Journal of Applied Geophysics最新文献

{"title":"Seismic first-arrival traveltime simulation based on reciprocity-constrained PINN","authors":"Hang Geng ,&nbsp;Chao Song ,&nbsp;Umair bin Waheed ,&nbsp;Cai Liu","doi":"10.1016/j.jappgeo.2025.105967","DOIUrl":"10.1016/j.jappgeo.2025.105967","url":null,"abstract":"<div><div>Simulating seismic first-arrival traveltime plays a crucial role in seismic tomography. First-arrival traveltime simulation usually relies on solving the eikonal equation. The accuracy of conventional numerical solvers is limited to a finite-difference approximation. In recent years, physics-informed neural networks (PINNs) have been applied to achieve this task. However, traditional PINNs encounter challenges in accurately solving the eikonal equation, especially in cases where the model exhibits directional scaling differences. These challenges result in substantial traveltime prediction errors when the traveling distance is long. To improve the accuracy of PINN in traveltime prediction, we incorporate the reciprocity principle as a constraint into the PINN training framework. Based on the reciprocity principle, which states that the traveltime between two points remains invariant when their roles as source and receiver are exchanged, we propose to apply this principle to multiple source–receiver pairs in PINN-based traveltime prediction. Furthermore, a dynamic weighting mechanism is proposed to balance the contributions of the eikonal equation loss and the reciprocity-constrained loss during the training process. This adaptive weighting evolves dynamically with the training epochs, enhancing the convergence of the training process. Experiments conducted on a simple lens velocity model, the Overthrust velocity model, and a 3D velocity model demonstrate that the introduction of the reciprocity-constrained PINN significantly improves the accuracy of traveltime predictions.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105967"},"PeriodicalIF":2.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269137","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":"Structural features and evolution of the Southern Junggar Basin: Insights from discrete numerical simulations","authors":"Wei Qiao ,&nbsp;Chong Shi ,&nbsp;Aifeima Ahetamuxi ,&nbsp;Xuan Tang ,&nbsp;Li-kun Hao","doi":"10.1016/j.jappgeo.2025.105983","DOIUrl":"10.1016/j.jappgeo.2025.105983","url":null,"abstract":"<div><div>The geotectonic movement mechanism and trend prediction are important components of energy exploration and geological structure analysis. Based on the survey results of the Mesozoic-Cenozoic intracontinental extrusion movement in the southern Junggar Basin in China, this study used the particle flow numerical simulation method to study the influence of strata extrusion, denudation, and sedimentation on the formation of topography. The results show that the topography and geomorphology obtained were in line with the survey results when the particle flow method was used to simulate the large-scale geological tectonic movement and the soft bond model and linear contact model were used for the competent and the detachment layers, respectively. The surface topography generated by discrete element method simulations of the deposition and erosion of the surface can be used to identify the average terrain line. The upper and lower parts of the average terrain were set as the erosion and deposition areas, respectively. The denudation-deposition and horizontal extrusion rates were linked to control the reduction and increase of particles, and the extrusion results of the numerical model for the southern Junggar Basin showed that multi-slip delamination extrusion and differential subsidence of the basement were the main reasons for the formation of the geology. When the geological plate was relatively large, anticlines and folds were more difficult to develop during the tectonic process. The direction and rate of extrusion significantly influence the structural morphology. These research results can provide a reference for the mechanistic analysis of tectonic movement.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105983"},"PeriodicalIF":2.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269049","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 clustering of traffic signals using a single seismic station","authors":"Xinyu Liu,&nbsp;Binbin Mi,&nbsp;Jianghai Xia,&nbsp;Jie Zhou,&nbsp;Yulong Ma","doi":"10.1016/j.jappgeo.2025.105979","DOIUrl":"10.1016/j.jappgeo.2025.105979","url":null,"abstract":"<div><div>Vehicle traffic generates vibrations propagating in the subsurface. Identification and clustering of these seismic sources are crucial for traffic monitoring and subsurface imaging. We propose a novel method which uses a single seismic station and deep clustering to categorize the traffic signals. We utilize a deep embedded clustering (DEC) to extract features from frequency-time spectrograms of the recorded seismic signals. The similar traffic signals are grouped according to their key features and further used to infer the type of the vehicles. This deep clustering framework is unsupervised without manual labeling. Synthetic tests achieve a clustering accuracy of more than 99 %. We apply the method to field seismic recordings at three sites nearby the roadside with traffic videos for label validation. Results show an average accuracy of approximately 83 % and 91 % for vehicle type classifications at the intersection sites (Sites 1 and 2), respectively, where there are speed bumps in the roads. The vehicles moving in the near and opposite lanes are also distinguished from each other, with an accuracy of 73.3 % and 90.2 % at Site 1, and 88.4 % and 86.3 % accuracy at Site 2, respectively. At Site 3 along a straight road, the deep clustering model maintains 82 % accuracy for identifying heavy vehicles (buses and trucks), although the classification of small vehicles (cars and bikes) is limited to 58 % due to the relatively weak seismic signals generated by the light vehicles. The results confirm the framework's ability to cluster traffic seismic signals. By addressing the lack of single-station methods for traffic signal classification with unsupervised deep clustering, the proposed method offers a low-cost and scalable alternative to traditional camera-based traffic sensing systems, providing an effective tool for traffic seismic monitoring at the city scale.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105979"},"PeriodicalIF":2.1,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269141","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":"Seismic site classification of Itanagar city, India, considering spatial variation of shear wave velocity obtained using extensive active MASW survey","authors":"Aditya Kumar Anshu ,&nbsp;Jumrik Taipodia ,&nbsp;Shiv Shankar Kumar ,&nbsp;Arindam Dey","doi":"10.1016/j.jappgeo.2025.105981","DOIUrl":"10.1016/j.jappgeo.2025.105981","url":null,"abstract":"<div><div>This study presents an advanced seismic site characterization for Itanagar, Arunachal Pradesh, situated within India's highly seismically active Himalayan belt. The research addresses critical limitations associated with traditional <em>V</em>_<em>s30</em>-based seismic site classifications, highlighting the necessity of incorporating detailed spatial variations of subsurface shear wave velocity (SWV) profiles. Employing Multichannel Analysis of Surface Waves (MASW), SWV data were collected at 22 selected locations, covering diverse geological terrains throughout the city. The MASW methodology involved field data acquisition, dispersion analysis, and inversion to obtain accurate SWV profiles up to 30 m depth. The analysis revealed significant heterogeneity in soil stratification, challenging the reliability of <em>V</em>_<em>s30</em>-based site classifications, particularly for sites exhibiting notable subsurface layer variability. The study demonstrates that relying solely on <em>V</em>_<em>s30</em> values can lead to underestimation or overestimation of seismic response, especially in complex geological environments. To address this, the research introduces an alternative approach of computing average SWV values over discrete 5 m depth intervals, providing a clearer and more realistic depiction of subsurface conditions. This innovative approach better captures soil stiffness variations and their implications for seismic wave amplification. Spatial distributions of SWV were visualized through contour maps, effectively delineating areas with potentially higher seismic amplification risks. Results indicate predominant site classes of C and D under NEHRP guidelines, characterized by medium to dense soils and soft rocks. This refined classification methodology advocates for more accurate site-specific seismic evaluations, emphasizing the importance of detailed subsurface characterization.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105981"},"PeriodicalIF":2.1,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269138","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":"Integration of magnetotelluric and seismic data analysis for delineating geological structures","authors":"Cuong Van Anh Le ,&nbsp;Duy Thong Kieu ,&nbsp;Thuan Van Nguyen","doi":"10.1016/j.jappgeo.2025.105976","DOIUrl":"10.1016/j.jappgeo.2025.105976","url":null,"abstract":"<div><div>Integration of interpretation of two magnetotelluric and seismic results can enhance the Earth's image by leveraging their distinct geophysical properties, namely, resistivity and reflectivity, respectively. High similarity between different geophysical results in the collocated magnetotelluric and seismic profiles can highlight key geological features and mitigate non-uniqueness issues. We applied 2D and 3D inversion approaches to the magnetotelluric data. This process produces a range of possible resistivity scenarios that yield model responses closely matching the field data. Prior to the 2D magnetotelluric inversion, strike analysis was employed to generate a collection of datasets, including the original measurements and versions rotated by different strike angles. In contrast, 3D magnetotelluric inversion utilized the original data directly, without the need for the strike analysis. To evaluate the performance of these inversion approaches in 2D MT profiles, we compare their inverted resistivity results from 3D synthetic magnetotelluric data to the known 3D resistivity model. For seismic data analysis, we incorporate seismic textural attributes as energy, entropy and their k-means clusters to delineate layering and fault structures. To research the geology of Olympic Dam, Australia, we used the workflow that integrated the individual inversion of the real magnetotelluric and seismic datasets to provide spatial distribution of the resistivity, seismic layers, and faults. Boundaries of the resistive blocks and conductive zones match well with the 2D seismic horizons and faults interpreted by the seismic attributes, respectively. The consistency of the electromagnetic inversion results with the seismic data highlights the potential of this workflow in successfully detecting layers, major faults, and the deep Moho interface, confirming its effectiveness.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105976"},"PeriodicalIF":2.1,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269139","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":"An explainable experience-driven hybrid model for TOC prediction in shale reservoirs based on data augmentation","authors":"Yuzhen Hong ,&nbsp;Shaogui Deng ,&nbsp;Zhijun Li ,&nbsp;Yueqin Guo ,&nbsp;Zhoutuo Wei","doi":"10.1016/j.jappgeo.2025.105977","DOIUrl":"10.1016/j.jappgeo.2025.105977","url":null,"abstract":"<div><div>Total Organic Carbon (TOC) content is a measure of the carbon content in organic compounds, commonly used as a critical indicator for assessing unconventional shale resources. Therefore, an accurate TOC prediction model can help evaluate the reservoir's hydrocarbon potential at a low cost and improve the development efficiency. However, the sparsity of experimental data and the high heterogeneity of reservoirs present challenges for TOC prediction. This study proposes combining data enhancement techniques and expert experience-driven machine learning models for accurate TOC prediction in complex shale reservoirs. Firstly, we propose a set of data enhancement methods to address the problems of weak logging response and insufficient TOC experimental data. We enrich the training dataset by introducing reconstruction curves to visualize the response and designing Generative Adversarial Network (GAN) simulations to generate high-quality data. In the experience-driven model construction, we optimized the traditional ΔlogR method by integrating expert knowledge and a detailed analysis of the physical properties of shale reservoirs. We proposed a density-gamma modified ΔlogR method as the core of the experience-driven approach. Furthermore, we integrated the empirical formula into the fitness function of the Grey Wolf Optimizer (GWO). We combined it with a Support Vector Regression (SVR) model to build a hybrid model. The hybrid method was tested in the Dongying Depression. The R² values for wells A and B were 0.95 and 0.97, with Root Mean Square Error (RMSE) values of 0.31 and 0.29, and Mean Absolute Error (MAE) values below 0.3. The prediction results demonstrated significant improvement over any single method. We also analyzed the correlation between well logging curves and prediction results using the SHapley Additive exPlanations (SHAP) method. By revealing the decision-making mechanism within the model, we verified the reasonableness of the experience-driven and enhanced the model's credibility.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105977"},"PeriodicalIF":2.1,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269140","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":"Using logarithmic multi-channel seismoelectric spectral ratios to estimate porosity and permeability","authors":"Ling Zeng ,&nbsp;Hengxin Ren ,&nbsp;Bo Yang ,&nbsp;Kaiyan Hu ,&nbsp;Xuzhen Zheng ,&nbsp;Peng Han ,&nbsp;Zuzhi Hu","doi":"10.1016/j.jappgeo.2025.105974","DOIUrl":"10.1016/j.jappgeo.2025.105974","url":null,"abstract":"<div><div>In this study, we devise a methodology which takes the data of logarithmic multi-channel seismoelectric spectral ratios (LMC-SESRs) as input for a broad learning (BL) plane neural network, aiming to concurrently assess porosity and permeability that are crucial hydrological parameters. We compare the sensitivity of LMC-SESRs data to porosity and permeability for a multilayer model. The results demonstrate that LMC-SESRs data exhibit sensitivity to both porosity and permeability, with a more pronounced sensitivity to porosity. Subsequently, we conduct network training and testing for porosity and permeability reconstruction using both LMC-SESRs and non-logarithmic data as inputs for the BL neural network. The results of testing dataset reveal that using LMC-SESRs data yields better reconstructions of porosity and permeability compared to using non-logarithmic data. After that, we perform simultaneous inversion of porosity and permeability for a multilayer model, validating the effectiveness of our method. Noise resistance tests are also carried out, demonstrating that the proposed method exhibits a good anti-noise ability.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105974"},"PeriodicalIF":2.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269054","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":"An innovative non-electrical logging technique for gas saturation estimation in shaly sandstone reservoirs influenced by the excavation effect","authors":"Bo Shen ,&nbsp;Di Tang ,&nbsp;Bohan Wu ,&nbsp;Yixiong Wu ,&nbsp;Yazhai Wei ,&nbsp;Wenzhi Lan ,&nbsp;Xiubin Ma ,&nbsp;Chao Wang","doi":"10.1016/j.jappgeo.2025.105972","DOIUrl":"10.1016/j.jappgeo.2025.105972","url":null,"abstract":"<div><div>Fluid saturation is a fundamental reservoir property, essential for accurate reservoir characterization and the optimization of exploration and development planning. Various saturation models derived from the Archie equation have consistently been the fundamental approach for saturation interpretation in electrical logging. However, due to differences in reservoir quality, the single fixed rock-electro parameters derived from experiments cannot adequately represent the conductive mechanisms of different reservoir types. This limitation results in saturation predictions that fail to accurately reflect the reservoir's actual gas-bearing properties. Although the “excavation effect” is often applied to identify fluid types in gas-bearing shaly sandstone reservoirs, it has seldom been used for quantitative saturation evaluation. This study is based on the interactive analysis of saturation, the excavation effect, and density-neutron separation degree (separation degree of density and neutron logging curves). It systematically analyzes the response mechanism between density-neutron separation degree and saturation under varying shale content and porosity conditions through numerical simulations. A new predictive method for gas reservoir saturation is developed by formulating a linear model that integrates density–neutron separation, shale volume, and porosity. When applied to the H Formation in the Y Basin, this approach demonstrated superior accuracy. The new DC-based model achieved a relative error of only 1.17 %, which is significantly more accurate than the Archie model (14.81 %) and the Indonesian model (5.63 %). The results validate the accuracy and robustness of the proposed method for gas saturation evaluation, offering practical insights for characterizing heterogeneous shaly sandstone reservoirs.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105972"},"PeriodicalIF":2.1,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269051","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":"Interpretation of generation process and precursors of successive rockbursts in a deep hard-rock water conveyance tunnel by microseismic monitoring","authors":"Fei Liu ,&nbsp;Yongjun Zhang ,&nbsp;Tianhui Ma ,&nbsp;Changhui Liang","doi":"10.1016/j.jappgeo.2025.105973","DOIUrl":"10.1016/j.jappgeo.2025.105973","url":null,"abstract":"<div><div>Rockburst pose substantial threats to safety of workers and underground structures during the construction of deep hard-rock tunnels and mines, and it has been increasing popular among the international rock engineering community in recent decades. This paper studies the generation process and mechanism of two successive, intense rockbursts in the Qinling water conveyance tunnel of the Hanjiang-to-Weihe River Diversion Project in Shaanxi province, China by analyzing the evolution of associated microseismicities and their focal mechanisms. Spectral analysis was performed using the Fast Fourier Transform (FFT) and S-transform to study the frequency-amplitude and time-frequency characteristics of recorded microseismicities during rockbursts generation process. Furthermore, the source parameters and statistical parameters, i.e., the lack of shock <em>b</em> value, the seismic index <em>S</em> value, and the cumulative apparent volume, based on limited microseismic data were analyzed to explore effective precursor for successive rockburst. This study focuses on the generation mechanism and precursors of rockbursts based on limited microseismic data, which is valuable for interpretation of the rockburst failures and prediction of successive rockbursts at short time intervals in the deep hard-rock tunnels.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105973"},"PeriodicalIF":2.1,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226988","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":"Full waveform inversion constrained to well log velocity data and seismic data using random mixing","authors":"A. Chang ,&nbsp;L. Gross ,&nbsp;S. Hӧrning","doi":"10.1016/j.jappgeo.2025.105968","DOIUrl":"10.1016/j.jappgeo.2025.105968","url":null,"abstract":"<div><div>Full Waveform Inversion (FWI) is a state-of-the-art technique for reconstructing high-resolution subsurface velocity models. However, conventional deterministic FWI is highly sensitive to the initial model and does not provide uncertainty quantification, while Bayesian FWI, although capable of addressing uncertainty, often incurs substantial computational cost. To bridge the gap between these two frameworks, previous work introduced a stochastic approach known as Random Mixing (RM). The method generates a collection of velocity models that are all reproduced given observational data and are conditional on a known geostatistical characterization in the form of a spatial correlation and marginal distribution. In this study, we extend the RM method for FWI by incorporating well-log information alongside seismic wavefield data. Vertical velocity profiles obtained from well logs are used to estimate the required geostatistical parameters, and the generated velocity realizations are constrained to honor the well-log measurements. We demonstrate the effectiveness of this approach using two test cases, including one with a simulated anisotropic layered velocity structure. The tests show that data provided by well logs allow for estimating geostatistical parameters with an accuracy sufficient for successful RM FWI and that restriction to velocity realizations conditional on well log data reduces uncertainty in the RM inversion results. The results validate the effectiveness of RM under both linear and non-linear constraints.</div></div>","PeriodicalId":54882,"journal":{"name":"Journal of Applied Geophysics","volume":"243 ","pages":"Article 105968"},"PeriodicalIF":2.1,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269136","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}