Computers & Geosciences最新文献

{"title":"Semi-analytical method for thermal field analysis of multiple arbitrarily shaped inhomogeneities in heterogeneous geological media","authors":"Guanxiang Ding ,&nbsp;Pu Li ,&nbsp;Xiaowu Luo ,&nbsp;Qinghua Zhou ,&nbsp;Hao Zhu ,&nbsp;Qiang Zhang ,&nbsp;Yanmin Liu","doi":"10.1016/j.cageo.2025.106025","DOIUrl":"10.1016/j.cageo.2025.106025","url":null,"abstract":"<div><div>Natural geological formations typically exhibit heterogeneous thermal properties due to the presence of multiple inhomogeneities, such as mineral inclusions, fractures, or pore clusters, which significantly influence subsurface heat transport. In this work, an effective semi-analytical approach is proposed to investigate the heterogeneous thermal field containing multiple inhomogeneities with arbitrary shapes and various conductivities. Temperature solutions for rectangular elements are constructed from integrated line element temperatures, from which temperature gradients and heat flux are analytically derived. The work features a unified formulation for both the interior and exterior thermal responses of inhomogeneities, avoiding separate treatment of field regions. By Combing the Numerical Equivalent Inclusion Method (NEIM) with two-dimensional Fast Fourier Transform (2D-FFT) algorithms, the proposed approach efficiently solves thermal fields involving both stiff and soft inhomogeneities in heterogeneous media. Furthermore, the method is applied to geostructures, analyzing the thermal distributions of multiple arbitrarily shaped inhomogeneities subjected to remote heat flux. The semi-analytical method demonstrates high accuracy, computational efficiency, and robustness, providing a valuable tool for geoscientific thermal studies.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106025"},"PeriodicalIF":4.4,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781276","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":"DisperPy: A machine learning based tool to automatically pick group velocity dispersion curves from earthquakes","authors":"André V.S. Nascimento ,&nbsp;Carlos A.M. Chaves ,&nbsp;Susanne T.R. Maciel ,&nbsp;George S. França ,&nbsp;Giuliano S. Marotta","doi":"10.1016/j.cageo.2025.106015","DOIUrl":"10.1016/j.cageo.2025.106015","url":null,"abstract":"<div><div>Seismology has made significant progress in high-resolution Earth imaging, largely driven by the increasing volume of freely available data. As a result, automated tools and machine learning algorithms are becoming essential for processing this vast amount of information. We present <em>DisperPy</em>, an open-source Python library developed to automatically extract group velocity dispersion curves from earthquake data. The analysis framework of <em>DisperPy</em> is structured around two primary tasks: (1) assessing the quality of waveforms to determine if dispersion extraction is feasible, and (2) measuring the group velocity dispersion curve for suitable waveforms. To address the first task, <em>DisperPy</em> uses a convolutional neural network trained on dispersion spectrograms to classify waveform quality. The model, based on the ResNet-34 architecture, is initialized with ImageNet-pretrained weights and fine-tuned using the fastai deep learning library. In the test set, the network achieves an accuracy of 92 % in distinguishing between high- and low-quality dispersion images. For the second task, <em>DisperPy</em> employs unsupervised learning techniques, starting with a Gaussian mixture model to separate dispersion energy from background noise, followed by <em>k-means</em> to separate the dispersion energy into clusters, making it easier to track amplitude maxima and then construct initial dispersion curves. Finally, a refinement of the initial dispersion is achieved using both the density-based spatial clustering of applications with noise algorithm and data quality criteria to remove possible outliers. To further test <em>DisperPy</em>, we conduct a surface wave tomography experiment across the contiguous United States using freely available vertical-component broadband waveforms. After processing the data with <em>DisperPy</em> and removing low-quality waveforms, the final dataset consisted of 194,325 unique dispersion curves. Consistent with previous studies, our maps reveal a prominent velocity dichotomy, with low velocities in the tectonically active western US and high velocities in the stable central and eastern US.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106015"},"PeriodicalIF":4.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662576","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 novel class-imbalance learning framework for fluid recognition: Application to Qingshimao-Gaoshawo tight-sand gas reservoirs in the Ordos Basin, China","authors":"Jun Yi ,&nbsp;ZhongLi Qi ,&nbsp;XiangChengZhen Li ,&nbsp;Fuqiang Lai ,&nbsp;Wei Zhou","doi":"10.1016/j.cageo.2025.105993","DOIUrl":"10.1016/j.cageo.2025.105993","url":null,"abstract":"<div><div>The mathematical model-based methods used for conventional oil and gas resources often perform poorly in fluid recognition of tight-sand reservoir, due to the mutual interference of various factors such as reservoir lithology and pore structure. Booming artificial intelligence technologies and accumulating logging data provide a solid foundation for the application of machine learning methods as new tools for fluid identification. However, there is often a serious class imbalance, which can easily lead to the inability to achieve ideal classification results, in the proportion of categories of the collected well logging data. Consequently, this issue has become a huge challenge for the academic and industrial communities. To address this, a novel class-imbalance learning framework for fluid recognition (CILF) is proposed to tight-sand gas reservoirs of Qingshimao-Gaoshawo area of Ordos Basin, in China. Specifically, an improved label propagation algorithm based on semi-supervised learning (SS-LPA) is designed at the data level, which can reduce the imbalance rate of raw data to some extent after assigning high-confidence labels to unlabeled samples. At the model level, <span><math><mi>Q</mi></math></span>-network, as an effective reinforcement learning approach, is introduced into ensemble learning framework (QNEL), which can enhance the multi-classification accuracy of fluid identification by training multiple baseline models that are given different weights for feedback on imbalanced data. The experimental results from 35 tight-sand wells in Qingshimao-Gaoshawo area of Ordos Basin validate the effectiveness of the proposed framework. Specifically, the performance of CILF is the best on all three typical evaluation metrics, and it outperforms others in 12 out of a total of 18 categories. In terms of the average scores for six categories, the precision, recall rate, and F1 score of the proposed framework reach 0.988, 0.984, and 0.985, respectively.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105993"},"PeriodicalIF":4.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654451","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":"ElasWave3D: A GPU-accelerated 3D finite-difference elastic wave solver for complex topography using irregular subdomain index arrays","authors":"Ivan Javier Sánchez-Galvis ,&nbsp;Herling Gonzalez-Alvarez ,&nbsp;William Agudelo ,&nbsp;Daniel O. Trad ,&nbsp;Daniel A. Sierra","doi":"10.1016/j.cageo.2025.105994","DOIUrl":"10.1016/j.cageo.2025.105994","url":null,"abstract":"<div><div>Simulating seismic wave propagation in complex geological structures is a challenging task in exploration geophysics, especially in foothill regions characterized by rough topography, irregular bedrock interfaces, low-velocity surface sediments, and significant heterogeneities. Although existing numerical methods can address such scenarios, they often require highly refined grids that lead to elevated computational costs. To address this, we introduce ElasWave3D, a three-dimensional solver based on the finite difference method for elastic wave propagation in the presence of irregular topography, specifically designed for GPU acceleration. The solver employs a novel Irregular Subdomain Index Array (ISIA) strategy to implement the parameter-modified (PM) formulation, thus enforcing the free-surface condition for arbitrary topographic variations. We validated ElasWave3D against the well-known SPECFEM3D solver in scenarios with rough topography and heterogeneous media, observing misfit errors below 1% and correlation values exceeding 99% in most cases. Additionally, our solver achieves more than an order-of-magnitude speedup (13×) over its CPU-OpenMP implementation on 24 cores. Consequently, ElasWave3D enables cost-effective, realistic, and detailed simulations of near-surface seismic scattering in heterogeneous Earth models with irregular topography.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105994"},"PeriodicalIF":4.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588363","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":"Low-code framework for IoT data warehousing and visualization","authors":"Victor Lamas,&nbsp;Alejandro Cortiñas,&nbsp;Miguel R. Luaces","doi":"10.1016/j.cageo.2025.105998","DOIUrl":"10.1016/j.cageo.2025.105998","url":null,"abstract":"<div><h3>Background:</h3><div>The Internet of Things has revolutionized data collection in geosciences through extensive sensor networks. However, developing web-based data warehousing systems for IoT data remains costly and complex. While studies address sensor variability and data ingestion architectures, they often overlook the critical data warehouse component needed to manage IoT data volume and variability. Additionally, Model-Driven Engineering techniques have been used to create dashboards for urban activities but lack advanced map-based visualizations, which are essential for geospatial data.</div></div><div><h3>Objectives:</h3><div>This study aims to address the challenges of creating IoT data warehouses for geosciences, encouraging scientists to share sensor data analysis results using a simple, user-friendly, and cost-effective approach.</div></div><div><h3>Methods:</h3><div>The proposed framework integrates (i) a Domain-Specific Language metamodel to define sensors, dimensions, and measurement parameters, (ii) a Software Product Line for IoT data warehouse creation, and (iii) a low-code platform with command-line and web interfaces. The approach was validated through four case studies: meteorological, traffic and air quality, coastal, and oceanic monitoring systems.</div></div><div><h3>Results:</h3><div>The framework enables efficient IoT data warehouse creation with customized spatial, temporal, and attribute aggregation. Case studies demonstrate adaptability across domains, supporting real-time data ingestion, sensor mobility, and advanced visualization.</div></div><div><h3>Conclusion:</h3><div>The study presents a scalable, user-friendly framework for IoT data warehousing in geosciences using SPL and DSL technologies, addressing domain-specific challenges and empowering non-expert users. Future work includes usability assessments and expansion to other domains.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105998"},"PeriodicalIF":4.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632362","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":"Efficient variable precision reduction in chaotic climate models: Analysis of the NEMO case in the destination earth project","authors":"Stella V. Paronuzzi-Ticco ,&nbsp;Gladys Utrera ,&nbsp;Mario C. Acosta","doi":"10.1016/j.cageo.2025.105989","DOIUrl":"10.1016/j.cageo.2025.105989","url":null,"abstract":"<div><div>Driven by the need to improve computational efficiency, the technique of reducing variable precision in model calculations has recently attracted a lot of attention, particularly in the field of weather and climate simulations models, where computational gains are crucial to produce operational results faster and make better use of HPC resources.</div><div>However, the source of computational improvements resulting from working in reduced precision, an aspect that could help facilitate the transition in many applications, has never been thoroughly explained. In this paper, we make a step in this direction, shedding light on how to efficiently apply variable precision reduction in chaotic applications, and presenting a computational study methodology to make this possible.</div><div>For this purpose, we employ a tool for automatic porting of oceanographic code to mixed precision recently developed at the Barcelona Supercomputing Center and consider as case studies one of the most widely employed ocean models, NEMO, in one of the most ambitious initiatives to date, Destination Earth, because it aims at creating interactive digital replicas of the Earth with unprecedented precision, supporting real-time decision-making and long-term adaptation strategies, which also entails an unprecedented computational cost in terms of supercomputing. We analyze in depth the impact of mixed precision on the most representative functions of the model, providing a clear step forward in understanding where to focus efforts in precision reduction. These results can guide scientists in significantly speeding up weather and climate models using mixed precision by targeting computationally intensive functions and optimizing communications.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105989"},"PeriodicalIF":4.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580570","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 self-attention convolutional long and short-term memory network for correcting sea surface wind field forecasts to facilitate sea ice drift prediction","authors":"Qing Xu ,&nbsp;Qilin Jia ,&nbsp;Yongqing Li ,&nbsp;Hao Zhang ,&nbsp;Peng Ren","doi":"10.1016/j.cageo.2025.105997","DOIUrl":"10.1016/j.cageo.2025.105997","url":null,"abstract":"<div><div>Accurate and timely correction of numerically forecasted sea surface wind fields is essential for sea ice drift prediction. However, current oceanic element prediction systems face two major challenges. The numerically forecasted sea surface wind fields are timely, but their accuracy is often limited. In contrast, reanalysis sea surface wind fields are more accurate but lack timeliness, limiting their applicability in urgent requirements. To address these challenges, a self-attention convolutional long and short-term memory network (SaCLN) has been developed for intelligently correcting the numerically forecasted sea surface wind fields. This approach combines the timeliness of the numerically forecasted wind fields with the accuracy of reanalysis wind fields to generate corrected wind fields that closely approximate the reanalysis wind fields. This network consists of a self-attention network and a convolutional long and short-term memory network (CLN). The self-attention network captures the global spatial correlations of a numerically forecasted sea surface wind field sequence. The CLN extracts the spatial and temporal characteristics of an attention weighted wind field sequence. The trained SaCLN model can effectively generate accurate and timely corrected wind fields, thereby enhancing the accuracy of sea ice drift prediction. The effectiveness of the SaCLN was validated through experiments predicting the drift of Arctic sea ice and Antarctic icebergs. Experimental results show that the drift results based on wind fields corrected by the SaCLN are more accurate than those based on numerically forecasted sea surface wind fields. This method has demonstrated its effectiveness in sea ice drift prediction, assisting researchers in better addressing the challenges posed by sea ice variability.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105997"},"PeriodicalIF":4.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588365","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":"Seismicity-constrained fault detection and characterization with a multitask machine learning model","authors":"Kai Gao,&nbsp;Ting Chen","doi":"10.1016/j.cageo.2025.105999","DOIUrl":"10.1016/j.cageo.2025.105999","url":null,"abstract":"<div><div>Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 105999"},"PeriodicalIF":4.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672224","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":"Towards an open soil-plant digital twin based on STEMMUS-SCOPE model following open science","authors":"Yijian Zeng ,&nbsp;Fakhereh Alidoost ,&nbsp;Bart Schilperoort ,&nbsp;Yang Liu ,&nbsp;Stefan Verhoeven ,&nbsp;Meiert Willem Grootes ,&nbsp;Yunfei Wang ,&nbsp;Zengjing Song ,&nbsp;Danyang Yu ,&nbsp;Enting Tang ,&nbsp;Qianqian Han ,&nbsp;Lianyu Yu ,&nbsp;Mostafa Gomaa Daoud ,&nbsp;Prajwal Khanal ,&nbsp;Yunfei Chen ,&nbsp;Christiaan van der Tol ,&nbsp;Raúl Zurita-Milla ,&nbsp;Serkan Girgin ,&nbsp;Bas Retsios ,&nbsp;Niels Drost ,&nbsp;Zhongbo Su","doi":"10.1016/j.cageo.2025.106013","DOIUrl":"10.1016/j.cageo.2025.106013","url":null,"abstract":"<div><div>Droughts and heatwaves jeopardize terrestrial ecosystem services. The development of an open digital twin of the soil-plant system can help monitor and predict the impact of these extreme events on ecosystem functioning. We illustrate how our recently developed STEMMUS-SCOPE model—STEMMUS, Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil; SCOPE, Soil Canopy Observation of Photosynthesis and Energy fluxes—links soil-plant processes to novel satellite observables (e.g. solar-induced chlorophyll fluorescence), contributing to such a digital twin. This soil-plant digital twin allows a mechanistic window for tracking above- and below-ground ecophysiological processes with remote sensing observations. Following Open Science and FAIR (Findable, Accessible, Interoperable, Reusable) principles, both for data and research software, we present the building blocks of the soil-plant digital twin. It emphasizes the importance of FAIR-enabling digital technologies to translate research needs and developments into reproducible and reusable data, software and knowledge.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106013"},"PeriodicalIF":4.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686901","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":"DAM-CGNet: Semantic segmentation-based approach for valley-bottom extraction from digital elevation models","authors":"Yuhan Ren,&nbsp;Hongming Zhang,&nbsp;Liang Dong,&nbsp;Huanyu Yang,&nbsp;Hongyi Li,&nbsp;Lu Du,&nbsp;Qiankun Chen,&nbsp;Songyuan Li","doi":"10.1016/j.cageo.2025.106012","DOIUrl":"10.1016/j.cageo.2025.106012","url":null,"abstract":"<div><div>The accurate extraction of valley bottoms from digital elevation models (DEMs) is crucial for hydrological and geomorphological analyses of mountainous landscapes. However, threshold settings rely on manual intervention; roads near valley bottoms resemble valley-bottom features, and thresholds cannot effectively adapt to valleys of various shapes, leading to low extraction accuracy in existing methods, particularly in narrow V-shaped valleys. To address these issues, this study developed a semantic segmentation approach called a Dense-based Attention Merging Context Guided Network (DAM-CGNet). Without relying on thresholds, this method effectively excludes roads on hillslopes and enhances the recognition of steep feature changes at valley boundaries, enabling the extraction of valley bottoms of various shapes. Key improvements include: (1) incorporating the convolutional block attention module (CBAM) to enhance feature reuse in the information flow, employing attention mechanisms to suppress irrelevant feature responses and focus on valley boundary features; (2) using the dense connection strategy of DenseNet to rebuild the feature flow, helping the model keep important valley-bottom details in deep layers and better recognize small and narrow valleys; and (3) addressing the limitations of single-channel DEM representation by evaluating various input combinations, ultimately selecting DEM, topographic position index (TPI), and slope as effective inputs for valley-bottom extraction. Experiments using semantic segmentation models and conventional methods validated the effectiveness of the proposed method. Specifically, DAM-CGNet achieved high accuracy on the test set (MPA: 90.15 %, MIoU: 84.18 %, FWIoU: 92.99 %) and outperformed conventional methods in extracting valley bottoms of various shapes. This method, without a manual threshold setting as in conventional approaches, enhances valley bottom extraction precision and provides a new perspective for subsequent valley bottom width calculations.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106012"},"PeriodicalIF":4.2,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672332","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}