Geophysical Prospecting最新文献

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Seismic Imaging of the Southern Vienna Basin (Austria) Using Probabilistic Ambient-Noise Tomography 使用概率环境噪声层析成像的南维也纳盆地(奥地利)地震成像
IF 1.8 3区 地球科学
Geophysical Prospecting Pub Date : 2025-09-01 DOI: 10.1111/1365-2478.70074
Clement Esteve, Y. Lu, J. M. Gosselin, R. Kramer, G. Bokelmann, G. Götzl
{"title":"Seismic Imaging of the Southern Vienna Basin (Austria) Using Probabilistic Ambient-Noise Tomography","authors":"Clement Esteve,&nbsp;Y. Lu,&nbsp;J. M. Gosselin,&nbsp;R. Kramer,&nbsp;G. Bokelmann,&nbsp;G. Götzl","doi":"10.1111/1365-2478.70074","DOIUrl":"https://doi.org/10.1111/1365-2478.70074","url":null,"abstract":"<p>Surface-wave ambient noise tomography has proven to be a cost-effective and reliable tool for imaging sedimentary basins when coupled with dense nodal seismic arrays. Here, we deployed 181 seismic nodes in two asynchronous phases across the southern Vienna Basin in spring 2024. We retrieve fundamental-mode Rayleigh and Love wave group velocity dispersion curves from seismic noise cross-correlations. We then obtained a pseudo three-dimensional (3D) <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>V</mi>\u0000 <msub>\u0000 <mi>S</mi>\u0000 <mi>V</mi>\u0000 </msub>\u0000 </msub>\u0000 <annotation>$V_{S_{V}}$</annotation>\u0000 </semantics></math> model and a seismic radial anisotropy (<span></span><math>\u0000 <semantics>\u0000 <mi>ζ</mi>\u0000 <annotation>$zeta$</annotation>\u0000 </semantics></math>) model of the area from a 2-step approach that employs trans-dimensional probabilistic (Bayesian) inference. The 3D <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>V</mi>\u0000 <msub>\u0000 <mi>S</mi>\u0000 <mi>V</mi>\u0000 </msub>\u0000 </msub>\u0000 <annotation>$V_{S_{V}}$</annotation>\u0000 </semantics></math> model highlights the structure of the Neogene basin. The 3D seismic radial anisotropy reveals several patterns, which may help constrain the presence and nature of faults and geologic fabrics in the study area. Combined, these models constrain first-order features of the basin structure that will be useful for planning further geothermal exploration. In particular, this work guides future detailed, spatially targeted two-dimensional/3D seismic reflection surveys.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.70074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923625","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}
引用次数: 0
An Improved Bedrock Geology Characterization in Limerick Basin Using Multi-Geophysical Data Integration Guided by Petrophysics and Outcrop Data 以岩石物理和露头资料为指导的多物探数据整合改进Limerick盆地基岩地质特征
IF 1.8 3区 地球科学
Geophysical Prospecting Pub Date : 2025-09-01 DOI: 10.1111/1365-2478.70066
Prithwijit Chakraborti, Aline Melo, Eoin Dunlevy, Mark Holdstock
{"title":"An Improved Bedrock Geology Characterization in Limerick Basin Using Multi-Geophysical Data Integration Guided by Petrophysics and Outcrop Data","authors":"Prithwijit Chakraborti,&nbsp;Aline Melo,&nbsp;Eoin Dunlevy,&nbsp;Mark Holdstock","doi":"10.1111/1365-2478.70066","DOIUrl":"https://doi.org/10.1111/1365-2478.70066","url":null,"abstract":"<p>Geological mapping in the Limerick Basin, Ireland, presents significant challenges due to the extensive glacial overburden obscuring the bedrock geology. To address this, multiple geophysical datasets comprising the Bouguer gravity anomaly, total magnetic intensity and resistivity depth slice at 60 m depth obtained from frequency domain electromagnetic data are integrated using a novel data integration workflow that uses geological (ground truth) and petrophysical data. The ground truth data available in this area contain information about the geological formations of outcrops and topmost geological units of drill cores procured from drilling campaigns undertaken by several mining companies.</p><p>The data integration workflow utilizes ground truth data for semi-supervised uniform manifold approximation and projection (UMAP) dimensionality reduction, which leads to cleaner separation of classes in the dimensionality-reduced data and improves the performance of the clustering algorithm for which we have used hierarchical density-based spatial clustering of applications with noise (HDBSCAN). The stochastic nature of UMAP yields slightly different results for each iteration. Hence, a repetitive workflow involving multiple iterations of UMAP and HDBSCAN is applied to create cluster maps with smoothly varying cluster labels, allowing us to classify them into ranges that are associated with geological formations and rock types using a combined interpretation technique involving geological, geophysical and petrophysical data.</p><p>The workflow is tested on a synthetic study inspired by the real geological setting of the Limerick Basin and geophysical datasets available in the area. The cluster map obtained from field data integration led to the proposal of a revised map of the area with significant modifications in the distribution of igneous and sedimentary units, specifically to the northwest and within the Limerick syncline region.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 7","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144923628","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}
引用次数: 0
Data-Driven Pegmatite Exploration Targeting in a Geologically Underexplored Area in the Tysfjord Region, Norway 数据驱动的伟晶岩勘探目标位于挪威Tysfjord地区地质勘探不足的地区
IF 1.8 3区 地球科学
Geophysical Prospecting Pub Date : 2025-08-26 DOI: 10.1111/1365-2478.70060
Hendrik Paasche, Marie-Andrée Dumais, Claudia Haase, Björn Eskil Larsen, Aziz Nasuti, Kerstin Saalmann, Georgios Tassis, Ying Wang, Axel Müller, Marco Brönner
{"title":"Data-Driven Pegmatite Exploration Targeting in a Geologically Underexplored Area in the Tysfjord Region, Norway","authors":"Hendrik Paasche,&nbsp;Marie-Andrée Dumais,&nbsp;Claudia Haase,&nbsp;Björn Eskil Larsen,&nbsp;Aziz Nasuti,&nbsp;Kerstin Saalmann,&nbsp;Georgios Tassis,&nbsp;Ying Wang,&nbsp;Axel Müller,&nbsp;Marco Brönner","doi":"10.1111/1365-2478.70060","DOIUrl":"https://doi.org/10.1111/1365-2478.70060","url":null,"abstract":"<p>We compute probabilistic Niobium–Yttrium–Fluorine (NYF) pegmatite prospectivity maps in the Tysfjord region in Northern Norway. NYF pegmatites are generally enriched in rare earth minerals and represent residual melts derived from granitic plutons or melts formed by partial melting of metaigneous rocks. In Tysfjord, however, these pegmatites contain high-purity quartz, which is the major target commodity of exploration and mining. As the area is geologically underexplored, we employ a data analytics approach for the discovery of new deposits. We carefully lay out our knowledge base and how it impacts the working hypothesis and feature engineering. Self-organizing maps are employed as an unsupervised and random forest classification as a supervised data analytics algorithm to process and link features derived from airborne magnetic and radiometric maps with sparse pegmatite occurrences available in the form of outcrops and active and abandoned mines. The predictive power of our probabilistic pegmatite prospectivity maps is analysed by means of additional boreholes, which indicates the usefulness of our prospectivity maps for exploration targeting. We recommend employing unsupervised and supervised data analytics approaches in exploration targeting case studies where uncertainty about the predictive power of the available database cannot be ruled out before subjecting the database to data analytics.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.70060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144897411","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}
引用次数: 0
A Deep Learning Approach for Transient Electromagnetic Data Denoising, Inversion and Uncertainty Analysis With Monte Carlo Dropout Technique 基于蒙特卡罗Dropout技术的瞬变电磁数据去噪、反演和不确定性分析的深度学习方法
IF 1.8 3区 地球科学
Geophysical Prospecting Pub Date : 2025-08-25 DOI: 10.1111/1365-2478.70069
Yinjia Zhu, Yeru Tang, Jianhui Li, Xiangyun Hu, Ronghua Peng
{"title":"A Deep Learning Approach for Transient Electromagnetic Data Denoising, Inversion and Uncertainty Analysis With Monte Carlo Dropout Technique","authors":"Yinjia Zhu,&nbsp;Yeru Tang,&nbsp;Jianhui Li,&nbsp;Xiangyun Hu,&nbsp;Ronghua Peng","doi":"10.1111/1365-2478.70069","DOIUrl":"https://doi.org/10.1111/1365-2478.70069","url":null,"abstract":"<div>\u0000 \u0000 <p>A comprehensive deep learning approach was introduced, encompassing data denoising, inversion imaging and uncertainty analysis. For denoising transient electromagnetic (TEM) data, we utilized a Bidirectional Long Short-Term Memory (BiLSTM) network. In the data inversion process, a combination of convolutional neural network (CNN) and BiLSTM structures was employed, and their outputs were consolidated using a multi-head attention mechanism. To ensure robust performance under challenging noise conditions, we implemented a specialized multi-channel noise training protocol during model optimization. The framework incorporates Monte Carlo (MC) dropout techniques to systematically evaluate prediction reliability throughout the inversion pipeline. This approach has not only been validated on test datasets but has also been successfully applied to the field dataset collected at the Narenbaolige Coalfield in Inner Mongolia, China. The deep learning inversion results obtained from both raw and denoised data exhibit reduced vertical continuity and increased roughness characteristics. In contrast, the Occam's inversion method with smoothness constraints yields results demonstrating superior lateral continuity and vertical smoothness. It is noteworthy that both inversion approaches show consistent interpretations regarding the scale of basalt formations and their contact interfaces with underlying sedimentary layers. Further uncertainty analysis reveals relatively higher uncertainty characteristics in the transition zones between basalt and sedimentary layers, as well as in deeper formations. The elevated uncertainty at interface regions may be attributed to model resolution limitations and inversion ill-posedness issues, whereas the higher uncertainty in deeper formations is more likely caused by the volumetric effects of electromagnetic field detection and the influence of observational data noise.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144894274","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}
引用次数: 0
High-Accuracy Modelling of 3D Frequency-Domain Elastic-Wave Equation Based on One-Direction Composition of the Average-Derivative Optimal Method 基于平均导数单向组合优化方法的三维频域弹性波方程高精度建模
IF 1.8 3区 地球科学
Geophysical Prospecting Pub Date : 2025-08-22 DOI: 10.1111/1365-2478.70070
Hao Wang, Jing-Bo Chen, Shu-Li Dong
{"title":"High-Accuracy Modelling of 3D Frequency-Domain Elastic-Wave Equation Based on One-Direction Composition of the Average-Derivative Optimal Method","authors":"Hao Wang,&nbsp;Jing-Bo Chen,&nbsp;Shu-Li Dong","doi":"10.1111/1365-2478.70070","DOIUrl":"https://doi.org/10.1111/1365-2478.70070","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate simulation of seismic waves is essential for achieving high-precision full-waveform inversion (FWI). Within the Cartesian coordinate system-based frequency-domain finite-difference (FDFD) framework, we propose a one-direction composition average-derivative optimal method for the 3D heterogeneous isotropic elastic-wave equation, referred to as the 45-point scheme. The results of dispersion analysis and weighted coefficient optimization demonstrate that the 45-point scheme achieves higher dispersion accuracy than the existing 27-point average-derivative scheme. More importantly, by constructing the impedance matrix along the ‘composition’ direction, the bandwidth of the sparse impedance matrix increases only slightly, with nonzero elements compactly distributed in strips. On the basis of the multifrontal massively parallel sparse direct solver (MUMPS) on a supercomputer platform, the 45-point scheme does not significantly increase computational complexity compared to the 27-point scheme. To further test the performance of the 45-point scheme, we provide several numerical experiments, including simple homogeneous and complex SEG/EAGE overthrust models. In comparison with the 27-point scheme, the 45-point scheme yields a notable improvement in computational accuracy, particularly for large grid ratios, while imposing only a modest increase in computational cost. These findings thus strongly suggest that the 45-point scheme holds promise as a viable option for the forward part of frequency-domain FWI in practical high-accuracy seismic imaging applications.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144888190","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}
引用次数: 0
Dynamic Fluid Flow Effects on Acoustic Propagation Characteristics of Unsaturated Porous Media in CO2 Geological Sequestration 动态流体流动对CO2地质封存中非饱和多孔介质声传播特性的影响
IF 1.8 3区 地球科学
Geophysical Prospecting Pub Date : 2025-08-21 DOI: 10.1111/1365-2478.70065
Yujuan Qi, Xiumei Zhang, Lin Liu
{"title":"Dynamic Fluid Flow Effects on Acoustic Propagation Characteristics of Unsaturated Porous Media in CO2 Geological Sequestration","authors":"Yujuan Qi,&nbsp;Xiumei Zhang,&nbsp;Lin Liu","doi":"10.1111/1365-2478.70065","DOIUrl":"https://doi.org/10.1111/1365-2478.70065","url":null,"abstract":"<div>\u0000 \u0000 <p>CO<sub>2</sub> geological sequestration (CGS) is a crucial strategy to mitigate the greenhouse effect. The quantitative correspondence between CO<sub>2</sub> saturation and acoustic response serves as the essential basis for monitoring CO<sub>2</sub> migration. However, due to dynamic fluid interactions between supercritical CO<sub>2</sub> and brine/oil in porous media, acoustic propagation behaviour is extremely complicated, even at the same saturation during drainage and imbibition processes. This study is motivated to evaluate the acoustic characteristics of the above porous stratum containing CO<sub>2</sub>. To do so, pore fluid parameter models specific to CGS are consolidated and refined, with the consideration of CO<sub>2</sub> solubility. Meanwhile, Lo's theory is modified to describe both partial flow and global flow in CO<sub>2</sub>-saturated porous media, capturing key mechanisms of patchy distribution and alterations in capillary pressure and relative permeability during drainage and imbibition. By combining these procedures, the wave propagation characteristics within CGS scenarios are systematically analysed. It is shown that CO<sub>2</sub> exhibits higher solubility than gases, leading to a distinct two-stage acoustic response, corresponding to its dissolved and free states. Relative permeability affects both compressional and shear waves, whereas capillary pressure and patchy distribution mainly affect compressional wave propagation. Notably, compressional waves exhibit heightened sensitivity to free CO<sub>2</sub> content and fluid flow dynamics, especially at ultrasound frequencies. The modified acoustic propagation theory demonstrates superior performance in characterizing compressional velocities during both drainage and imbibition. These findings highlight the dynamic fluid flow effects in CGS, providing a theoretical framework for analysing acoustic propagation characteristics.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881135","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}
引用次数: 0
Seismic Envelope-Driven Broadband Acoustic Impedance Inversion Using End-to-End Deep Sequential Convolutional Neural Network 基于端到端深度顺序卷积神经网络的地震包络驱动宽带声阻抗反演
IF 1.8 3区 地球科学
Geophysical Prospecting Pub Date : 2025-08-20 DOI: 10.1111/1365-2478.70068
Anjali Dixit, Animesh Mandal, Santi Kumar Ghosh
{"title":"Seismic Envelope-Driven Broadband Acoustic Impedance Inversion Using End-to-End Deep Sequential Convolutional Neural Network","authors":"Anjali Dixit,&nbsp;Animesh Mandal,&nbsp;Santi Kumar Ghosh","doi":"10.1111/1365-2478.70068","DOIUrl":"https://doi.org/10.1111/1365-2478.70068","url":null,"abstract":"<div>\u0000 \u0000 <p>Absolute impedance estimation is crucial for quantitative interpretation of petrophysical parameters such as porosity and lithology, from band-limited seismic data. The missing low-frequency part of the conventional seismic data leads to non-uniqueness in the solution and causes a hindrance to the absolute impedance estimation. This work presents an application of seismic envelope to retrieve absolute acoustic impedance (AI) values directly from band-limited data in an innovative workflow based on a deep sequential convolutional neural network (DSCNN). Along with the band-limited data and seismic envelope, we also incorporate the instantaneous phase information (to compensate for the lost phase information in a seismic envelope) as an auxiliary input into the DSCNN model to map the band-limited data into broadband data and then to retrieve absolute AI values. We have tested the proposed workflow on two synthetic benchmark datasets of Marmousi2 and SEAM 2D subsalt Earth model, as well as one field dataset of the F3 block, the Netherlands. Our results underline that the proposed approach is efficient in recovering the deeper features quite well as compared to the conventional approach, wherein only seismic band-limited data are used as input. Numerical tests show that the estimated low-frequency impedance is recovered well with our proposed seismic envelope-driven approach. Thus, the proposed workflow provides a robust solution for broadband impedance inversion by utilizing only one regression-based unified deep learning (DL) model. This work primarily highlights the potential of seismic envelope to greatly improve the estimation of low-frequency components of subsurface impedance model in a DL framework. Such a workflow for absolute impedance inversion from band-limited seismic will play an important role in reservoir characterization and in quantifying the elastic attributes.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144881086","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}
引用次数: 0
Application of Marchenko-Based Isolation to a Land S-Wave Seismic Dataset 基于marchenko的隔震方法在陆地s波地震数据集中的应用
IF 1.8 3区 地球科学
Geophysical Prospecting Pub Date : 2025-08-13 DOI: 10.1111/1365-2478.70064
Faezeh Shirmohammadi, Deyan Draganov, Johno van IJsseldijk, Ranajit Ghose, Jan Thorbecke, Eric Verschuur, Kees Wapenaar
{"title":"Application of Marchenko-Based Isolation to a Land S-Wave Seismic Dataset","authors":"Faezeh Shirmohammadi,&nbsp;Deyan Draganov,&nbsp;Johno van IJsseldijk,&nbsp;Ranajit Ghose,&nbsp;Jan Thorbecke,&nbsp;Eric Verschuur,&nbsp;Kees Wapenaar","doi":"10.1111/1365-2478.70064","DOIUrl":"https://doi.org/10.1111/1365-2478.70064","url":null,"abstract":"<p>The overburden structures often can distort the responses of the target region in seismic data, especially in land datasets. Ideally, all effects of the overburden and underburden structures should be removed, leaving only the responses of the target region. This can be achieved using the Marchenko method. The Marchenko method is capable of estimating Green's functions between the surface of the Earth and arbitrary locations in the subsurface. These Green's functions can then be used to redatum wavefields to a level in the subsurface. As a result, the Marchenko method enables the isolation of the response of a specific layer or package of layers, free from the influence of the overburden and underburden. In this study, we apply the Marchenko-based isolation technique to land S-wave seismic data acquired in the Groningen province, the Netherlands. We apply the technique for combined removal of the overburden and underburden, which leaves the isolated response of the target region, which is selected between 30 and 270 m depth. Our results indicate that this approach enhances the resolution of reflection data. These enhanced reflections can be utilised for imaging and monitoring applications.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833066","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}
引用次数: 0
A Glitch Detection and Removal Method for Three-Component Seismic Data From Mars Based On Deep Learning 基于深度学习的火星三分量地震数据故障检测与去除方法
IF 1.8 3区 地球科学
Geophysical Prospecting Pub Date : 2025-08-13 DOI: 10.1111/1365-2478.70067
Jiangjie Zhang, Yawen Zhang, Zhengwei Li, Chenyuan Wang
{"title":"A Glitch Detection and Removal Method for Three-Component Seismic Data From Mars Based On Deep Learning","authors":"Jiangjie Zhang,&nbsp;Yawen Zhang,&nbsp;Zhengwei Li,&nbsp;Chenyuan Wang","doi":"10.1111/1365-2478.70067","DOIUrl":"https://doi.org/10.1111/1365-2478.70067","url":null,"abstract":"<div>\u0000 \u0000 <p>The data recorded by the seismometer on the InSight are contaminated by interference signals called ‘glitches’, which have a specific duration and waveform. These glitches emerge very frequently with large amplitude differences and affect the subsequent processing of the data. Traditional methods for glitches removal rely on the threshold and cannot perfectly detect non-standard and composite glitches. We propose a deep learning based method for glitch detection and removal. A detection model based on the PhaseNet network is developed for three-component data. The ConvTasNet from the field of speech signal separation is introduced into the noise removal model to separate the glitches from the single-component data. The advantages of deep learning include the ability to autonomously extract features from the training set without requiring parameter adjustment and the ability to quickly process large amounts of data. The proposed method can detect and suppress non-standard glitches and provides a novel approach to removing them from Mars exploration records.</p>\u0000 </div>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144833065","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}
引用次数: 0
A Deep Learning–Based Dual Latent Space Method for the Estimation of Physical Flow Properties From Fibre-Optic Measurements 基于深度学习的光纤物理流特性估计对偶潜空间方法
IF 1.8 3区 地球科学
Geophysical Prospecting Pub Date : 2025-08-11 DOI: 10.1111/1365-2478.70063
Misael M. Morales, Kostyantyn Kravchenko, Andrea Rosales, Alberto Mendoza, Michael Pyrcz, Carlos Torres-Verdín
{"title":"A Deep Learning–Based Dual Latent Space Method for the Estimation of Physical Flow Properties From Fibre-Optic Measurements","authors":"Misael M. Morales,&nbsp;Kostyantyn Kravchenko,&nbsp;Andrea Rosales,&nbsp;Alberto Mendoza,&nbsp;Michael Pyrcz,&nbsp;Carlos Torres-Verdín","doi":"10.1111/1365-2478.70063","DOIUrl":"https://doi.org/10.1111/1365-2478.70063","url":null,"abstract":"<p>Distributed fibre-optic sensing (DFOS) technologies have emerged as cost-effective high-resolution monitoring alternatives over conventional geophysical techniques. However, due to the large volume and noisy nature of the measurements, significant processing is required and expert, fit-for-purpose tools must be designed to interpret and utilize DFOS measurements, including temperature and acoustics. Deep learning techniques provide the flexibility and efficiency to process and utilize DFOS measurements to estimate subsurface energy resource properties. We propose a deep learning-based dual latent space method to process distributed acoustic sensing (DAS) and distributed temperature sensing (DTS) measurements and estimate the injection point location and relative multiphase flow rates along a flow-loop equipped with a DFOS unit. The dual latent space method is composed of two identical convolutional U-Net AutoEncoders to compress and reconstruct the DAS and DTS data, respectively. The AutoEncoders are capable of determining an optimal latent representation of the DAS and DTS measurements, which are then combined and trained using one experimental trial and used to estimate the physical flow properties along five different test experimental trials. The predictions are obtained within 7 ms and with over 99.98% similarity and less than <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>3.68</mn>\u0000 <mo>×</mo>\u0000 <msup>\u0000 <mn>10</mn>\u0000 <mrow>\u0000 <mo>−</mo>\u0000 <mn>9</mn>\u0000 </mrow>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$3.68times 10^{-9}$</annotation>\u0000 </semantics></math> absolute error. The method is also shown to be robust to Gaussian noise and can be applied to different multiphase scenarios with a single pre-training procedure. The proposed method is therefore capable of fast and accurate estimation of physical flow properties at the laboratory scale and can potentially be used for rapid and accurate estimation in different laboratory or field subsurface energy resource applications.</p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":"73 6","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.70063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144811032","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}
引用次数: 0
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