Geophysical Prospecting最新文献

{"title":"Three-dimensional gravity forward modelling based on rectilinear grid and Block–Toeplitz Toeplitz–Block methods","authors":"Ling Wan,&nbsp;Shihe Li,&nbsp;Rui Ye,&nbsp;Yifan Wang,&nbsp;Zenghan Ma,&nbsp;Tingting Lin","doi":"10.1111/1365-2478.13658","DOIUrl":"https://doi.org/10.1111/1365-2478.13658","url":null,"abstract":"<div>\u0000 \u0000 <p>The main method for calculating the gravity field involves discretizing the density sources into a stack of rectangular prisms with a regular grid distribution. The analytical formulation of the gravity anomaly for a right-angled rectangular prism is affected by depth, with the kernel function decaying as depth increases. In addition, the efficiency of the computation and the storage requirements often pose challenges. We present a fast computational method for three-dimensional gravity forward modelling of subsurface space using rectilinear grid and apply the Block–Toeplitz Toeplitz–Block method to the rectilinear grid. The size of the upright rectangles increases with depth to offset the effect of depth. We assume that the observation points are distributed on a homogeneous grid, and the kernel sensitivity matrices exhibit a Block–Toeplitz Toeplitz–Block structure, which is symmetric. For rectilinear dissections of subsurface space in MATLAB, the logarithmic interval size is used. The rectilinear mesh can offset the effect of depth to some degree allowing gravity anomalies to decrease more quickly. For the test of a single model, the gravity anomalies decrease faster and more rapidly in the case of the rectilinear grid compared to the uniform grid. In addition to this, we performed simulations on more complex models and demonstrated that using the Block–Toeplitz Toeplitz–Block method on this basis greatly improves the computational efficiency.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"459-470"},"PeriodicalIF":1.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115781","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":"Unsupervised learning inversion of seismic velocity models based on a multi-scale strategy","authors":"Senlin Yang,&nbsp;Bin Liu,&nbsp;Yuxiao Ren,&nbsp;Peng Jiang","doi":"10.1111/1365-2478.13665","DOIUrl":"https://doi.org/10.1111/1365-2478.13665","url":null,"abstract":"<p>Deep learning-based methods have performed well in seismic waveform inversion tasks in recent years, while the need for velocity models as labels has somewhat limited their application. Unsupervised learning allows us to train the neural network without labels. When inverting seismic velocity models from observed data, labels are often unavailable for real data. To address this problem and improve network generalization, we introduce a multi-scale strategy to enhance the performance of unsupervised learning. The first ‘multi-scale’ is derived from the conventional full waveform inversion strategy, in which the low-, middle- and high-frequency inversion results are successively predicted during the network training. Another ‘multi-scale’ is to introduce multi-scale similarity as an additional data loss term to improve the inversion results. With 12,000 samples from the overthrust model, our method obtains comparable results with the supervised learning method and outperforms unsupervised methods that rely only on the mean square error as a loss function. We compare the performance of the proposed method with multi-scale full waveform inversion on the Marmousi model, and the proposed method achieves better results at low- and middle-frequencies, and, as a result, it provides good initial models for further full waveform inversion updates.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"471-486"},"PeriodicalIF":1.8,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115391","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":"Approximation for reflection and transmission at a thin isotropic poroelastic bed between two isotropic elastic half-spaces","authors":"Chun Yang,&nbsp;Alexey Stovas,&nbsp;Yun Wang,&nbsp;Jie Qu,&nbsp;Xueming He","doi":"10.1111/1365-2478.13666","DOIUrl":"https://doi.org/10.1111/1365-2478.13666","url":null,"abstract":"<p>Seismic responses from a horizontal poroelastic layer provide chances to detect fluids and characterize reservoirs. The poroelastic layer can be considered a thin poroelastic bed if the layer's thickness is less than about one-eighth of the P-wave wavelength. Most previous theoretical studies on the reflection and transmission of waves in a model containing a thin poroelastic bed employ fluid or poroelastic medium as the overlying media. Existing approximate formulas of PP-wave reflection coefficients are given for P-wave normal-incidence. Thus, this paper derived the wave reflection and transmission approximate formulas of a thin poroelastic bed between two elastic half-spaces with P-wave oblique incident. First, we illustrated the exact reflection and transmission matrix equations for P-wave incidents based on poroelasticity theory and the boundary conditions. Assuming the poroelastic bed's thickness is far less than wavelengths of S- and P-waves, approximate reflection and transmission formulas are expanded in Taylor series centred at value of the parameter defined as the product of angular frequency, thickness and slowness. Numerical results show that the thinner the poroelastic layer, the closer the approximate reflection and transmission coefficients are to the exact ones. The approximate formulas are valid for small and medium angles. Approximated PP-wave reflection and transmission coefficients are closer to the exact values than those of the converted waves, which is caused by the fact that P-wave has a lower slowness than S-wave.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"487-506"},"PeriodicalIF":1.8,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114747","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":"A workflow combining Kirchhoff single-stack redatuming and common-reflection surface-based enhancement for depth imaging: A case study","authors":"German Garabito,&nbsp;Bruno F. Gonçalves,&nbsp;João L. Caldeira,&nbsp;Hashem Shahsavani","doi":"10.1111/1365-2478.13662","DOIUrl":"https://doi.org/10.1111/1365-2478.13662","url":null,"abstract":"<p>Seismic datasets acquired in onshore basins typically present a great challenge for seismic depth imaging due to the poor quality of the prestack data caused by inherent acquisition difficulties and, in particular, the distortion of the seismic signal caused by topography and heterogeneity of the weathering zone. In this seismic data, the standard static correction does not give satisfactory results in depth imaging. Wave-equation-based redatuming is an alternative solution to this problem, as it correctly moves the data measured on the terrain surface to a new flat datum. But most redatuming techniques require knowledge of an accurate near-surface velocity model. A new workflow for depth imaging of land data is proposed by combining Kirchhoff single-stack redatuming and prestack data enhancement by common-reflection surface stack method. A major advantage of this redatuming method is that it only requires a good approximation of the near-surface velocity model to properly remove distortions in the seismic signal caused by the rugged topography and weathering zone. A new algorithm is introduced to apply Kirchhoff single-stack redatuming to multi-coverage land seismic data. Common-reflection surface-based prestack data enhancement is applied after redatuming to attenuate radon noise and enhance coherent event. The proposed workflow has been successfully applied to land seismic data from the Parnaíba Basin in northeastern Brazil, transforming surface-referenced prestack data to a new flat datum. By applying common-reflection surface-based enhancement to the redatumed prestack data, a significant improvement in signal-to-noise ratio and enhancement of reflections was achieved. The depth-migrated image confirms the great improvement in quality, where the reflectors show strong enhancement and better continuity throughout the section, compared to the migrated image obtained from the only redatumed data. This improvement in quality was important for the interpretation of the main reflectors of the geological formations.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"507-522"},"PeriodicalIF":1.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112958","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":"Applications of deep learning-based resolution-enhanced seismic data in fault identification","authors":"Lei Lin,&nbsp;Chenglong Li,&nbsp;Yanbin Kuang,&nbsp;Xing Xin,&nbsp;Zhi Zhong","doi":"10.1111/1365-2478.13664","DOIUrl":"https://doi.org/10.1111/1365-2478.13664","url":null,"abstract":"<p>High-quality seismic data play a crucial role in accurately interpreting tectonic and lithologic features such as small faults, river margins and thin beds. Over the past decades, researchers have developed numerous methods to enhance seismic resolution and signal-to-noise ratio. However, the benefits of quality-improved seismic data for seismic interpretation have received limited attention. In response, we propose a generative adversarial network–based algorithm to enhance seismic quality and assess how this algorithm improves the accuracy of both machine learning–based and manual fault identification. For machine learning–based fault identification, we integrate a resolution enhancement and noise attenuation neural network (HRNet) with a fault identification neural network (FaultNet). A raw seismic image is first fed into the trained HRNet to obtain a resolution-enhanced and noise-suppressed image, which is then input into the trained FaultNet to produce the high-resolution fault probability map. For manual fault identification, we enlisted three interpreters with geophysical backgrounds to annotate faults on seismic images both before and after HRNet enhancement. Comparison experiments on three field seismic samples show that our method generates more accurate, cleaner and sharper fault probability maps than directly feeding raw seismic images into FaultNet. In addition, our workflow outperforms both the milestone fault identification method and state-of-the-art Transformer-based neural networks, particularly in detecting small-scale faults. Furthermore, the HRNet-enhanced seismic images help interpreters identify small- and medium-scale faults with reduced uncertainty. In the future, HRNet-enhanced seismic data can be applied to a broader range of high-precision seismic interpretation tasks, including horizon picking, channel boundary detection and attribute inversion.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"523-542"},"PeriodicalIF":1.8,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113026","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":"Three-dimensional electromagnetic inversion of transfer function data from controlled sources","authors":"Paula Rulff,&nbsp;Thomas Kalscheuer,&nbsp;Mehrdad Bastani,&nbsp;Dominik Zbinden","doi":"10.1111/1365-2478.13660","DOIUrl":"https://doi.org/10.1111/1365-2478.13660","url":null,"abstract":"<p>We develop a three-dimensional inversion code to image the resistivity distribution of the subsurface from frequency-domain controlled-source electromagnetic data. Controlled-source electromagnetic investigations play an important role in many different geophysical prospecting applications. To evaluate controlled-source electromagnetic data collected with complex measurement setups, advanced three-dimensional modelling and inversion tools are required.</p><p>We adopt a preconditioned non-linear conjugate gradient algorithm to enable three-dimensional inversion of impedance tensor and vertical magnetic transfer function data produced by multiple sets of two independent active sources. Forward simulations are performed with a finite-element solver. Increased sensitivities at source locations can optionally be counteracted with a weighting function in the regularization term to reduce source-related anomalies in the resistivity model. We investigate the capabilities of the inversion code using one synthetic and one field example. The results demonstrate that we can produce reliable subsurface models, although data sets from single pairs of independent sources remain challenging.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"543-561"},"PeriodicalIF":1.8,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13660","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143121351","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":"Facies-constrained simultaneous inversion for elastic parameters and fracture weaknesses using azimuthal partially incidence-angle-stacked seismic data","authors":"Huaizhen Chen,&nbsp;Jian Han,&nbsp;Kun Li","doi":"10.1111/1365-2478.13659","DOIUrl":"https://doi.org/10.1111/1365-2478.13659","url":null,"abstract":"<p>In order to improve the identification and characterization of underground fractured reservoirs, seismic inversion for elastic properties and fracture indicators is required. To improve the accuracy of seismic inversion, model constraints are necessary. Model constraints of P- and S-wave moduli can be provided by well logging data; however, model constraints of fracture weaknesses are often unavailable. To obtain model constraints of fracture weaknesses, we propose a two-stage inversion method, which is implemented as (1) estimating azimuthal elastic impedance (AEI) and fracture facies using partially incidence-angle-stacked seismic data at different azimuths; and (2) using the estimated azimuthal elastic impedance to predict P- and S-wave moduli, density and fracture weaknesses, which is constrained by models constructed using the estimated fracture facies. In the first stage, we use Gaussian mixture prior distribution to obtain azimuthal elastic impedance of different incidence angles and azimuths, and we also predict fracture facies combining the obtained azimuthal elastic impedance and seismic data. In the second stage, we implement the Bayesian maximum a posterior inversion for estimating unknown parameter vectors. We apply the proposed inversion method to noisy synthetic seismic data, which illustrates the inversion method is robust even in the case of a signal-to-noise ratio of 1. Tests on real data reveal that reliable results of P- and S-wave moduli and fracture weaknesses are obtained, which verifies that the inversion method is a valuable tool for generating reliable fracture indicators from azimuthal seismic data for identifying underground fractured reservoirs.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"562-574"},"PeriodicalIF":1.8,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120866","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":"Comparative analysis of eikonal-based first-arrival tomography: A case study from the Santos Basin, Brazil","authors":"Paulo H. B. Alves,&nbsp;Marco Cetale,&nbsp;Sergio L. E. F. da Silva,&nbsp;Luiz Alberto Santos","doi":"10.1111/1365-2478.13657","DOIUrl":"https://doi.org/10.1111/1365-2478.13657","url":null,"abstract":"<div>\u0000 \u0000 <p>We compare classical and adjoint-state first-arrival tomography approaches in subsurface model reconstruction, focusing on pre-salt updates with a circular-shot ocean bottom node geometry. The investigation demonstrates that, whereas conventional tomography has faster convergence and better alignment with observed data, it produces significant noise artefacts and fails to adequately represent the reservoir section. In contrast, adjoint-state tomography provides superior model reconstruction by taking into account the complete travel time volume, significantly lowering noise and boosting reservoir imaging despite its higher computational cost. A quantitative investigation of root mean squared errors for ultra-long offsets confirms the efficacy of adjoint-state tomography in minimizing data misfit and improving model fidelity. The findings emphasize the potential of adjoint-state tomography in enhancing subsurface imaging and underscore the limits of conventional tomography in handling complex subsurface details with sparse acquisition geometry.</p></div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 4","pages":"1060-1075"},"PeriodicalIF":1.8,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845958","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":"A hybrid network for three-dimensional seismic fault segmentation based on nested residual attention and self-attention mechanism","authors":"Qifeng Sun,&nbsp;Hui Jiang,&nbsp;Qizhen Du,&nbsp;Faming Gong","doi":"10.1111/1365-2478.13655","DOIUrl":"https://doi.org/10.1111/1365-2478.13655","url":null,"abstract":"<p>Fault detection is a crucial step in seismotectonic interpretation and oil–gas exploration. In recent years, deep learning has gradually proven to be an effective approach for detecting faults. Due to complex geological structures and seismic noise, detection results of such approaches remain unsatisfactory. In this study, we propose a hybrid network (NRA-SANet) that integrates a self-attention mechanism into a nested residual attention network for a three-dimensional seismic fault segmentation task. In NRA-SANet, the nested residual coding structure is designed to fuse multi-scale fault features, which can fully mine fine-grained fault information. The two-head self-attention decoding structure is designed to construct long-distance fault dependencies from different feature representation subspaces, which can enhance the understanding of the model regarding the global fault distribution. In order to suppress the interference of seismic noise, we propose a fault-attention module and embed it into the model. It utilizes the weighted and the separate-and-reconstruct channel strategy to improve the model sensitivity to fault areas. Experiments demonstrate that NRA-SANet exhibits strong noise robustness, while it can also detect more continuous and more small-scale faults than other approaches on field seismic data. This study provides a new idea to promote the development of seismic interpretation.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"575-594"},"PeriodicalIF":1.8,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119394","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":"Topography-dependent eikonal solver in tilted transverse isotropic media with anelliptical factorization","authors":"Xiaole Zhou,&nbsp;Serge Sambolian,&nbsp;Haiqiang Lan,&nbsp;Stéphane Operto","doi":"10.1111/1365-2478.13656","DOIUrl":"https://doi.org/10.1111/1365-2478.13656","url":null,"abstract":"<p>Incorporating anisotropy and complex topography is necessary to perform traveltime tomography in complex land environments while being a computational challenge when traveltimes are computed with finite-difference eikonal solvers. Previous studies have taken this challenge by computing traveltimes in transverse isotropic media involving complex topography with a finite-difference eikonal equation solver on a curvilinear grid. In this approach, the source singularity, which is a major issue in eikonal solvers, is managed with the elliptical multiplicative factorization method, where the total traveltime field is decomposed into an elliptical base traveltime map, which has a known analytical expression and an unknown perturbation field. However, the group velocity curve can deviate significantly from an ellipse in anellipitically anisotropic media. In this case, the elliptical base traveltime field differs significantly from the anelliptical counterpart, leading to potentially suboptimal traveltime solutions, even though it helps to mitigate the detrimental effects of the source singularity. To overcome this issue, we develop a more accurate topography-dependent eikonal solver in transverse isotropic media that relies on anelliptical factorization. To achieve this, we first define the coordinate transform from the Cartesian to the curvilinear coordinate system, which provides the necessary framework to implement the topography-dependent transverse isotropic finite-difference eikonal solver with arbitrary source and receiver positioning. Then, we develop a semi-analytical method for the computation of the topography-dependent anelliptical base traveltime field. Finally, we efficiently solve the resulting quadratic elliptical equation using the fast sweeping method and a quartic anelliptical source term through fixed-point iteration. We assess the computational efficiency, stability and accuracy of the new eikonal solver against the solver based on elliptical factorization using several transverse isotropic numerical examples. We conclude that this new solver provides a versatile and accurate forward engine for traveltime tomography in complex geological environments such as foothills and thrust belts. It can also be used in marine environments involving complex bathymetry when tomography is applied to redatumed data on the sea bottom.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 2","pages":"595-610"},"PeriodicalIF":1.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116021","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}