Geophysical Prospecting最新文献

{"title":"The Tangquan Geothermal System Inferred From the Controlled Source Audio-Frequency Magnetotelluric Survey","authors":"Meigen Zhang","doi":"10.1111/1365-2478.70253","DOIUrl":"https://doi.org/10.1111/1365-2478.70253","url":null,"abstract":"<div>\u0000 \u0000 <p>The Tangquan geothermal field in Zunhua County, Hebei Province, China, has been renowned since the Tang dynasty (618–907 AD) for its ancient hot spring, which disappeared due to the proliferation of anthropogenic wells. The geothermal wells in this area range in depth from tens of metres to 400 m, with water temperatures between 30°C and 67°C. Prior to this study, no effective high-resolution geophysical surveys, such as electromagnetic (EM) exploration, had been conducted, resulting in limited constraints on the subsurface structure and conceptual understanding of the geothermal system. In this study, controlled-source audio-frequency magnetotelluric (CSAMT) surveys were conducted along five profiles with a total length of 9 km and 180 soundings to characterize the electrical structure and identify potential fluid-conducting pathways. The inverted resistivity profiles reveal a main fault controlling the geothermal field and detect a low-resistivity zone in the shallow subsurface, which is interpreted as the fracture zone filled with thermal water. We infer that groundwater gathers in the fault and gets heated. The resulting lower density hot water then ascends along the fault to shallower depths, where it is stored in the mixed contact zone of fractured granite and metamorphic rocks, forming the Tangquan geothermal field. An updated conceptual model of the field is therefore proposed. Subsequently, three deep wells were drilled, with depths ranging from approximately 600 to 1100 m. The geological conditions encountered during drilling were in excellent agreement with the predictions, and the water temperature and yield are satisfactory. One well produces a flow rate exceeding 100 m³/h at a temperature above 70°C, making it the most productive geothermal well in the area. The other two wells yield flow rates of approximately 40–50 m³/h at a temperature of about 50–60°C. The results demonstrate the effectiveness of CSAMT for imaging deep fracture-controlled geothermal reservoirs and provide a transferable exploration model for geothermal systems hosted in granitic terrains.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meta-Learning-Enhanced Implicit Full Waveform Inversion","authors":"Zefeng Wang,&nbsp;Shijun Cheng,&nbsp;Weijian Mao,&nbsp;Wei Ouyang,&nbsp;Huanhuan Tang","doi":"10.1111/1365-2478.70248","DOIUrl":"https://doi.org/10.1111/1365-2478.70248","url":null,"abstract":"<div>\u0000 \u0000 <p>Implicit full waveform inversion (IFWI) introduces implicit neural representations to parameterize the subsurface velocity model as a continuous function of spatial coordinates, which alleviates the dependence on the initial model and improves inversion flexibility. However, IFWI still requires a large number of iterative updates for each new exploration area, leading to slow convergence, high computational cost and a lack of mechanisms to share prior knowledge across different geological settings, thereby limiting its efficiency and generalization capability. To further accelerate convergence and enhance cross-area generalization, we propose a meta-learning-based IFWI method, referred to as meta-learning-enhanced implicit full waveform inversion (Meta-IFWI). In this framework, the subsurface velocity model is represented using an implicit neural network with periodic activation functions (SIREN), while a meta-learning strategy is employed to pretrain a single network on multiple velocity inversion tasks. Through this process, the network learns shared inversion priors and rapid adaptation strategies across different geological scenarios. For a new inversion task, the meta-trained initialization enables Meta-IFWI to adapt to the observed seismic data with fewer gradient updates than randomly initialized IFWI. Numerical experiments conducted on the in-distribution layered and Overthrust models and the out-of-distribution Marmousi 2 model demonstrate that Meta-IFWI achieves higher final reconstruction accuracy under the same iteration budget and substantially reduces the online inversion time required to reach the velocity-model error obtained by IFWI. Additional experiments conducted at three Gaussian-noise levels further demonstrate that Meta-IFWI consistently retains higher reconstruction accuracy and structural fidelity than IFWI for both in-distribution and out-of-distribution models.</p></div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Eikonal-Based Angle-Domain Least-Squares Kirchhoff Depth Migration in General Anisotropic Media","authors":"Paulo H. B. Alves,&nbsp;Wei Zhang,&nbsp;Mauricio D. Sacchi,&nbsp;Marco Cetale","doi":"10.1111/1365-2478.70249","DOIUrl":"https://doi.org/10.1111/1365-2478.70249","url":null,"abstract":"<p>Least-squares migration (LSM) is an inversion-based imaging method that seeks a reflectivity model whose predicted data best fit the recorded seismic data in a least-squares sense. However, conventional LSM can be unreliable for anisotropic reflection data if reflection angles are neglected. First, ignoring anisotropic parameters often produces defocused images and biased kinematics. Second, conventional LSM is generally more sensitive to migration-velocity errors than its extended-angle counterpart. To address these limitations, we propose an angle-domain least-squares Kirchhoff depth migration (ADLSKDM) framework that explicitly accounts for general anisotropic effects via angle-dependent linear inversion. Our workflow combines (i) a semi-analytical eikonal solver that efficiently computes traveltimes in complex models under weak anisotropy, and (ii) a regularized least-squares inversion that suppresses artefacts in angle-domain common-image gathers (ADCIGs). Numerical tests on anisotropic synthetic datasets demonstrate that ADLSKDM retrieves correct angle-dependent kinematics with reduced ambiguity and fewer artefacts than conventional LSM.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.70249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancement-Constrained Parsimonious Stochastic Magnetic Inversion Framework for UXO Localization","authors":"Hanbing Ai,&nbsp;Yunus Levent Ekinci,&nbsp;Roman Pašteka,&nbsp;Ahmad Alvandi,&nbsp;Yatong Cui","doi":"10.1111/1365-2478.70246","DOIUrl":"https://doi.org/10.1111/1365-2478.70246","url":null,"abstract":"<div>\u0000 \u0000 <p>Magnetic surveying is widely used for unexploded ordnance (UXO) detection. However, reliable target localization and characterization remain challenging because of noise, orientation-dependent magnetic responses, elongated target geometries and inversion non-uniqueness. In this context, we present an enhancement-constrained stochastic magnetic inversion workflow that uses anomaly-enhancement results as spatial prior information to restrict the admissible model space before optimization. First, magnetic anomalies are denoised using a modified non-local means approach to suppress high-frequency noise that would otherwise be amplified during derivative-based processing. Modified local-phase filters are then used to delineate laterally confined target zones, whereas approximate depth estimates constrain the vertical search range. The resulting spatial bounds are incorporated into a parsimonious stochastic inversion based on the Hunger Games Search algorithm, in which UXO responses are approximated by uniformly magnetized equivalent spheres for computational efficiency. The workflow is tested using noisy synthetic data generated from elongated UXO-like sphero-cylinders. The intentional mismatch between the forward-model geometry and the equivalent-sphere inversion model provides a more demanding assessment of robustness to geometric simplification. Normalized source strength (NSS)-based Euler deconvolution (NSS-Euler) is evaluated as an independent localization benchmark and is not incorporated into the proposed inversion workflow. The proposed framework is then applied to magnetic data acquired over targets with known positions and orientations at a controlled test site in Slovakia. Compared with otherwise identical unconstrained inversions, the derived spatial constraints improve target localization, reduce solution ambiguity and increase the consistency of the recovered models. NSS-Euler provides an independent estimate of target location and depth, whereas the constrained stochastic inversion yields more accurate localization and depth estimates while additionally recovering effective-magnetization and other source-related parameters. Although this simplified representation cannot reproduce all geometric details of real UXO bodies, it offers a practical and computationally efficient framework for localization-oriented interpretation. The proposed strategy may also be applicable to other compact-target-detection problems in which enhancement-derived spatial information can guide inversion.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalized Matched Filtering for Improving Seismic Section Clarity: Incorporating Frequency-Based Regularization","authors":"Amin Kahrizi","doi":"10.1111/1365-2478.70254","DOIUrl":"https://doi.org/10.1111/1365-2478.70254","url":null,"abstract":"<div>\u0000 \u0000 <p>Seismic data resolution is crucial for subsurface imaging. But factors such as the finite bandwidth of seismic sources, wavelet interference and noise often degrade the clarity of reflectors, making their detection challenging. Although recent advances in seismic data processing methods have shown promise in enhancing resolution, they remain limited by underlying physical constraints and inherent assumptions. In contrast, matched filtering, a physics-based signal-processing technique, can enhance the detectability of known waveforms by maximizing the signal-to-noise ratio. In this article, the source wavelet is first approximated using Wiener inverse filtering, followed by blue reflectivity modifications consisting of amplitude compensation of 3 dB/octave and a +<i>π</i>/4 phase correction. After applying the proposed generalized matched filter (GMF), an additional +<i>π</i>/2 phase correction is introduced as a direct consequence of the derivation, ensuring proper phase alignment. Unlike conventional matched filtering, which is designed for isolated reflectors, the GMF developed in this study explicitly incorporates the thin-layer response by modifying the original matched-filter equation. This modification represents a key novelty of the study, as it enables the filter to account for the differential effects of the seismic wavelet. To stabilize the results and suppress noise amplification, frequency-based regularization differentiation is applied. The proposed GMF workflow is validated using stationary and non-stationary synthetic examples as well as a field seismic dataset. The results demonstrate improved thin-bed resolution and reflector coherence through wavelet compression and phase correction within the available frequency bandwidth, rather than through artificial spectral broadening. Quantitative analyses based on side-lobe energy ratio (SLER), power spectral density (PSD), tuning-thickness measurements and seismic coherence demonstrate that GMF enhances seismic interpretation while preserving the spectral characteristics of the original data.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D U-Net-Assisted Automated Facies Classification From Seismic Volume of Amguri Prospect, Upper Assam Shelf, NE India","authors":"Bappa Mukherjee,&nbsp;Soumitra Kar,&nbsp;Rohit Banerjee,&nbsp;Kalachand Sain","doi":"10.1111/1365-2478.70241","DOIUrl":"https://doi.org/10.1111/1365-2478.70241","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditionally, facies classification from seismic data heavily relies on manual interpretation, a time-consuming and subjective task. To circumvent this laborious traditional workflow, an automated 3D U-Net-based framework of facies classification from 3D seismic data is presented. Initially, the seismic data were cleaned using a dip-steered median filter (DSMF), and facies of different geological ages were labelled to generate facies masks. Afterwards, a 3D-UNet facies-prediction model was trained by feeding the DSMF-filtered seismic volume as input and the corresponding facies mask as the target. After successful training, the feasibility of the facies classification model was tested over the entire seismic volume. In the training phase, ∼94% accuracy was achieved, and test-phase accuracy was evaluated using accuracy metrics and structural similarity index (SSIM), signal to noise ratio (SNR), <i>R</i>², root mean squared error (RMSE) and mean squared error (MSE) parameters, indicating that the U-Net-derived facies classes are well corroborated with the manually interpreted facies over the entire seismic volume. Industrial-grade seismic data from the Amguri prospect of Upper Assam Shelf, India, were analysed in this study. The accuracy-based ranking of the facies classes is: Overburden &gt; Basement &gt; Lakwa &gt; Tura &gt; Sylhet &gt; Kopili &gt; Geleki &gt; Barail Coal Shale (BCS) &gt; Barail Main Sand (BMS). The model shows higher accuracy, improved delineation of facies boundaries and better spatial continuity, consistent with known geological formations. The demonstrated 3D U-Net-based paradigm is an effective and scalable approach for automated facies interpretation from seismic data, reducing interpreter bias and improving reservoir characterisation in complex geological environments.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Acoustic Pure P- and S-Wave Equations for Modelling and Reverse Time Migration in Tilted Transversely Isotropic Media","authors":"Lucas S. Bitencourt,&nbsp;Reynam C. Pestana","doi":"10.1111/1365-2478.70238","DOIUrl":"https://doi.org/10.1111/1365-2478.70238","url":null,"abstract":"&lt;p&gt;To reduce information loss in seismic imaging of complex geological media, it is essential to account for anisotropy, particularly in tilted transversely isotropic (TTI) formations–the most common anisotropy in exploration geophysics. However, the elastic wave equations governing such media inherently couple &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;annotation&gt;$P$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;- and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;\u0000 &lt;annotation&gt;$S$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-waves, whereas acoustic reverse time migration (RTM) relies on pure &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;annotation&gt;$P$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-wavefields. This coupling introduces shear-wave energy during modelling, leading to artefacts in the migrated image. To address this issue, exact acoustic pure &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;annotation&gt;$P$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;- and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;\u0000 &lt;annotation&gt;$S$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-wave equations are derived from the analytical decomposition of the elastic wave equation in vertically transversely isotropic media. Their dispersion relations are mathematically equivalent to those of the exact elastic wave equation, and a simple yet effective optimization procedure is proposed for their numerical solution in TTI media. The proposed formulations accurately decouple the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;annotation&gt;$P$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;- and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;\u0000 &lt;annotation&gt;$S$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-wavefields with near-zero error in both the phase-angle and spatial domains – where, in the latter, the error is also near zero in both phase and amplitude – even when the &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msub&gt;\u0000 &lt;mi&gt;S&lt;/mi&gt;\u0000 &lt;mi&gt;V&lt;/mi&gt;\u0000 &lt;/msub&gt;\u0000 &lt;annotation&gt;$S_V$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-wave velocity is nonzero and &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;δ&lt;/mi&gt;\u0000 &lt;mo&gt;&gt;&lt;/mo&gt;\u0000 &lt;mi&gt;ε&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;$delta &gt;epsilon$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;. Moreover, the pure &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mi&gt;P&lt;/mi&gt;\u0000 &lt;annotation&gt;$P$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;-wave formulation is shown t","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.70238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148848679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inversion of Induced Polarization Parameters From Time-Domain Electromagnetic Data Using the Debye Decomposition Model","authors":"Masayuki Motoori,&nbsp;Lindsey Heagy,&nbsp;Gosuke Hoshino,&nbsp;Kumpei Nagase,&nbsp;Takumi Sato","doi":"10.1111/1365-2478.70243","DOIUrl":"https://doi.org/10.1111/1365-2478.70243","url":null,"abstract":"<p>Time-domain electromagnetics (TEM) is a valuable tool for exploring seafloor hydrothermal deposits, which are characterized by variations in resistivity and chargeability. Several surveys using the Waseda integrated seafloor time-domain electromagnetics (WISTEM) have been conducted. Negative transients, which are attributed to induced polarization (IP) effects for this coincident-type transmitter–receiver configuration loop, have been observed in data collected over a known deposit in the Okinawa Trough (2018). The Cole–Cole type model is a common parameterization of complex resistivity due to IP effects; however, it implicitly assumes that the data are sensitive to a wide range of frequencies. TEM data have limited frequency content, governed by the measurement time range and the diffusive nature of the fields. Using a synthetic example, we show that TEM inversion using the Cole–Cole model suffers from significant instability when the frequency content of the data is insufficient to constrain all four Cole–Cole parameters. The Debye decomposition model provides a way to overcome this challenge by allowing explicit selection of the relaxation time band over which the influence of chargeability in the data behaves distinctly from the influence of resistivity. In this paper, we introduce a workflow for inverting TEM data using a Debye decomposition model. We demonstrate how to set the relaxation time band when inverting TEM data. We then compare the model recovered from the TEM data with a model obtained by inverting spectral IP (SIP) data from the target, which describes how its resistivity varies as a function of frequency, over a wide frequency range. When inverting SIP data, we use the same Debye decomposition model as was used for the TEM data. The recovered IP parameters can be compared within a common parameterization. Using a synthetic example, we demonstrate that the inversion results obtained from the TEM data are consistent with the bandwidth-limited model obtained from the SIP response. Finally, we present a field application and show that the method recovers IP parameters that are consistent with expected physical property values for seafloor hydrothermal deposits.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.70243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Neural Networks for Model Error Correction in Probabilistic Inversion of Controlled Source Electromagnetic Data","authors":"Matías W. Elías,&nbsp;Marina Rosas-Carbajal,&nbsp;Federico Späth,&nbsp;Fabio I. Zyserman","doi":"10.1111/1365-2478.70242","DOIUrl":"https://doi.org/10.1111/1365-2478.70242","url":null,"abstract":"<div>\u0000 \u0000 <p>We present a workflow for three-dimensional probabilistic inversion of controlled-source electromagnetic data that effectively balances accuracy and computational efficiency. The approach mitigates the high computational cost of forward modelling by employing a surrogate model derived from a mesh coarsening strategy. To account for the modelling errors inherent to this approximation, we implement a deep-learning–based parametric correction, enabling the joint inversion of correction and subsurface physical parameters. We use a synthetic marine experiment to verify that the proposed method recovers the true subsurface parameters. The inclusion of an error correction improves the predictive accuracy of the coarse mesh strategy and significantly reduces computation time compared to fine meshing forward modelling. Application to real-world marine data acquisition further illustrates the capability of the method to estimate the geometry and location of an oil reservoir. Our results highlight the potential of deep-learning–assisted surrogate modelling as a practical tool for accelerating the probabilistic inversion.</p></div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Theoretical Verification of Homogeneous Seismic Body Waves in Viscoelastic Isotropic and Transversely Isotropic Media","authors":"Pengde Wang,&nbsp;Kejia Pan,&nbsp;Xu Liu,&nbsp;Jianping Liao,&nbsp;Chao Jin,&nbsp;Hexiu Liu","doi":"10.1111/1365-2478.70239","DOIUrl":"https://doi.org/10.1111/1365-2478.70239","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;p&gt;On the basis of real ray path and the newly developed &lt;b&gt;g&lt;/b&gt;*-Hamiltonian method of ray-velocity vectors, we theoretically investigate three homogeneous body waves (qP, qSV and qSH) in viscoelastic transversely isotropic media and distinguish them from their inhomogeneous situations. We demonstrate theoretical conditions of the &lt;i&gt;Q&lt;/i&gt;-factors of medium for the three homogeneous body waves and conduct computational experiments with rock samples to validate the analytic derivations and characterizations of the homogenous and inhomogeneous body waves. The theoretical analysis and computational experiments reveal that in a viscoelastic isotropic medium P- and S-wave are always homogeneous; in a viscoelastic transversely isotropic medium, the qP- and qSV-wave are homogeneous only when four &lt;i&gt;Q&lt;/i&gt;-factors &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;{&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;11&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;13&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mo&gt;,&lt;/mo&gt;\u0000 &lt;mspace&gt;&lt;/mspace&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;33&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${ {{Q}^{( {11} )}},{{Q}^{( {13} )}}, {{Q}^{( {33} )}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;, &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;44&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;}&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${{Q}^{( {44} )}}} $&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; share a same value, and qSH-wave becomes homogeneous only when &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mo&gt;(&lt;/mo&gt;\u0000 &lt;mn&gt;44&lt;/mn&gt;\u0000 &lt;mo&gt;)&lt;/mo&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;/msup&gt;\u0000 &lt;annotation&gt;${{Q}^{( {44} )}}$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt; equals &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;msup&gt;\u0000 &lt;mi&gt;Q&lt;/mi&gt;\u0000 ","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"74 7","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148783722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}