Geophysical Prospecting最新文献

{"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}

{"title":"Compressive sensing principles applied in time and space for three-dimensional land seismic data acquisition and processing","authors":"Woodon Jeong,&nbsp;Constantinos Tsingas,&nbsp;Mohammed S. Almubarak,&nbsp;Yue Ma","doi":"10.1111/1365-2478.13654","DOIUrl":"https://doi.org/10.1111/1365-2478.13654","url":null,"abstract":"<p>Compressive sensing introduces novel perspectives on non-uniform sampling, leading to substantial reductions in acquisition cost and cycle time compared to current seismic exploration practices. Non-uniform spatial sampling, achieved through source and/or receiver areal distributions, and non-uniform temporal sampling, facilitated by simultaneous-source acquisition schemes, enable compression and/or reduction of seismic data acquisition time and cost. However, acquiring seismic data using compressive sensing may encounter challenges such as an extremely low signal-to-noise ratio and the generation of interference noise from adjacent sources. A significant challenge to this innovative approach is to demonstrate the translation of theoretical gains in sampling efficiency into operational efficiency in the field. In this study, we propose a spatial compression scheme based on compressive sensing theory, aiming to obtain an undersampled survey geometry by minimizing the mutual coherence of a spatial sampling operator. Building upon an optimised spatial compression geometry, we subsequently consider temporal compression through a simultaneous-source acquisition scheme. To address challenges arising from the recorded compressed seismic data in the non-uniform temporal and spatial domains, such as missing traces and crosstalk noise, we present a joint deblending and reconstruction algorithm. Our proposed algorithm employs the iterative shrinkage-thresholding method to solve an <i>ℓ</i>₂–<i>ℓ</i>₁ optimization problem in the frequency–wavenumber–wavenumber (<i>ω</i>–<i>k_x</i>–<i>k_y</i>) domain. Numerical experiments demonstrate that the proposed algorithm produces excellent deblending and reconstruction results, preserving data quality and reliability. These results are compared with non-blended and uniformly acquired data from the same location illustrating the robustness of the application. This study exemplifies how the theoretical improvements based on compressive sensing principles can significantly impact seismic data acquisition in terms of spatial and temporal sampling efficiency.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"3-18"},"PeriodicalIF":1.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114469","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":"Hydrocarbon detection via quantum mechanics–based highlight volumes extraction","authors":"Ya-juan Xue,&nbsp;Hong Zhang,&nbsp;Jin-Qiang Zhang,&nbsp;Xing-jian Wang,&nbsp;Jun-xing Cao,&nbsp;Zhe-ge Liu,&nbsp;Wu Wen,&nbsp;Jia Yang,&nbsp;Dong-Fang Li","doi":"10.1111/1365-2478.13653","DOIUrl":"https://doi.org/10.1111/1365-2478.13653","url":null,"abstract":"<p>Spectral decomposition, aiding for direct hydrocarbon detection, generally employs time–frequency analysis methods to characterize the time-varying frequency contents of the subsurface layers. However, ongoing efforts to improve time–frequency analysis resolution still face limitations, leading to inaccurate spectral decomposition. In this study, a quantum mechanics–based highlight volumes extraction method, which includes the quantum peak amplitude above average volume and the quantum peak frequency volume, is proposed as a novel spectral decomposition method for hydrocarbon detection. Seismic data are transformed into the time–frequency domain using continuous wavelet transform, and then each sample's amplitude spectrum of each trace is projected on a specific basis constructed by the wave functions using the Schröedinger equation. This yields the corresponding projection coefficient for each sample's amplitude spectrum. For each projection coefficient, the quantum peak amplitude above average volume is calculated by subtracting the average amplitude from the maximum amplitude. The quantum peak frequency volume consists of the local frequency points where the quantum peak amplitude is the maximum. Our approach stands out for its ability to indicate strong amplitude anomalies typically associated with the hydrocarbons and precise gas reservoir locations and has also been validated to handle seismic data with low signal-to-noise ratio well. Model tests and field data applications show the effectiveness and the advantages of the proposed quantum mechanics–based highlight volumes extraction method. The comparison with the conventional methods illustrates that the proposed quantum mechanics–based highlight volumes extraction method has higher temporal and spatial resolution and is more accurate in detecting the hydrocarbons in the gas reservoir. However, it may require longer computational times compared with the conventional methods. This work aims to complement the current spectral decomposition techniques with a new quantum mechanics–based highlight volumes extraction method.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"19-37"},"PeriodicalIF":1.8,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114205","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":"Accelerating target-oriented multi-parameter elastic full-waveform uncertainty estimation by reciprocity","authors":"W. A. Mulder,&nbsp;B. N. Kuvshinov","doi":"10.1111/1365-2478.13650","DOIUrl":"https://doi.org/10.1111/1365-2478.13650","url":null,"abstract":"<p>The accuracy of a model obtained by multi-parameter full-waveform inversion can be estimated by analysing the sensitivity of the data to perturbations of the model parameters in selected subsurface points. Each perturbation requires the computation of the seismic response in the form of Born scattering data for a typically very large number of shots, making the method time consuming. The computational cost can be significantly reduced by placing sources of different types at the Born scatterer, the point where the subsurface parameters are perturbed. Instead of modelling each shot separately, reciprocity relations provide the wavefields from the shot positions to the scatter point in terms of wavefields from the scatterer to the shot positions. In this way, the Born scattering data from a single point in the isotropic elastic case for a marine acquisition with pressure sources and receivers can be expressed in terms of the wavefields for force and moment tensor sources located at the scatterer and only a small number of forward runs are required. A two-dimensional example illustrates how the result can be used to determine the Hessian and local relative covariance matrix for the model parameters at the scatterer at the cost of five forward simulations. In three dimensions, that would be nine.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"38-48"},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13650","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114094","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":"Enhancing porosity prediction: Integrating seismic inversion utilizing sparse layer reflectivity, and particle swarm optimization with radial basis function neural networks","authors":"Ravi Kant,&nbsp;Brijesh Kumar,&nbsp;Ajay P. Singh,&nbsp;G. Hema,&nbsp;S. P. Maurya,&nbsp;Raghav Singh,&nbsp;K. H. Singh,&nbsp;Piyush Sarkar","doi":"10.1111/1365-2478.13651","DOIUrl":"https://doi.org/10.1111/1365-2478.13651","url":null,"abstract":"<p>Seismic inversion, a crucial process in reservoir characterization, gains prominence in overcoming challenges associated with traditional methods, particularly in exploring deeper reservoirs. In this present study, we propose an inversion approach based on modern techniques like sparse layer reflectivity and particle swarm optimization to obtain inverted impedance. The proposed sparse layer reflectivity and particle swarm optimization techniques effectively minimize the error between recorded seismic reflection data and synthetic seismic data. This reduction in error facilitates accurate prediction of subsurface parameters, enabling comprehensive reservoir characterization. The inverted impedance obtained from both methods serves as a foundation for predicting porosity, utilizing a radial basis function neural network across the entire seismic volume. The study identifies a significant porosity zone (&gt;20%) with a lower acoustic impedance of 6000–8500 m/s g cm³, interpreted as a sand channel or reservoir zone. This anomaly, between 1045 and 1065 ms two-way travel time, provides high-resolution insights into the subsurface. The particle swarm optimization algorithm shows higher correlation results, with 0.98 for impedance and 0.73 for porosity, compared to sparse layer reflectivity's 0.81 for impedance and 0.65 for porosity at well locations. Additionally, particle swarm optimization provides high-resolution subsurface insights near well location and across a broader spatial range. This suggests particle swarm optimization's superior potential for delivering higher resolution outcomes compared to sparse layer reflectivity.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 1","pages":"49-66"},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114096","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":"Poynting and polarization vectors mixed imaging condition of source time-reversal imaging","authors":"Nan Hu,&nbsp;Hao Li,&nbsp;Yunsheng Zhao,&nbsp;Yongming Lu,&nbsp;Tao Lei,&nbsp;Mei He,&nbsp;Xingda Jiang,&nbsp;Wei Zhang","doi":"10.1111/1365-2478.13649","DOIUrl":"https://doi.org/10.1111/1365-2478.13649","url":null,"abstract":"<p>Source time-reversal imaging based on wave equation theory can achieve high-precision source location in complex geological models. For the time-reversal imaging method, the imaging condition is critical to the location accuracy and imaging resolution. The most commonly used imaging condition in time-reversal imaging is the scalar cross correlation imaging condition. However, scalar cross-correlation imaging condition removes the directional information of the wavefield through modulus operations to avoid the direct dot product of mutually orthogonal P- and S-waves, preventing the imaging condition from leveraging the wavefield propagation direction to suppress imaging artefacts. We previously tackled this issue by substituting the imaging wavefield with the energy current density vectors of the decoupled wavefield, albeit at the cost of increased computational and storage demands. To balance artifact suppression with reduced computational and memory overhead, this work introduces the Poynting and polarization vectors mixed imaging condition. Poynting and polarization vectors mixed imaging condition utilizes the polarization and propagation direction information of the wavefield by directly dot multiplying the undecoupled velocity polarization vector with the Poynting vector, eliminating the need for P- and S-wave decoupling or additional memory. Compared with scalar cross-correlation imaging condition, this imaging condition can accurately image data with lower signal-to-noise ratios. Its performance is generally consistent with previous work but offers higher computational efficiency and lower memory usage. Synthetic data tests on the half-space model and the three-dimensional Marmousi model demonstrate the effectiveness of this method in suppressing imaging artefacts, as well as its efficiency and ease of implementation.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 4","pages":"1037-1059"},"PeriodicalIF":1.8,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845943","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}