Acta Geophysica最新文献_第10页

Few-shot learning for dynamic anomaly detection in seismic ambient noise monitoring 地震环境噪声监测中动态异常检测的少弹学习方法

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-05-26 DOI: 10.1007/s11600-026-01902-8

Xiaomin Wu, Fang Ye

{"title":"Few-shot learning for dynamic anomaly detection in seismic ambient noise monitoring","authors":"Xiaomin Wu, Fang Ye","doi":"10.1007/s11600-026-01902-8","DOIUrl":"10.1007/s11600-026-01902-8","url":null,"abstract":"<div><p>Seismic ambient noise cross-correlation has become an essential tool for monitoring subsurface dynamics, offering continuous data acquisition and broad spatial coverage. To address the dense and repetitive of geological anomaly events, we propose a few-shot anomaly detection method integrating deep temporal modeling with statistical distance metrics. By integrating Transformer-based temporal modeling with Mahalanobis distance metrics for anomaly detection, a time–frequency few-shot learning framework for multi-type anomaly recognition under limited samples is developed. Additionally, an adaptive learning rate adjustment strategy is proposed, which detects data drift by measuring the Euclidean distance between new data samples and historical class prototypes. The experimental results demonstrate that the baseline model achieves accuracies of 96.5% and 98.5% on 4-way 1-shot and 4-way 5-shot tasks, respectively, with 200 training samples. Even under extreme conditions (40 samples, SNR = 6), the model maintains strong performance (≈ 85% accuracy). Upon introducing the dynamic update mechanism, the model achieves an average accuracy of 94.5% during continual learning. Each iteration takes 1.1 s, with parameter optimization requiring only 0.6 s, thus satisfying near-real-time processing requirements for the model update process. The proposed method is validated using continuous 320-day monitoring data from an operational seismic network, achieving an average classification accuracy of 91.8% under complex environmental interference. Visualization of the model’s attention distribution highlights high-energy regions in the time–frequency domain, offering physically grounded interpretability for anomaly analysis.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148172911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 novel method to predict the P- and S-wave polarities of downhole microseismic events based on hybrid deep learning model 基于混合深度学习模型的井下微地震纵波和纵波极性预测新方法

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-05-24 DOI: 10.1007/s11600-026-01904-6

Junping Zhang, Qinghui Mao, Tahir Azeem, Xiurong Li, Xuliang Zhang, Zhixian Gui, Shijie Zhou

{"title":"A novel method to predict the P- and S-wave polarities of downhole microseismic events based on hybrid deep learning model","authors":"Junping Zhang, Qinghui Mao, Tahir Azeem, Xiurong Li, Xuliang Zhang, Zhixian Gui, Shijie Zhou","doi":"10.1007/s11600-026-01904-6","DOIUrl":"10.1007/s11600-026-01904-6","url":null,"abstract":"<div><p>Accurate microseismic monitoring is a key step in unconventional oil and gas fracturing monitoring, including core tasks such as inversions of microseismic source location and source mechanism solution. Accurate P- and S-wave polarities can ensure reliable waveform stacking in subsequent migration-based location and enable fast inversion of the microseismic source mechanism. Currently, existing methods are primarily limited to predicting P-wave first-motion polarities. In this work, we have presented a hybrid deep learning architecture integrating a convolutional neural network (CNN), a long short-term memory (LSTM), and a multi-head attention mechanism, aiming to simultaneously estimate P- and S-wave first-motion polarities. We constructed a comprehensive dataset based on synthetic data and actual fracture monitoring data to carry out experimental tests. Compared with the traditional CNN, CNN-LSTM, or single-head attention mechanism models, the proposed model demonstrated optimal performance in estimating P- and S-wave first-motion polarities. For P- and S-waves, it achieved respective accuracies of 92.45% and 97.43%, with a combined average of 94.94%, a macro precision of 94.96%, a macro recall of 94.89%, and a macro-F1 score of 94.92%. Ten-fold cross-validation demonstrated its robustness and strong generalization ability. Furthermore, the example tests demonstrated the model’s ability to estimate the first-motion polarities of P- and S-waves under different signal-to-noise ratio conditions (encompassing high and low levels).</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

UH-Net: U-Net with hybrid attention for full waveform inversion UH-Net: U-Net混合关注全波形反演

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-05-23 DOI: 10.1007/s11600-026-01870-z

Zheng Zhang, Ziyu Qin, Liyan Liu, Fan Min

{"title":"UH-Net: U-Net with hybrid attention for full waveform inversion","authors":"Zheng Zhang, Ziyu Qin, Liyan Liu, Fan Min","doi":"10.1007/s11600-026-01870-z","DOIUrl":"10.1007/s11600-026-01870-z","url":null,"abstract":"<div><p>Data-driven inversion methods are gaining popularity due to their remarkable ability to learn from data rather than relying on physical assumptions. Existing deep learning full waveform inversion methods, such as FCNVMB, demonstrate excellent performance by training a U-shaped network (U-Net). However, the original U-Net lacks the ability to capture correlations between local and global information. In this paper, we embed the Vision Transformer (ViT) with hybrid attention into U-Net to address this issue. First, we design a hybrid attention mechanism (HAM) that incorporates the existing ViT’s multi-head self-attention mechanism and a local windowed attention mechanism. It captures both global and local dependencies to improve the model’s prediction capability. Second, we design a new loss function by combining a customized Canny loss and the mean square error loss. Due to the close velocity values between some strata in data, many unclear boundaries in the prediction results can be mitigated. Third, we devise a transfer learning fine-tuning scheme to address the issue of insufficient training data of the SEGSalt dataset. This scheme requires only fine-tuning to transfer the exceptional performance of the pre-trained model to other datasets. Experiments are undertaken on OpenFWI and SEGSalt dataset. Results demonstrate that UH-Net outperforms two state-of-the-art data-driven methods. The code is available at https://github.com/fansmale/uhnet.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Enhancing deep learning based RMT data inversion using Gaussian random field 利用高斯随机场增强基于深度学习的RMT数据反演

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-05-22 DOI: 10.1007/s11600-026-01876-7

Koustav Ghosal, Arun Singh, Samir Malakar, Shalivahan Srivastava, Deepak Gupta

{"title":"Enhancing deep learning based RMT data inversion using Gaussian random field","authors":"Koustav Ghosal, Arun Singh, Samir Malakar, Shalivahan Srivastava, Deepak Gupta","doi":"10.1007/s11600-026-01876-7","DOIUrl":"10.1007/s11600-026-01876-7","url":null,"abstract":"<div><p>Deep learning (DL) methods have emerged as a powerful tool for the inversion of geophysical data. When applied to field data, these models often struggle without additional network fine-tuning. This is because they are built on the assumption that the statistical patterns in the training and test datasets are the same. To address this, we propose a DL-based inversion scheme for Radio Magnetotelluric data where the subsurface resistivity models are generated using Gaussian random fields (GRFs). The network’s generalization ability was tested on two distinct datasets, each with a significant distribution shift from the GRF used for training. One dataset consisted of a homogeneous background with various rectangular anomalous bodies. The second dataset, designed to be more challenging, represented a geologically realistic scenario with multiple layers and faults. After end-to-end training with the GRF dataset, the pre-trained network successfully identified board anomalies in the unseen data. Synthetic experiments confirmed that the diversity in the GRF dataset enhances generalization compared to a homogeneous background dataset. The network accurately recovered structures in the resistivity model and demonstrated robustness to noise, outperforming traditional gradient-based methods. Finally, the developed scheme is tested using exemplary field data from a waste site near Roorkee, India. The proposed scheme enhances generalization in a data-driven supervised learning framework, suggesting a promising direction for DL based inversion methods in Radio magnetotelluric data.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Research on recognition of rockburst microseismic signal based on deep convolution neural network attention mechanism algorithm 基于深度卷积神经网络注意机制算法的岩爆微震信号识别研究

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-05-21 DOI: 10.1007/s11600-026-01897-2

Guili Peng, Tong Wang, Honglei Li, Shoubin Wang, Tong Shen, Denghui Jin, Shuming Gong, Jialun Zhang

{"title":"Research on recognition of rockburst microseismic signal based on deep convolution neural network attention mechanism algorithm","authors":"Guili Peng, Tong Wang, Honglei Li, Shoubin Wang, Tong Shen, Denghui Jin, Shuming Gong, Jialun Zhang","doi":"10.1007/s11600-026-01897-2","DOIUrl":"10.1007/s11600-026-01897-2","url":null,"abstract":"<div><p>Rockburst is a common geological hazard encountered in deep-buried tunnel and mining engineering, and its occurrence is closely related to rock mass fracturing activities. Microseismic monitoring technology captures signals generated by rock fracture, providing important data support for analyzing underground rock mass behavior. However, in practical engineering applications, microseismic signals are often contaminated by blasting, mechanical, and environmental noise, and manual identification suffers from low efficiency and poor stability. Therefore, accurately distinguishing effective microseismic signals from various types of noise is a key issue in microseismic data analysis. This study focuses on the task of microseismic signal classification and proposes a novel deep learning model, GhostRegNet-CBAM. The model integrates the Ghost module and the Convolutional Block Attention Module (CBAM) to enhance feature extraction capability and classification performance. A dataset is constructed based on measured data from the Baihetan Hydropower Station, and classification experiments are conducted on microseismic signals, blasting noise, mechanical noise, and environmental noise. Experimental results show that the proposed model outperforms the original RegNet model in terms of recognition accuracy and classification performance and demonstrates stronger robustness in complex noise environments. This study provides an effective method for the automatic identification of microseismic signals under complex geological conditions and can provide methodological support for subsequent research on the identification and classification of rockburst microseismic signals.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 mixed-order minimum entropy regularization for inversion of magnetic data 磁资料反演的混合阶最小熵正则化

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-05-21 DOI: 10.1007/s11600-026-01896-3

Mohammad Rezaie

{"title":"A mixed-order minimum entropy regularization for inversion of magnetic data","authors":"Mohammad Rezaie","doi":"10.1007/s11600-026-01896-3","DOIUrl":"10.1007/s11600-026-01896-3","url":null,"abstract":"<div><p>The inversion of magnetic data is a crucial geophysical technique for imaging subsurface structures, but it is inherently non-unique. Regularization is essential to obtain geologically plausible solutions, with stabilizers based on L<sub>2</sub> norms often producing overly smooth models that obscure boundaries, while sparse constraints can lead to artificially concentrated features with exaggerated susceptibilities. This paper introduces a novel framework for magnetic data inversion utilizing a mixed-order minimum entropy stabilizer, which integrates both first- and second-order probability measures within a pseudo-quadratic form. The proposed method is designed to mitigate the limitations of its individual components, striking a balance between the excessive focusing of first-order minimum entropy and the over-smoothing of second-order minimum entropy stabilizers. To achieve the solution, we use a reweighted regularized conjugate gradient (RRCG) algorithm as an efficient approach. The efficacy of this approach is demonstrated through comprehensive testing on two synthetic models—a dipping dike and two cuboids—and a field dataset from the Nikka volcanogenic massive sulfide (VMS) deposit in Ontario. Results show that the mixed-order stabilizer consistently generates compact models with clearly defined boundaries and accurate susceptibility values, outperforming the conventional methods. This study confirms that the mixed-order minimum entropy regularization provides a robust and effective tool for producing geologically realistic subsurface models from magnetic data, enhancing interpretation accuracy in exploration geophysics.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 ensemble learning framework for forecasting root zone soil moisture in semi-arid regions of South Bihar 比哈尔邦南部半干旱区根区土壤湿度预测的深度集成学习框架

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-05-21 DOI: 10.1007/s11600-026-01887-4

Ravi Patel, Aditya Kumar, Niharika Koch, Gurpreet Singh, Jainath Yadav

{"title":"A deep ensemble learning framework for forecasting root zone soil moisture in semi-arid regions of South Bihar","authors":"Ravi Patel, Aditya Kumar, Niharika Koch, Gurpreet Singh, Jainath Yadav","doi":"10.1007/s11600-026-01887-4","DOIUrl":"10.1007/s11600-026-01887-4","url":null,"abstract":"<div><p>Accurate forecasting of root zone soil moisture (RZSM) is crucial for effective groundwater management, irrigation planning, and drought mitigation in semi-arid agrarian regions such as South Bihar. Traditional hydrological models mostly struggle to figure out the nonlinear temporal dynamics inherent in soil moisture data. This study proposes a hybrid deep ensemble learning framework that leverages the strengths of six deep neural networks (LSTM, Bi-LSTM, GRU, Bi-GRU, RNN, and CNN) as base models, with eXtreme Gradient Boosting (XGBoost) employed as a meta-learner in a stacked ensemble architecture. Each model is independently trained on Groundwater Root Zone Soil Wetness (GWETROOT) time series data (Jan 1985 to June 2025), and their predictions are aggregated using XGBoost to generate a robust final forecast. The study utilizes daily RZSM data (GWETROOT, surface to 100 cm depth) obtained from the NASA POWER project, which provides satellite- and model-based gridded estimates. The performance of all models was evaluated across five districts in Bihar (Arwal, Aurangabad, Gaya, Jehanabad, and Nawada) using standard statistical metrics including MAE, MAPE, RMSE, MSE, and <span>(R^2)</span>. Results demonstrate that the proposed ensemble approach consistently outperformed individual models, offering improved accuracy (<span>(R^2)</span> = 0.99) and generalizability. The findings highlight the effectiveness of integrating deep learning with ensemble techniques for soil moisture forecasting and offer a scalable solution for climate-resilient water resource management.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 novel hybrid framework: vine copulas synergized with multiscale decomposition for enhanced daily river discharge simulation 一种新的混合框架：藤copula与多尺度分解协同用于增强河流日流量模拟

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-05-21 DOI: 10.1007/s11600-026-01872-x

Majid Rastgari, Samad Emamgholizadeh, Emad Mahjoobi, Mohammad Nazeri Tahroudi

{"title":"A novel hybrid framework: vine copulas synergized with multiscale decomposition for enhanced daily river discharge simulation","authors":"Majid Rastgari, Samad Emamgholizadeh, Emad Mahjoobi, Mohammad Nazeri Tahroudi","doi":"10.1007/s11600-026-01872-x","DOIUrl":"10.1007/s11600-026-01872-x","url":null,"abstract":"<div><p>This study evaluates the performance of hybrid models combining vine copula with wavelet decomposition and complete ensemble empirical mode decomposition (CEEMD) for daily river discharge simulation at two hydrological stations (Abajalo and Tapik in Iran). The proposed approaches—C-vine-wavelet (decomposition levels 2, 3, and 4) and C-vine-CEEMD—were assessed using multiple statistical metrics, including the root mean square error (RMSE), the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and the Nash–Sutcliffe statistic (NSE), Willmott’s Index, and Explained Variance Score, along with Taylor diagram analysis for comprehensive model comparison. Results indicate that the C-vine-wavelet (level 3) model consistently outperformed other methods, achieving the lowest RMSE (2.99 m<sup>3</sup>/s at Abajalo and 4.81 m<sup>3</sup>/s at Tapik) while maintaining high accuracy in trend detection (NSE > 0.6, Willmott’s Index > 0.9). The C-vine-CEEMD model demonstrated superior capability in capturing temporal variability (highest NSE = 0.72 at Abajalo) but exhibited higher errors in peak flow estimation. Taylor diagram analysis confirmed that both top-performing models maintained a strong correlation (<i>R</i> ≈ 0.8–0.9) with the observed data, although with a slight overestimation in extreme events. A key finding was the station-dependent performance of the models, with CEEMD showing better results at Abajalo than Tapik, suggesting that hydrological characteristics influence model effectiveness. The wavelet-based approach at level 3 decomposition provided an optimal balance between noise reduction and signal preservation, making it the most reliable choice for discharge simulation. The findings support the use of C-vine-wavelet (level 3) as a robust method for daily discharge prediction, while emphasizing the need for site-specific model calibration in practical applications.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Dual-event arrival-time picking for downhole microseismic monitoring using a fully convolutional network 使用全卷积网络进行井下微地震监测的双事件到达时间选择

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-05-21 DOI: 10.1007/s11600-026-01898-1

Jiali Pan, Xiao Tian, Yichong Chen, Yuxing Pan, Xiong Zhang

{"title":"Dual-event arrival-time picking for downhole microseismic monitoring using a fully convolutional network","authors":"Jiali Pan, Xiao Tian, Yichong Chen, Yuxing Pan, Xiong Zhang","doi":"10.1007/s11600-026-01898-1","DOIUrl":"10.1007/s11600-026-01898-1","url":null,"abstract":"<div><p>Accurate arrival-time picking is essential for downhole microseismic event localization and hydraulic-fracturing evaluation. Traditional methods (e.g., STA/LTA) and deep-learning networks are typically designed for single-event picking, making it difficult to reliably identify all phases when multiple events occur within the same time window. To address this challenge, this study proposes a fully convolutional network (FCN)-based dual-event detection and arrival-time picking model for downhole microseismic monitoring. The model processes fixed-length three-component waveform data, and P-waves and S-waves are encoded with opposite Gaussian probability distributions to enhance phase discrimination. Trained on 80 downhole microseismic events with extensive data augmentation, the model is validated on field data and compared with the STA/LTA method, the PhaseNet model, and a single-event FCN model. Results demonstrate superior dual-event recognition and arrival picking performance, achieving F1 scores of approximately 95% for both P-waves and S-waves and maintaining strong robustness under low-SNR conditions. This study provides an efficient and reliable solution for multi-event microseismic monitoring, offering valuable support for underground engineering safety and shale gas well early-warning applications.</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148009304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Modeling of SH wave propagation induced by point source in gradient fluid-saturated porous layered structure with geophysical application 梯度流体饱和多孔层状结构点源SH波传播模拟及其地球物理应用

IF 2.1 4区地球科学

Acta Geophysica Pub Date : 2026-05-20 DOI: 10.1007/s11600-026-01867-8

Snehamoy Pramanik, Shalini Saha, Ch. Jayanthi

{"title":"Modeling of SH wave propagation induced by point source in gradient fluid-saturated porous layered structure with geophysical application","authors":"Snehamoy Pramanik, Shalini Saha, Ch. Jayanthi","doi":"10.1007/s11600-026-01867-8","DOIUrl":"10.1007/s11600-026-01867-8","url":null,"abstract":"<div><p>This study develops a theoretical framework for shear-horizontal (SH) wave propagation generated by a point source in a fluid-saturated porous layer overlying a gradient porous half-space. The formulation employs Biot’s poroelastic theory extended to include spatially varying permeability and tortuosity in the lower half-space, thereby representing realistic heterogeneity. The governing equations of motion and fluid continuity are solved analytically using separation of variables, while boundary and interface continuity conditions are rigorously enforced to obtain a complex dispersion relation. The real and imaginary parts of this relation characterize the phase velocity and attenuation of SH waves, respectively. A numerical root-searching algorithm is implemented to extract physically admissible solutions over a range of non-dimensional frequencies, and parametric studies are performed to investigate the effects of gradient strength, layer thickness, and frequency on dispersive and damping behaviors. The results show strong agreement with existing analytical models and demonstrate enhanced predictive capability for graded porous systems. The proposed formulation extends classical poroelastic wave theories to heterogeneous media and provides valuable insights for subsurface characterization, seismic site response analysis, and poromechanical modeling of layered geomaterial.\u0000</p></div>","PeriodicalId":6988,"journal":{"name":"Acta Geophysica","volume":"74 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148008971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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