Computers & Geosciences最新文献

{"title":"IsoMapGen: Framework for early prediction of peak ground acceleration using tripartite feature extraction and gated attention model","authors":"Anushka Joshi,&nbsp;Pradeep Singh,&nbsp;Balasubramanian Raman","doi":"10.1016/j.cageo.2024.105849","DOIUrl":"10.1016/j.cageo.2024.105849","url":null,"abstract":"<div><div>Time series data associated with seismic activities pose significant challenges in disaster preparedness. These challenges underscore the need for reliable and timely damage assessments, critical for developing effective response strategies. The computation of Peak Ground Acceleration (PGA) is central to these assessments, serving as a crucial element in generating dynamic damage maps essential for managing rescue operations. Traditional approaches usually derive PGA from full-length accelerograms after an event, a process that is often complicated and prone to delays. In this work, Isoseismal Map Generator (IsoMapGen) is an end-to-end deep-learning framework engineered to predict early PGA using the initial few seconds of the primary waveform. This model integrates a novel spatio-temporal learning approach with gated component-wise attention mechanisms to enhance PGA and magnitude predictions for real-time damage mapping. It employs a chained prediction methodology that dynamically updates damage maps in response to incoming seismic data. The waveform, as well as tabular features extracted from the waveform, are passed in the model. The data imbalance in high-magnitude earthquake records of the tabular datasets has been addressed through synthetic data using a Conditional Tabular Generative Adversarial Network (CTGAN). CTGAN’s application in generating synthetic earthquake indicator data is largely unexplored. A detailed comparative analysis of IsoMapGen has been designed against established baseline models, highlighting its strong performance in real-time applications. The models’ efficacy was demonstrated by successfully predicting site-specific PGA from early three seconds of ground motion related to three recent earthquakes of magnitude 7.6, 6.1, and 5.8 <span><math><msub><mrow><mi>M</mi></mrow><mrow><mi>J</mi><mi>M</mi><mi>A</mi></mrow></msub></math></span>, that occurred on January 01, 2024. This represents notable progress in earthquake damage mitigation using early PGA prediction. Furthermore, this work could be utilized for other short-length time series characterization problems.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105849"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 VTI medium prestack migration method based on the De Wolf approximation","authors":"Huachao Sun,&nbsp;Jianguo Sun","doi":"10.1016/j.cageo.2024.105835","DOIUrl":"10.1016/j.cageo.2024.105835","url":null,"abstract":"<div><div>Anisotropy of velocity is an inherent characteristic of subsurface rock layers, and neglecting its effects can lead to errors in imaging positioning. The present study assumes that subsurface anisotropy follows the VTI (vertically transversely isotropic) medium model and the De Wolf approximation is employed for wavefield computation to enhance imaging accuracy. Drawing on scattering theory, the medium parameters are divided into background parameters (background velocity and anisotropy) and disturbance parameters (velocity and anisotropy disturbances). The mathematical formulation of the De Wolf approximation integral equation in a VTI medium is derived, and a generalized screen approximation (VTI-GS) operator is developed for this medium. The VTI-GS operator is applied to prestack migration. An amplitude attenuation factor is introduced through algorithm implementation and programming to mitigate spatial aliasing and improve migration image accuracy. Error analysis and pulse response test demonstrate that the VTI-GS operator is well-suited for the VTI medium. Migration images for the concave model and the Hess model confirm that the VTI-GS operator yielded higher imaging accuracy than conventional isotropic imaging methods.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105835"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discriminator-based stratigraphic sequence semantic augmentation seismic facies analysis","authors":"Suibao Wang ,&nbsp;Baiquan Yan ,&nbsp;Yu Sun ,&nbsp;Zhenghao Tang","doi":"10.1016/j.cageo.2024.105828","DOIUrl":"10.1016/j.cageo.2024.105828","url":null,"abstract":"<div><div>With the rapid development of deep learning technologies, the seismic facies analysis technique using image classification and image segmentation models has made three-dimensional dense interpretation of seismic facies feasible. However, the application of deep learning models in seismic facies analysis is currently confronted with several challenges. These include difficulties in segmenting seismic facies in structurally complex areas, lower segmentation accuracy for rare categories within seismic facies, and the presence of significant “intra-facies noise” in the segmentation results. To address these issues, we propose a Discriminator-based Stratigraphic Sequence Semantic Augmentation Seismic Facies Analysis model (DSFA). Specifically, the model employs three primary strategies: firstly, the use of the Focal Loss function to enhance the model's learning capability for challenging segmentation samples and rare seismic facies categories; secondly, the utilization of the discriminator to output a Markov Random Field for learning stratigraphic sequence information; and lastly, the adoption of Skip connections between the model's Encoder and Decoder to integrate multi-scale seismic profile information. Experimental findings reveal that the DSFA model effectively addresses prevalent issues in seismic facies analysis, achieving optimal performance across comprehensive evaluation metrics. Additionally, the model is applicable to research in seismic geomorphology.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105828"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing managed artificial recharge backwash using a multi-objective particle swarm optimization coupled with a clogging simulation model","authors":"Tianjiao Zhang,&nbsp;Qi Zhu,&nbsp;Zhang Wen","doi":"10.1016/j.cageo.2025.105869","DOIUrl":"10.1016/j.cageo.2025.105869","url":null,"abstract":"<div><div>Artificial recharge (AR) plays an important role in the management of groundwater resources and the mitigation of hydrogeological problems. However, challenges related to clogging inevitably arise during groundwater recharge. Although the clogging mechanism during groundwater recharge has been intensively studied in the past decades, there is a relative scarcity of studies focused on strategies for preventing clogging through artificial interventions. This study introduces an optimization framework that integrates a clogging model with two objective functions to obtain an optimized backwashing strategy aimed at minimizing clogging during groundwater recharge. The proposed clogging model for the groundwater recharge process considers both physical clogging (attachment of suspended solids) and chemical clogging (iron oxide clogging) by coupling COMSOL and PHREEQC models. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm was used to obtain the Pareto-optimal solutions, by evaluating the clogging conditions and recharge efficiencies of different strategies, which enables stakeholders to determine suitable backwashing frequency and duration among various groundwater backwashing strategies. The results indicate that optimized backwashing strategy can significantly reduce clogging in groundwater recharge projects. With the highest backwashing frequency and duration, clogging near the recharge wells would be reduced to 90% of that observed during normal recharge without strategies, and the time spent on backwashing would only constitute 4.8% of the recharge time.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105869"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 integrated neighborhood and scale information network for open-pit mine change detection in high-resolution remote sensing images","authors":"Zilin Xie ,&nbsp;Kangning Li ,&nbsp;Jinbao Jiang ,&nbsp;Jinzhong Yang ,&nbsp;Xiaojun Qiao ,&nbsp;Deshuai Yuan ,&nbsp;Cheng Nie","doi":"10.1016/j.cageo.2025.105880","DOIUrl":"10.1016/j.cageo.2025.105880","url":null,"abstract":"<div><div>The open-pit mine change detection (CD) in high-resolution remote sensing images plays a crucial role in mineral development and environmental protection. Recent advancements in deep learning have significantly promoted the open-pit mine CD. However, the existing deep-learning-based CD methods encounter challenges in effectively integrating neighborhood and scale information from high-resolution remote sensing images, resulting in insufficient performance. Therefore, according to exploration of the influence patterns of neighborhood and scale information, this paper proposed an integrated neighborhood and scale information network (INSINet) dedicated to open-pit mine CD in high-resolution remote sensing images. Specifically, INSINet introduces 8-neighborhood-image information to expend the receptive field, which improves the recognition of boundary regions in center images. Drawing on techniques of skip connection, deep supervision, and attention mechanism, the multi-path deep supervised attention module is designed to enhance multi-scale information for fusion and change feature extraction. Experimental results demonstrate that incorporating neighborhood and scale information increases the F1 score of INSINet by 6.40%, with improvements of 3.08% and 3.32% respectively. INSINet outperforms existing methods, achieving an overall accuracy of 97.69%, an intersection over union of 71.26%, and an F1-score of 83.22%. INSINet shows significance for open-pit mine CD in high-resolution remote sensing images.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105880"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ensemble Kalman, adaptive Gaussian mixture, and particle flow filters for optimized earthquake occurrence estimation","authors":"Hamed Ali Diab-Montero ,&nbsp;Andreas S. Stordal ,&nbsp;Peter Jan van Leeuwen ,&nbsp;Femke C. Vossepoel","doi":"10.1016/j.cageo.2024.105836","DOIUrl":"10.1016/j.cageo.2024.105836","url":null,"abstract":"<div><div>Probabilistic forecasts are regarded as the highest achievable goal when predicting earthquakes, but limited information on stress, strength, and governing parameters of the seismogenic sources affects their accuracy. Ensemble data-assimilation methods, such as the Ensemble Kalman Filter (EnKF), estimate these variables by combining physics-based models and observations. While the EnKF has demonstrated potential in perfect model experiments using earthquake simulators governed by rate-and-state friction (RSF) laws, challenges arise from the non-Gaussian distribution of state variables during seismic cycle transitions. This study investigates the Adaptive Gaussian Mixture Filter (AGMF) and the Particle Flow Filter (PFF) as alternatives for improved stress and velocity estimation in earthquake sequences compared to Gaussian-based methods like the EnKF. We test the AGMF and the PFF’s performance using Lorenz 96 and Burridge–Knopoff 1D models which are, respectively, standard simplified atmospheric and earthquake models. This approach, using widely recognized and commonly used testbed models in their fields, makes the methods and findings accessible to both the data assimilation and seismology communities, while supporting comparisons and collaboration. We test these models in periodic, and aperiodic conditions, and analyze the impact of assuming Gaussian priors on the estimates of the ensemble methods. The PFF demonstrated comparable performance in chaotic scenarios, yielding lower RMSE for the estimates of the Lorenz 96 models and stronger resilience to underdispersion for the Burridge–Knopoff 1D models. This is vital given the limited and sparse historical earthquake data, underscoring the PFF’s potential in enhancing earthquake forecasting. These results emphasize the need for careful data assimilation method selection in seismological modeling.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105836"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SeisSegDiff: A label-efficient few-shot texture segmentation diffusion model for seismic facies classification","authors":"Tobi Ore,&nbsp;Dengliang Gao","doi":"10.1016/j.cageo.2024.105823","DOIUrl":"10.1016/j.cageo.2024.105823","url":null,"abstract":"<div><div>Traditional seismic facies analysis, which depends on manual interpretation of seimic amplitude, encounters difficulties because of the complexity, volume, and limited resolution of the seismic data. To tackle these problems, seismic texture based deep learning has emerged as a highly promising technique. However, the reliance on extensive labeled datasets poses a significant hurdle. Here we introduce SeisSegDiff, an innovative approach to seismic texture classification that combines diffusion probabilistic models with deep learning models. We use diffusion models to enhance the generalization capabilities and accuracy of deep learning models for seismic facies segmentation. The proposed method utilizes the feature maps obtained from the intermediate layers of a Unet denoising network that estimates the Markov phase of the reverse diffusion process, serving as seismic image representations. These feature maps serve as high-level semantic information for addressing deep learning seismic interpretation challenges, hence reducing the requirement for large, labeled datasets. We assess the effectiveness of SeisSegDiff by conducting experiments on two seismic benchmark datasets from the Netherlands F3 and the Parihaka Basin. The results demonstrate the exceptional performance of our method in defining subsurface facies boundaries and structures. The ability of SeisSegDiff to operate with minimal labeled datasets (∼&lt;1% inlines) further emphasizes its potential for practical field deployments. Our work will draw the geophysical deep learning community closer to the goal of creating a unified global seismic texture model for automatic seismic interpretation and cost effective subsurafe characterization.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105823"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating key parameters impacting the performance of Seis Seg Diff model for seismic facies classification","authors":"Tobi Ore,&nbsp;Dengliang Gao","doi":"10.1016/j.cageo.2024.105829","DOIUrl":"10.1016/j.cageo.2024.105829","url":null,"abstract":"<div><div>Facies are a body of rock that is distinct from adjacent rock units based on observable characteristics such as composition and texture. They are sought out in subsurface characterization tasks because of the valuable information they provide about past environments and geological processes. In seismic data, facies express distinct reflection patterns and are traditionally interpreted manually using seismic attributes. However, manual interpretation is typically time-consuming and biased by the interpreter. Automatic interpretation methods that capitalize on the predictive ability of deep learning have been proposed with relative success. However, these methods are data-intensive with practical deployment limitations. SeisSegDiff is a novel model that draws from the representations learned by diffusion models to classify the facies accurately with limited training data. In this paper, we investigate the quality of the representations learned by the diffusion model and the impact of the model hyperparameters on its performance. We found that for a diffusion denoising encoder-decoder network, the middle decoder blocks [5–13] at the later time steps of the diffusion process [0–250] had the most informative representations for the facies discrimination. For the few shot capability, the model had a mIoU of 0.75 when it was trained with only 3 inlines and its performance consequently increased for more training cross sections with 0.83 when trained with 5 inlines and crosslines, outperforming the state-of-the-art with only ∼2% training data. Furthermore, we found that the model is robust in the presence of faults but struggles with regions with complex salt structures. Our results demonstrate that well designed SeisSegDiff model parameters can greatly speed up subsurface characterization tasks in practical field settings with real seismic and well data. We anticipate the model to be a starting point for more sophisticated applications of the diffusion model for geophysical data interpretation and processing. For example, the learned representations from the diffusion model can lend themselves to the development of a global reservoir property inversion model.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105829"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Siamese topographic generation model: A deep learning model for generating Antarctic subglacial topography with fine details","authors":"Yiheng Cai ,&nbsp;Yanliang He ,&nbsp;Shinan Lang ,&nbsp;Xiangbin Cui ,&nbsp;Xiaoqing Zhang ,&nbsp;Zijun Yao","doi":"10.1016/j.cageo.2025.105857","DOIUrl":"10.1016/j.cageo.2025.105857","url":null,"abstract":"<div><div>The ongoing accumulation of radio-echo sounding (RES) measurements in Antarctica in recent years has significantly expanded our understanding of subglacial structures. The effective use of RES-collected data to construct accurate Antarctic subglacial topography has emerged as a vital component of contemporary polar research. Various methods, including conventional interpolation, inversion techniques, and even deep learning methods, have been used to recreate Antarctic bed topography. However, these bed topographies are often plagued by over-smoothing, loss of small-scale features, low precision, and instability.</div><div>The Siamese topographic generation model (STGM) is proposed here to address the above mentioned issues. After being trained on ArcticDEM, this model can generate Antarctic subglacial topography with stability and accuracy by merging the advantages of deep learning-based generative models, Siamese networks, kernel prediction, and deformable convolutions. In terms of evaluation, both quantitative and qualitative comparisons with current Antarctic subglacial digital elevation models demonstrate that our method can generate topographical features, such as mountains, ice streams, and valleys, with high precision and minimal artifacts. In quantitative validation, our model achieves over 20% improvement in both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) compared to the previously best-performing method (GEI), surpassing existing models in terms of accuracy and detail.</div><div>Moreover, an error analysis specifically focusing on the effect of varying track intervals has been conducted, offering a benchmark for future investigations into the influence of track density on model errors. Finally, using STGM based on the RES data, the subglacial topography of Princess Elizabeth Land has also been successfully generated. In this area, the topography generated by STGM at a resolution of 500 m clearly depicts subglacial lakes and valleys, revealing the complexity and diversity of the subglacial topography.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105857"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Time reversal imaging and transfer learning for spatial and temporal seismic source location","authors":"Anna Franczyk,&nbsp;Damian Gwiżdż","doi":"10.1016/j.cageo.2024.105843","DOIUrl":"10.1016/j.cageo.2024.105843","url":null,"abstract":"<div><div>This article presents the application of Time Reversal Imaging (TRI) and transfer learning methods for spatial and temporal seismic wave location. The study applies the ResNet-50 model, pre-trained on the basis of ImageNet images, and later retrained using seismic wave field component images. The objective of the study was to provide an accurate classification of seismic source areas and determine the temporal localization of seismic events.</div><div>The research involved training the ResNet-50 model based on datasets of wave field component images obtained through the backpropagation of reversed waveforms in simplified geological models. The classification was evaluated using performance metrics. Additionally, to assess its effectiveness in realistic scenarios the methodology was applied to the complex Marmousi velocity model.</div><div>The results show that the combined TRI and transfer learning approach is highly effective in classifying seismic source areas. The trained model successfully identifies patterns unique to seismic wave components, enabling precise spatial localization. Additionally, the method accurately determines the focusing time, which is essential for the temporal localization of seismic events. The article includes research on the influence of receiver network geometry on localization outcomes. By examining various receiver configurations, valuable insights have been gained, further improving the practical applicability of the method.</div><div>The study highlights the potential for further advances by extending the methodology to three-dimensional models, although there remains a need to address various computational challenges. Three-dimensional modeling would enhance the accuracy of source localization, especially in the case of seismic sources characterized by dominant isotropic components.</div><div>In conclusion, the combination of TRI and transfer learning presents a promising approach for ensuring precise spatial and temporal seismic wave location. This methodology has the potential to enhance seismic monitoring, early warning systems, and make a significant contribution to earthquake engineering and hazard assessment.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"196 ","pages":"Article 105843"},"PeriodicalIF":4.2,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}