100 km southeast of the New Madrid seismic zone and, thus, is subject to potentially dangerous seismic shaking and liquefaction events. Holocene floodplain alluvium and associated Pliocene/Pleistocene terraces of the Forked Deer and Hatchie Rivers cover a large part of Madison County. Underlying these unconsolidated sediments are approximately ∼650 m of poorly consolidated Paleogene and Cretaceous terrestrial and marine sediments, which are underlain by Paleozoic limestone bedrock. Uniform regional geology used in the 2014 United States Geological Survey seismic hazard maps assumes a homogenous seismic velocity structure above bedrock. Three-dimensional geologic mapping of the ∼700 m of Quaternary, Neogene, Paleogene, and Cretaceous sediments conducted in this study provides a more detailed velocity structure, resulting in a more accurate and detailed map of the expected ground motions in Madison County during future earthquakes. In addition, the surface geologic map reveals regions that are susceptible to liquefaction. This paper discusses the methods used to construct the surface geology map, subsurface structure contour maps, isopach maps, and 3D geologic model used in the seismic hazard and liquefaction hazard analyses of Madison County, Tennessee, and summaries of the results of these analyses are presented.

Improving ambient noise crosscorrelations using a polarization-based azimuth filter

Seismic interferometry is widely used to image and monitor earth structures at different scales by extracting an empirical Green’s function (EGF) from the crosscorrelation of ambient noise under the assumption of diffuse wavefields or energy equipartitioning. However, EGFs may be incorrectly estimated and lead to a misunderstanding of subsurface structures because the distribution of ambient noise sources is neither isotropic nor stationary. To extract reliable EGFs from the nonideal ambient noise data, we develop a polarization-based azimuth filter (PAF) to attenuate the ambient noise energy of nonstationary sources to improve the quality of crosscorrelation functions. The PAF is a time-frequency domain azimuth filter for 3C seismic data, which uses time-frequency polarization analysis to measure the azimuths of ambient noise signals and then filters the 3C data to obtain the signals from desired directions. Based on the stationary-phase theory, we use the PAF to extract the ambient noise signals from stationary-phase zones, which improves the quality of the crosscorrelation function and eliminates the requirement for long observations. A synthetic test and two short-time engineering examples demonstrate that uneven source distributions might cause serious spurious signals in EGFs, and we demonstrate the improvement in EGFs using our method. In addition, the PAF may allow for the complete separation of the fundamental and higher modes of Rayleigh waves, which uncovers more information implicit in the ambient noise data.

Analytical solutions of ray-tracing equations in generalized elliptical anisotropy

Ray-tracing in anisotropic media is pivotal for interpreting observed seismic data and creating high-resolution images of subsurface structures, which are crucial in exploration geophysics. Elliptical anisotropy, a simplified model that approximates a transversely isotropic medium, is particularly relevant for geologic settings such as shale formations or stressed sedimentary layers where directional dependencies of seismic velocities are pronounced. We develop an analytical solution of the ray-tracing equations for a 2D inhomogeneous and anisotropic medium, where velocities depend elliptically on direction and increase linearly with depth — a scenario frequently encountered in stratified geologic formations. Unlike previous studies that assume constant ellipticity throughout the medium, our approach allows for variations in ellipticity, providing a more flexible and realistic representation of subsurface anisotropy. The phase velocities along the x- and z-axes are not necessarily multiples of each other at every point, offering a generalized version of the elliptical anisotropy. This enhancement may enable more accurate predictions and interpretations of observed seismic data, particularly in complex exploration scenarios. The analytical solution yields expressions for the ray paths and the wavefront normals. By setting the normals of the wavefront at the seismic source point and the location of the seismic source as the initial conditions in phase space, we explore the evolution of these wavefront normal curves across different types of the elliptical anisotropy. Our innovative approach includes plotting the evolution of wavefront normal curves on the generalized momentum coordinate plane of the phase space — that commonly overlooked in traditional models focused only on position coordinates.

Seismic random noise suppression via mining multi-scale local and global information

Suppressing random noise is critical for revealing real subsurface structures. Convolutional neural networks (CNNs), the leading seismic data denoising methods, excel at extracting local features but struggle to capture global representations. Unet can extract and reuse multi-scale features, aiding in the precise detection of details and semantic information; however, being based on convolutional operations, it struggles to capture global information. To capture global representations, researchers normally employ Transformers in high-level visual tasks, owing to their self-attention mechanisms. This paper introduces a method for mining multi-scale local and global information based on hybrid-gated Unet (HGUnet), which integrates Transformer, CNN, and Unet architectures to enhance the feature representation capability for seismic random noise suppression tasks. HGUnet comprises hybrid-gated blocks (HGB) embedded within a U-shaped architecture, employing a concurrent structure of Octave convolution and lightweight multi-head self-attention mechanism to efficiently extract multi-scale local and global features simultaneously. Moreover, at the conclusion of the HGB, to precisely leverage information and reduce computing costs, a gated feedforward network is designed to retain valuable information and prune redundancies for feature fusion. Synthetic and field experimental results demonstrate that HGUnet improves denoising quality over traditional and CNN methods without adding significant computing costs.

Estimation of Subsurface Structure Using Euler Deconvolution Method of Magnetic Data at the Geothermal Area of Sonai Village and its Surroundings, Konawe Regency

It has been carried out research with the aim of determining the subsurface structure at the geothermal area of Sonai Village and its surroundings, Konawe Regency. The data used are magnetic data obtained through field measurements at 126 points in the N180oS direction. After the data were subjected to diurnal and IGRF corrections, a residual (local) magnetic field anomaly of around -150 nT to 90 nT was obtained. On the residual magnetic anomaly map which has been reduced to the Pole (RTP), the Euler Deconvolution (ED) method is applied to the Index Structure N=0 to estimate the subsurface structure in the form of the presence of minor faults, and it is known that there are 5 minor faults at a depth of around 9 to 38 meters. Information on the existence of these faults is then used in 2D modeling. Modeling results show that these minor faults cut through two rock layers, which are the layers composed of conglomerate rocks from the Alangga Formation and peridotites as bedrock from the Ultramafic Complex. One of the minor faults closest to the manifestation area (hot spring) is at coordinates around 4o1’12.412” S and 122o7’24.263” E to 4o1’15.532” S and 122o7’19.561” E with a distance of ±15 meters. The existence of these minor faults is thought to be the migration routes for heat flow or conduction to the surface at the geothermal area of Sonai Village and its surroundings.

SwinInver: 3D data-driven seismic impedance inversion based on Swin Transformer and adversarial training

As deep learning becomes increasingly prevalent in seismic impedance inversion, 3D data-driven approaches have garnered substantial interest. However, two critical challenges persist. First, existing methodologies predominantly rely on Convolutional Neural Networks (CNNs), which, due to the inherent locality of convolutional operations, are inadequate in capturing the global context of seismic data. This limitation notably hinders their performance in inverting complex subsurface structures, such as salt bodies. Second, the current inversion frameworks are prone to overfitting, particularly when trained on limited seismic datasets. To address these challenges, we propose SwinInver, a novel backbone network that integrates the Swin Transformer as its fundamental unit, coupled with a high-resolution network design to facilitate comprehensive global modeling of intricate subsurface structures. Furthermore, we incorporate adversarial training to enhance the inversion process and effectively mitigate overfitting. Experimental evaluations demonstrate that SwinInver significantly surpasses conventional CNN-based approaches in both synthetic and field data scenarios, providing a more accurate and reliable framework for seismic impedance inversion.

Geophysical characterisation of subsurface structures related to gold mineralisation in the Mpambaa area, SE of Iramba–Sekenke Greenstone Belt, Tanzania

Geophysical characterisation of subsurface structures related to gold mineralisation in the Mpambaa area, SE of Iramba–Sekenke Greenstone Belt, Tanzania