Journal of Applied Geophysics最新文献

{"title":"Characterization and Applications of Favorable Coal–Rock Architectures Based on Seismic Facies Boundaries: The Ordos Basin","authors":"ZeLei Jiang ,&nbsp;Xuri Huang ,&nbsp;Dong Zhang ,&nbsp;YuCong Huang ,&nbsp;Yong Wu","doi":"10.1016/j.jappgeo.2025.106092","DOIUrl":"10.1016/j.jappgeo.2025.106092","url":null,"abstract":"<div><div>The Ordos Basin is one of the most resource-rich and critical regions for deep coalbed methane gas within China, in which the efficient development of this methane gas is key to increasing reserves and boosting production. The gas-bearing content of coal–rock systems is largely controlled by their internal architectural configurations. Seismic detection plays a critical role in attempts to convert coalbed methane gas resources into recoverable reserves and increase production capacity. However, unconventional self-sourced and trapped coal–rock gas reservoirs exhibit distinct geological features. Coalbeds are generally characterized by limited thicknesses, complex capping lithologies, and laterally heterogeneous architectures. These complexities hinder a clear understanding of their architectural patterns and seismic response signatures, resulting in underdeveloped seismic detection methods. To address these challenges and achieve high-resolution characterizations of favorable coal–rock architectures, we here focus on a representative area in the Yulin region of the Ordos Basin. By integrating basic geological coal–rock types with gas-enriched architectural features, favorable coal–rock architectures in the study area were classified into three distinct types: dual-layer limestone–coal, integrated mudstone–coal, and integrated sandstone–coal. The geophysical response characteristics of these architectures were then identified using seismic forward modeling of favorable architectural models. After selecting sensitive seismic attributes, a neural network-based multi-attribute clustering method was applied to characterize the spatial distribution of favorable coal–rock facies architectures. In addition, image-processing edge detection techniques were used to delineate the lateral boundaries of each type of architecture. Herein, an innovative methodology is proposed for seismic- and well-data integration to achieve the fine-scale characterization of favorable coal–rock architectures under facies-type and architectural boundaries. Our findings provide both theoretical insights and technical guidance for the efficient exploration and development of coalbed methane gas in the Ordos Basin.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"246 ","pages":"Article 106092"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145903959","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":"Target-oriented full waveform inversion based on optimal transport theory","authors":"Tianjing Shen ,&nbsp;Xiaochun Chen ,&nbsp;Kai Chen ,&nbsp;Yukai Wo ,&nbsp;Xuri Huang","doi":"10.1016/j.jappgeo.2026.106120","DOIUrl":"10.1016/j.jappgeo.2026.106120","url":null,"abstract":"<div><div>Target-oriented full waveform inversion (TOFWI) provides an efficient strategy for high-resolution imaging in local regions of interest, but its effectiveness is often limited by two main challenges: the need for redatuming of the acquisition system and the sensitivity of conventional <span><math><msub><mi>L</mi><mn>2</mn></msub></math></span>-norm-based TOFWI (<span><math><msub><mi>L</mi><mn>2</mn></msub></math></span>-TOFWI) to initial models and data quality. In this study, we propose an optimal transport (OT)-based TOFWI framework to overcome these limitations. First, we apply the Marchenko redatuming method to retrieve virtual reflection responses that isolate the target-zone wavefield. Then, we incorporate OT-based misfit functions, including graph-space OT (GSOT) and Kantorovich-relaxed OT (KROT), to enhance the convexity of the inversion landscape and improve robustness against noise and velocity-model inaccuracies. KROT introduces entropy regularization via the Sinkhorn algorithm, leading to smooth transport plans and improved numerical stability, whereas GSOT relies on a discrete assignment formulation that yields sparse transport plans and higher sensitivity to local waveform variations. Numerical experiments demonstrate that the proposed OT-TOFWI framework delivers more accurate and stable reconstructions than conventional <span><math><msub><mi>L</mi><mn>2</mn></msub></math></span>-TOFWI, particularly under conditions of significant initial-model errors and low signal-to-noise ratio. Furthermore, comparisons with global full waveform inversion highlight that OT-TOFWI achieves better resolution in deeper structures with lower computational cost. These results confirm that integrating Marchenko redatuming with OT-based misfit functions provides a promising pathway for reliable target-oriented seismic imaging in complex geological settings.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"246 ","pages":"Article 106120"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079091","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":"Automated microseismic classification in deep coal seams: Application to stress redistribution and fault reactivation in the Dongtan coal mine","authors":"Jinhai Liu ,&nbsp;Rui Xu ,&nbsp;Kai Zhan ,&nbsp;Jiajun Chen ,&nbsp;Guangming Li ,&nbsp;Chao Kong","doi":"10.1016/j.jappgeo.2026.106093","DOIUrl":"10.1016/j.jappgeo.2026.106093","url":null,"abstract":"<div><div>Understanding how stress redistribution and structural reactivation evolve during deep coal mining is essential for assessing seismic hazards. In this study, we develop an automated microseismic classification workflow that integrates PhaseNet-based P-wave picking, residual-guided multi-window trimming, short-time Fourier transform (STFT) spectrogram generation and a dynamic-attention convolutional neural network to identify mining-induced and tectonic events in real time. The workflow is first trained and validated on labelled microseismic waveforms, achieving 93% overall accuracy on a five-class test set (blast, microseismic, earthquake, noise and others). We then deploy it on five high-SNR stations (WDZ4–WDZ8) at the 6306 working face of the Dongtan Coal Mine, where it captures the progressive transition from blast-dominated to tectonic-dominated microseismicity as mining advances into faulted zones. This trend, interpreted together with independent geological mapping and published focal-mechanism and stress-inversion results, indicates enhanced stress transfer and structural activation within the surrounding strata. Overall, the results demonstrate that intelligent seismic classification can quantitatively track the coupling between mining activities and geological structures, providing a practical tool for stress monitoring and early warning in deep coal seams.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"246 ","pages":"Article 106093"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928695","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":"Horizontal fracture prediction in shale gas reservoirs based on a generalization-enhanced framework integrating rock physics-driven data augmentation and CNN","authors":"Xiaodong Zhang,&nbsp;Zhiqi Guo,&nbsp;Cai Liu","doi":"10.1016/j.jappgeo.2025.106075","DOIUrl":"10.1016/j.jappgeo.2025.106075","url":null,"abstract":"<div><div>Fracture detection is essential for characterizing shale gas reservoirs. Although amplitude variation with azimuth methods are widely applied to predict vertical fractures, identifying horizontal fractures remains challenging due to their complex seismic responses, which differ from azimuthal anisotropy signatures. Quantitative seismic interpretation that integrates rock physics modeling with deep learning provides a promising framework for horizontal fracture prediction. However, the representativeness of available data poses a key limitation in areas with sparse borehole control, constraining the generalization capability of predictive models. A generalization-enhanced framework that combines rock physics-driven data augmentation with convolutional neural networks (CNN) is proposed to address this limitation. A shale-specific rock physics model for horizontal fractures is first established, followed by a model-based inversion scheme to estimate horizontal fracture density from well logs. The estimated fracture densities are then statistically expanded as random variables to generate augmented datasets that simulate spatial variability beyond borehole control. Corresponding elastic properties are computed using the rock physics model, forming physics-constrained datasets for CNN training. Cross-validation results demonstrate that the proposed data augmentation strategy reduces the root-mean-square error (RMSE) of horizontal fracture density estimation by approximately 14 %. Field application further confirms that the augmented model improves consistency with log-derived fracture densities and mitigates spurious anomalies compared with the non-augmented approach. The proposed framework thus provides a physics-guided and data-augmented methodology for robust prediction of horizontal fracture density, offering enhanced fracture characterization in shale gas reservoirs.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106075"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840194","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":"Effect of data filtering on source mechanisms inverted from surface microseismic monitoring array","authors":"Toufik Chtouki ,&nbsp;Matej Petružálek ,&nbsp;Frantisek Staněk ,&nbsp;Leo Eisner ,&nbsp;Zuzana Jechumtálová ,&nbsp;Naveed Iqbal ,&nbsp;Umair bin Waheed","doi":"10.1016/j.jappgeo.2025.106070","DOIUrl":"10.1016/j.jappgeo.2025.106070","url":null,"abstract":"<div><div>The source mechanisms of induced microseismic events help understanding underground operations and mitigating hazards associated with induced seismicity. However, the uncertainty in the inverted source mechanisms is not well understood. In this study, we examine the impact of digital filters applied to dense surface monitoring data on the inverted source mechanisms derived from P-wave amplitudes. Ten filters, designed and used to increase signal to noise ratio, were tested. Filtering strongly affects both the shear and non-shear components of the full moment tensor. The differences in shear component orientation can exceed 20° in Kagan angle for some filters, despite the excellent coverage provided by the monitoring network. By constraining the inversion to pure shear mechanisms, the orientation was more stable. The smallest errors were observed with bandpass, interferometry, wavelet (with a well-chosen wavelet), and Wiener filters. On the other hand, the SVD and AGC filters resulted in largest changes in source mechanisms. Our results show that data filtering can lead to significant errors in the source mechanisms, which could potentially be misinterpreted if used to infer stress or other reservoir parameters.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106070"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884367","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":"Deblending of simultaneous source seismic data in common shot domain based on multi-output U-shaped net transformer","authors":"Binghui Zhao ,&nbsp;Liguo Han ,&nbsp;Laiyu Lu ,&nbsp;Xiaomiao Tan","doi":"10.1016/j.jappgeo.2025.106017","DOIUrl":"10.1016/j.jappgeo.2025.106017","url":null,"abstract":"<div><div>In seismic data acquisition, the simultaneous source technique has been widely used by virtue of its high acquisition efficiency. After collecting a large amount of simultaneous source data, the simultaneous source data needs to be deblended. Nevertheless，the highly coherent and intricate entanglement of aliased signals with desired signals poses a significant hurdle for effective shot deblending. Conventional deblending methods require determining the specific excitation time of each shot, and based on this, performing operations such as pseudo deblending, channel set conversion, and denoising. This not only requires high accuracy of the excitation time, but also is a complicated operation that requires denoising each shot separately, which is computationally huge. We designed a multi-output U-shaped Net Transformer (UNetr)based on the principles of imaging. By utilizing a transformer, which is more sensitive to positional information, as an encoder, this network can distinguish the waveform characteristics of different single shots and separate the blended data directly in the common shot channel set. After testing, the method is more capable for coherent signals and more effective for deblending of overlapping shots. Without relying on time coding, the method skips the complex processing flow. The processing efficiency is improved and the deblending effect is significant.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106017"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145625388","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 ERT advanced detection imaging of goaf floor in coal mining face based on random forest algorithm","authors":"Pengyu Wang,&nbsp;Xiaofeng Yi,&nbsp;Shumin Wang","doi":"10.1016/j.jappgeo.2025.106053","DOIUrl":"10.1016/j.jappgeo.2025.106053","url":null,"abstract":"<div><div>Water inrush of goaf floor is one of the most important factors threatening the safety production of coal mines, which often causes great economic losses and casualties. After the goaf floor is filled with water, the apparent resistivity value decreases significantly. Therefore, the electrical resistivity tomography (ERT), which is sensitive to low-resistivity anomalous bodies such as water, has a unique advantage in the detection of water in goaf floor. At present, the main method for advanced detection of goaf floor is ERT three-point-source method, but this method can only realize one-dimensional positioning of the water-bearing body in goaf floor, which is easy to misjudge the location of the water-bearing body in practical application. To solve this problem, the random forest algorithm is used to process the advanced detection data, and then the apparent resistivity contour map of the goaf floor is predicted, which simplifies the measurement process and realizes two-dimensional positioning of the water-bearing body in goaf floor. Its effectiveness has been proved by the verification experiments, and the prediction accuracy reaches 98.86 %. This method is used to detect the goaf floor in Ji 17–33,200 coal mining face, and the location of the suspected water-bearing body has been determined.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106053"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737522","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 first arrival picking for low signal-to-noise ratio data based on supervirtual interferometry and deep learning","authors":"Xuefeng Gao ,&nbsp;Weiping Cao ,&nbsp;Ranran Yang ,&nbsp;Xuri Huang ,&nbsp;Wensheng Duan ,&nbsp;Zhongbo Xu","doi":"10.1016/j.jappgeo.2025.106060","DOIUrl":"10.1016/j.jappgeo.2025.106060","url":null,"abstract":"<div><div>First arrival picking is an important step in seismic data processing, as its accuracy and efficiency directly impact the quality and turnaround time of near-surface velocity models and even the overall seismic processing result. This step can be very challenging for seismic data acquired in regions with complex near-surface structures, such as foothills and desert, where seismic data exhibit low signal-to-noise ratios (SNR) and first arrival picking is critical for effective subsurface exploration. To address these challenges, we propose an automated first arrival picking method that integrates supervirtual interferometry (SVI) with deep learning (DL) to achieve robust picking under low-SNR conditions. Our two-stage framework first employs SVI to enhance the first arrival signals in low-SNR seismic traces, thereby recovering the first arrival signals in low-SNR regions. Subsequently, to correct the impact of the pre-arrival artifacts introduced by SVI, an improved U-Net neural network architecture is properly trained with labels containing these pre-arrival artifacts to achieve accurate first arrival picking for SVI output. Tests on synthetic seismic traces and field low-SNR data from complex near-surface geologic condition demonstrate that this method achieves reliable results under low SNR conditions without human intervention, and verify this approach as a viable tool for automatic picking of first arrival times for low SNR seismic data.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106060"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840191","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":"Rock physics modeling and seismic responses of deep carbonate rocks in Tahe oilfield","authors":"Chenglong Wu ,&nbsp;Hemin Yuan ,&nbsp;Xin Zhang","doi":"10.1016/j.jappgeo.2025.106072","DOIUrl":"10.1016/j.jappgeo.2025.106072","url":null,"abstract":"<div><div>Controlled by multi-stage tectonic-karst processes and high-temperature high-pressure (HTHP) environment, the deep carbonate reservoir in Tahe oilfield has complex seismic responses. To characterize these intricate seismic responses, we integrate a comprehensive rock physics modeling workflow with seismic forward modeling to bridge micro-scale elastic properties and macro-scale seismic signatures. We constructed a dual-porosity rock model by combining the differential effective medium (DEM) model and Gassmann equation. Critically, we incorporated temperature-pressure-salinity corrections for fluid properties and modeled the effects of pressure and temperature on the rock frame. The model was then used to generate reservoir parameters for seismic forward modeling. The modeling results demonstrated that the Gassmann equation outperformed the DEM model, and P-wave velocity prediction was improved by adding HTHP corrections and salinity. The seismic forward modeling results revealed that porosity and pore structure are the dominant controls on seismic features, with fluid type being minor unless gas is present. This study quantitatively characterized the seismic rock physics properties of the deep carbonates in the Tahe Oilfield, providing a robust method for accurately predicting velocities and seismic responses of carbonates in similar geological settings.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106072"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840307","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":"2.75-D Global joint inversion of gravity and magnetic anomalies with appraisal of model reconstruction uncertainty","authors":"Yunus Levent Ekinci ,&nbsp;Hanbing Ai ,&nbsp;Çağlayan Balkaya ,&nbsp;Arka Roy","doi":"10.1016/j.jappgeo.2025.106036","DOIUrl":"10.1016/j.jappgeo.2025.106036","url":null,"abstract":"<div><div>Inversion procedures are fundamental tools for reconstructing causative sources of gravity and magnetic anomalies. While 2-D polygonal and 3-D polyhedral models can represent irregularly shaped bodies, a more flexible framework is needed to bridge the gap between computational simplicity and geometric realism. To address this, we propose a 2.75-D global joint inversion scheme based on the nature-inspired Hunger Games Search (HGS) metaheuristic algorithm. Synthetic tests involving modal and sensitivity analyses were carried out to identify potential difficulties and uncertainties in the considered problem, revealing that the global optimizer must efficiently balance the critical trade-off between global exploration and local exploitation. The proposed scheme was applied to synthetic anomalies, with and without noise, and benchmarked against the probabilistic Hamiltonian Monte Carlo algorithm. Two field datasets were then analyzed, and the results were interpreted considering existing geological and geophysical knowledge. Post-inversion analyses confirmed the reliability of the estimated models, while compact inversion and correlation imaging techniques supported the HGS outcomes. Notably, joint inversion consistently improved convergence and reduced estimation errors compared to individual inversions by exploiting the complementary sensitivities of gravity and magnetic data. The 2.75-D approach enhances geometric flexibility while maintaining a parsimonious parameterization. HGS is an efficient and robust optimizer for joint inversion of gravity and magnetic anomalies, capable of producing geologically plausible models with rapid convergence and minimal uncertainty.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"245 ","pages":"Article 106036"},"PeriodicalIF":2.1,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694550","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}