Journal of Applied Geophysics最新文献

{"title":"Instability and failure characteristics of surrounding rock and coal pillar of gob-side roadways under coupled longitudinal-transverse wave and stress fields during close-distance multi-seam mining","authors":"Hao Xu ,&nbsp;Shengquan He ,&nbsp;Feng Shen ,&nbsp;Dazhao Song ,&nbsp;Xueqiu He ,&nbsp;Zhenlei Li ,&nbsp;Majid Khan ,&nbsp;Fanxiang Zhao","doi":"10.1016/j.jappgeo.2025.106055","DOIUrl":"10.1016/j.jappgeo.2025.106055","url":null,"abstract":"<div><div>Under static-dynamic stress coupling in close-distance multi-seam mining, gob-side roadway surrounding rock and adjacent coal pillar are subjected to intense mine pressure. This study investigates coal-rock failure under coupled longitudinal-transverse wave and stress conditions. Microseismic monitoring, numerical simulations, and field measurements were conducted to show that microseismic events mainly cluster near the excavated coal seam, as well as adjacent roof and floor strata. The surrounding rock of gob-side roadway and the adjacent coal pillar (8 m wide) exhibit a higher microseismic event density compared to other areas. Under static loading, tensile failure initiates at the mid-height of the coal pillar. The roadway exhibits pronounced asymmetric deformation, with lateral displacement reaching 42 mm on the gob side and 10 mm on the coal side. Severe fragmentation occurs on the coal pillar side contributes to this asymmetric deformation. Under dynamic loading of longitudinal-transverse waves, the gob and fracture zones exhibit significantly higher attenuation than other strata. Meanwhile, surrounding rock masses and coal pillar structures show elevated dynamic responses compared to adjacent areas. The kinetic energy reaches its maximum during the longitudinal-transverse wave coupling stage, with the horizontal component exceeding the vertical component. Wave coupling intensifies asymmetric damage, leading to over 70 % of failure volume in the coal pillar. The pillar stress state transitions from compressive to tensile, with 95.8 % of the stored elastic energy released. Borehole imaging shows 7.79 m fracture depth on the pillar side and minimal damage on the coal side. The field observations confirm the reliability of numerical simulations. The analysis indicates that a remaining coal pillar above the studied coal seam causes stress concentration at the working face, with peak stress reaching 50 MPa. The combination effect of high static stress and dynamic disturbances generated by key stratum rupture serves as the main mechanism contributing to strong mine pressure behavior. This mechanism results in asymmetric roadway deformation and coal pillar instability. The findings provide a theoretical basis for optimizing support design and mitigating dynamic hazards in gob-side roadways under similar geological conditions.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106055"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737523","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 facies modeling of a carbonate reservoir in the Santos Basin using machine learning","authors":"Henrique M. Santana,&nbsp;Emilson P. Leite","doi":"10.1016/j.jappgeo.2025.106049","DOIUrl":"10.1016/j.jappgeo.2025.106049","url":null,"abstract":"<div><div>Three-dimensional facies modeling is essential for optimizing hydrocarbon exploration and production, particularly in complex carbonate reservoirs. Traditional classification methods rely on manually delineating homologous zones in scatter plots of elastic parameters, introducing subjectivity and limiting predictive accuracy. This study implements a machine learning-based workflow to classify petroelastic facies, flow units, and geomechanical units, minimizing arbitrary thresholding and improving interwell predictions. A supervised learning model was trained using well log data and validated through cross-validation, incorporating pseudo-curves derived from seismic inversion products (e.g., Zp, Zs) to enhance resolution. Data resampling techniques were applied to address class imbalance within the Barra Velha Formation. The resulting classification achieved 70–95 % accuracy and generated 3D models that highlight distinct facies distributions, with reservoir facies concentrated in the top and bottom intervals, while the middle interval exhibited predominantly non-reservoir facies, consistent with petrographic analyses. The facies distribution also revealed elongated reservoirs aligned with NE-SW structural highs, compartmentalized by faults of the same orientation. This improved understanding of facies distribution supports better placement of wells and reduced development risk. By eliminating the need for manual delineation of facies zones, this study presents a novel approach to three-dimensional facies classification from seismic inversion data, offering an objective and efficient alternative to more traditional methods used for reservoir characterization.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106049"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694552","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":"Methodology to feasibility and 4D interpretation support in a pre-salt carbonate reservoir","authors":"Evângela P.A. da Silva ,&nbsp;Alessandra Davólio ,&nbsp;Marcos Sebastião dos Santos ,&nbsp;Denis José Schiozer","doi":"10.1016/j.jappgeo.2025.106035","DOIUrl":"10.1016/j.jappgeo.2025.106035","url":null,"abstract":"<div><div>Discovered in 2006 and with a current production of almost 70 % of hydrocarbons produced in Brazil, pre-salt carbonates are of great interest for 4D seismic analysis. 4D feasibility studies help to understand how production influences the 4D seismic response. We developed a workflow to obtain seismic amplitude data from a simulation model and then access estimated 4D attributes through a fast-track inversion procedure. Our aim was to understand the effect of dynamic changes (pressure and saturation) in 4D seismic attributes considering the challenges regarding carbonate reservoirs under a water alternation gas (WAG) production strategy, using a pre-salt like case. We also compared inverted 4D attributes with those obtained by petro-elastic modeling and with simulator data to evaluate the reliability of a fast-track inversion procedure.</div><div>The study showed that 4D positive amplitude anomalies are observed in the water alternation gas injectors regions, due to water replacing oil. These anomalies were also observed after 4D inversion, with the best results presented by ΔAI and ΔS_w. The application of the fast-track inversion method for 4D data is a viable alternative for those looking to perform a first qualitative analysis of 4D data quickly, but your limitations must be considered to avoid misinterpretations.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106035"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694549","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":"Object-oriented algorithm for the extraction of diffracted/scattered waves in the data domain","authors":"M. Protasov","doi":"10.1016/j.jappgeo.2025.106054","DOIUrl":"10.1016/j.jappgeo.2025.106054","url":null,"abstract":"<div><div>Diffracted and scattered waves are used to construct diffraction images and enhance seismic interpretation. This paper presents an efficient object-oriented algorithm for detecting and enhancing scattered waves in the seismic data domain, enabling to solve challenging problem i.e. the generation of high-quality diffraction images based on scattered wave energy. Scattered waves are detected through coherent summation over sufficiently large apertures, while excess reflected events are suppressed. A key innovation of the algorithm is its method for determining the kinematic parameters of scattered waves, it calculates the hodographs of scattered waves within a pre-constructed velocity model for the specific target area where diffraction images are to be generated. For the data summation using stage two approaches are implemented: the first is based on weights derived from semblance analysis; the second employs an operator-oriented summation approach. Tests on simple methodological data and realistic data for a fractured model from Eastern Siberia demonstrate that the operator-oriented approach effectively enhances the amplitudes of scattered waves originating in the target domain while providing an accurate enough approximation of these waves. Application of the algorithm to real data shows that it produces diffraction images with a higher signal-to-noise ratio and image focusing compared to conventional methods.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106054"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694548","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":"Rapid grouting evaluation with magnetic detection: Methods and mechanisms","authors":"Botao Wang ,&nbsp;Jiacheng Zhang ,&nbsp;Luoluo Song ,&nbsp;Zhiheng Wang ,&nbsp;Rentai Liu ,&nbsp;Xiuhao Li ,&nbsp;Shihao Luo ,&nbsp;Mengjun Chen ,&nbsp;Jiwen Bai","doi":"10.1016/j.jappgeo.2025.106061","DOIUrl":"10.1016/j.jappgeo.2025.106061","url":null,"abstract":"<div><div>This study develops a rapid, non-contact grouting evaluation method based on the magnetic detectability of magnetized cement slurry (MCS) containing Fe₃O₄ particles. The mechanical, rheological, and magnetic properties of MCS were systematically analyzed to construct a quantitative framework for magnetic signal-based grout detection. Results show that Fe₃O₄ incorporation significantly enhances magnetization without impairing flowability or strength; at W/C = 0.7 and 10 wt% Fe₃O₄, MCS achieved 8969.14 A/m saturation magnetization, 1.10 relative permeability, and compressive strength improvement from 6.83 MPa to 13.86 MPa. Magnetic field mapping and modeling based on the Maxwell-Garnett and surface magnetic charge theories revealed power-law attenuation consistent with experiments, with &lt;3 % error in permeability estimation. Simulations showed that a 1m³ MCS body produced a detectable anomaly (&gt;0.1μT) at 7.9 m, confirming strong remote sensing capability. A practical grouting detection scheme was further developed and validated through curtain grouting simulations for coal mine water control. This work establishes the fundamental mechanisms and quantitative criteria for magnetically traceable grout design and detection, offering a new pathway toward efficient, high-resolution, and non-destructive grouting evaluation in underground engineering.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106061"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737520","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 new method for constructing dynamic-static conversion models for rock mechanics based on acoustic wave propagation characteristics","authors":"Sheng Wang ,&nbsp;Kaizhou Xu ,&nbsp;Haixue Wang ,&nbsp;Xiaofei Fu ,&nbsp;Chengxuan Ren","doi":"10.1016/j.jappgeo.2025.106083","DOIUrl":"10.1016/j.jappgeo.2025.106083","url":null,"abstract":"<div><div>To overcome the limitations of conventional dynamic-static parameter conversion methods characterized by low accuracy and destructive core dependency, this study presents a novel non-destructive approach for high-precision dynamic-static mechanical parameter conversion based on acoustic wave propagation characteristics. Considering that the acoustic wave wavelength is much smaller than the measurement scale of the rock body, a stratified mechanical model for heterogeneous rocks was constructed. The bulk density and reciprocal of wave velocity for each sub-sample layer of the rock sample are set to follow normal distributions, with the variance and expected values of these distributions determined by experimental measurements of wave velocity and bulk density in macroscopic rock samples. Under these constraints, target values of layer parameters are determined through a statistical search algorithm, establishing the dynamic-static mechanical parameter conversion model. Taking Bozhong Oilfield reservoir cores as an example, mechanical and acoustic experiments demonstrate that static parameters obtained with the new method show close agreement with test values, yielding average deviations of 0.048 for Young's modulus and 0.066 for Poisson's ratio. Compared with conventional methods, the dynamic-static conversion relationship developed through this new method proves more effective and accurate in estimating stratum static mechanical parameters when applied to well logging interpretation data. The new method enhances core utilization while maintaining accuracy, offering a cost-efficient solution for reservoir mechanical characterization.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106083"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884267","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":"Deep learning seismic impedance inversion with CVAE-generated labels and bidirectional self-supervised learning","authors":"Heng Su,&nbsp;Junxing Cao,&nbsp;Linsen Zhao,&nbsp;Pengfei Jian","doi":"10.1016/j.jappgeo.2025.106074","DOIUrl":"10.1016/j.jappgeo.2025.106074","url":null,"abstract":"<div><div>In recent years, deep learning has gained increasing attention in the field of geophysics due to its powerful feature extraction and nonlinear modeling capabilities, providing innovative solutions to complex subsurface characterization problems. However, its widespread application remains constrained by two major challenges: limited generalization ability and a heavy reliance on large-scale, high-quality labeled data. Although seismic and well-log data are relatively abundant, accurately paired labeled data remain scarce, which largely limits the application of deep learning in the field of geophysics. To address this problem, we propose a novel label-generation approach based on Conditional Variational Autoencoders (CVAEs), and introduce a novel training framework that integrates supervised and self-supervised learning within a bidirectional learning model. Experiments conducted on both synthetic and real post-stack seismic datasets for impedance inversion demonstrate that the proposed method significantly outperforms baseline method in terms of accuracy and generalization. These results highlight the practicality and robustness of our approach, offering a promising solution to the long-standing challenge of data scarcity in geophysical applications.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106074"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884271","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 variational inference inversion approach to electrical resistivity tomography","authors":"Sean Berti,&nbsp;Mattia Aleardi,&nbsp;Felipe Rincón,&nbsp;Eusebio Stucchi","doi":"10.1016/j.jappgeo.2025.106041","DOIUrl":"10.1016/j.jappgeo.2025.106041","url":null,"abstract":"<div><div>We present a particle-based variational inference approach for Electrical Resistivity Tomography (ERT) using the Annealed Stein Variational Gradient Descent (A-SVGD) algorithm. This approach enables efficient approximation of the posterior distribution while addressing common limitations of standard SVGD—such as mode collapse and underestimation of variance—through a temperature-based annealing strategy. To reduce both the dimensionality of the inverse problem and computational cost, we perform the inversion in a compressed model space using the Discrete Cosine Transform (DCT), aligning the number of particles with the number of parameters—an ideal setting for a SVGD algorithm. We validate our method on both synthetic and real datasets, showing improved convergence and lower data misfit compared to standard SVGD and conventional deterministic inversion, along with effective uncertainty quantification comparable to those provided by a well-established gradient-based Markov Chain Monte Carlo algorithm. These results underscore the potential of A-SVGD, especially when combined with model-space compression, as a scalable and robust framework for nonlinear geophysical inversion.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106041"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694547","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":"Automatic assessing and masking algorithms for electromagnetic transfer functions based on machine learning methods","authors":"Kun Ning ,&nbsp;Hao Dong ,&nbsp;Cheng Guo","doi":"10.1016/j.jappgeo.2025.105989","DOIUrl":"10.1016/j.jappgeo.2025.105989","url":null,"abstract":"<div><div>The magnetotelluric (MT) method is an electromagnetic geophysical technique to investigate the Earth's electrical conductivity structure. It has been widely applied from deep structural studies to near-surface resource explorations. The raw MT time series data are usually transformed and processed into frequency-domain transfer functions (TFs), before being applied in geophysical inversion and interpretations. However, the assessment and elimination of low-quality TF data points still rely heavily on manual operations, which are time-consuming and requiring substantial expertise for operators, thereby reducing the overall efficiency of the data processing workflow. The rise of machine learning (ML) algorithms nowadays has opened up possibilities for rapid and automated data classification, leading to extensive success in fields like finance and image processing. This study explores two popular ML classification algorithms, namely Support Vector Machine (SVM) and Deep Neural Network (DNN), to automatically assess the TF quality. Various data features, such as the difference between a given data point and its neighboring points, were extracted to form a reduced subspace for classification. The classification accuracy of the two algorithms was compared against their manual counterpart, which indicates that the SVM algorithm achieved an accuracy of 93 %, while the DNN algorithm achieved 86 % with the real-world TF data. Consequently, an automated electromagnetic TF masking program based on the SVM algorithm was developed, enabling the accurate and rapid identification and removal of low-quality data points. For instance, manual masking of TF data from a single site may typically require approximately five minutes, whereas the new algorithm accomplishes the task in just a few seconds, significantly enhancing the efficiency of data masking.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"244 ","pages":"Article 105989"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145340660","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":"Research on outburst risk prediction method for coal roadway excavation process based on resistivity- electromagnetic radiation detection","authors":"Qiang Liu ,&nbsp;Liming Qiu ,&nbsp;Yankun Ma ,&nbsp;Dazhao Song ,&nbsp;Miaomiao Yan ,&nbsp;Wei Wang ,&nbsp;Jie Liu ,&nbsp;Limin Qie ,&nbsp;Qi Jia ,&nbsp;Peiwu Liao","doi":"10.1016/j.jappgeo.2025.106011","DOIUrl":"10.1016/j.jappgeo.2025.106011","url":null,"abstract":"<div><div>The safety monitoring of high gas coal seam excavation is a critical measure for the prevention and control of coal and gas outburst accidents. In this paper, the geophysical methods such as direct current (DC) method and electromagnetic radiation (EMR) monitoring were used to evaluate the effectiveness of hydraulic flushing in the coal seam and to monitor and warn the safety of the coal seam roadway. The main conclusions are as follows: An outburst risk prediction method for coal roadway excavation process based on resistivity- electromagnetic radiation detection was proposed. The effective range of hydraulic flushing in the coal seam was found to be 8–12 m using the DC method. The reduction in gas content ranged from approximately 0.2 to 3 m³/t per unit, resulting in an overall decrease of around 40 %. EMR is effective in monitoring the dynamic events of the coal seam boring process. The signal was a fluctuation in the EMR signal after excavation began, reaching its maximum value during a coal burst. A method based on processing EMR-AE data to detect precursor signals is proposed. The Unified Precursor Index (UPI) of 0.75 is used as the early-warning threshold for coal burst events, indicating intense state changes in the coal mass. The UPI allows for the coal burst event to be detected 20 min in advance. The research provides a new perspective for the monitoring of coal rock dynamic disasters.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"244 ","pages":"Article 106011"},"PeriodicalIF":2.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467568","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}