Computers & Geosciences最新文献

{"title":"A novel metric to assess the accuracy of land use change modeling","authors":"Youcheng Song ,&nbsp;Haijun Wang ,&nbsp;Xiaoxu Cao ,&nbsp;Bin Zhang ,&nbsp;Jialin Xie ,&nbsp;Zhijia Gong ,&nbsp;Yaotao Liang ,&nbsp;Zongyou He ,&nbsp;Guanxian Huang","doi":"10.1016/j.cageo.2025.106053","DOIUrl":"10.1016/j.cageo.2025.106053","url":null,"abstract":"<div><div>The integration of the first law of geography into land use change simulation models has attracted considerable attention, aiming to improve model accuracy through the enhanced representation of spatial heterogeneity. However, existing evaluation metrics, which primarily focus on cell-to-cell agreements, inadequately capture the models' ability to represent spatial heterogeneity. Consequently, there is a pressing need for updated evaluation metrics that accurately reflect the models' capability to depict spatial features. To address this issue, the Fuzzy Figure of Merit (Fuzzy FoM) grounded in fuzzy theory was proposed. This metric effectively quantifies and visualizes a model's ability to capture spatial features by introducing the notion of degree of membership, facilitating a comprehensive analysis of model accuracy from both statistical and spatial perspectives. This paper demonstrates the metric's utility in the validation process, illustrating four land use change models that incorporate the spatial heterogeneity.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106053"},"PeriodicalIF":4.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098951","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":"Corrigendum to “Seismic random noise attenuation using structure-oriented 3D curvelet transform” [Comput. Geosci. 206 (2026) 106020]","authors":"Minggui Liang ,&nbsp;Shaohuan Zu ,&nbsp;Zifei Li ,&nbsp;Wenlu Liu ,&nbsp;Haojun Chen ,&nbsp;Zhengyu Tan","doi":"10.1016/j.cageo.2025.106044","DOIUrl":"10.1016/j.cageo.2025.106044","url":null,"abstract":"","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106044"},"PeriodicalIF":4.4,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048989","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":"Uncertainty-aware ensemble learning and dynamic threshold optimization for landslide susceptibility mapping","authors":"Ting Xiao ,&nbsp;Wei Huang ,&nbsp;Lichang Wang ,&nbsp;Beibei Yang ,&nbsp;Zuohui Qin ,&nbsp;Xiaodong Liu ,&nbsp;Yingbin Xiao","doi":"10.1016/j.cageo.2025.106042","DOIUrl":"10.1016/j.cageo.2025.106042","url":null,"abstract":"<div><div>Landslides represent a prevalent and devastating geological hazard. Identifying areas susceptible to landslides is vital for disaster prevention and reduction. However, traditional models suffer from limited predictive accuracy, strong regularity in breakpoint selection for susceptibility zoning, and inconsistent predictions across different models, resulting in uncertainty in susceptibility assessment. To address these issues, this study proposes an innovative intelligent landslide susceptibility mapping approach that integrates ensemble learning, multi-model uncertainty analysis, and dynamic optimization. Focusing on Linxiang City, Hunan Province, China, this research synthesizes historical landslide inventories and field-identified unstable slopes as positive samples. Three base models were constructed: logistic regression (LR), random forest (RF), and graph neural network (GNN). Ensemble learning using the stacking method was applied to combine these models. The ensemble further incorporates prediction uncertainty estimation and multi-dimensional k-nearest neighbor (KNN) adjacency matrix. Utilizing an attention mechanism, the model dynamically integrates geographic features, environmental factors, and prediction outputs. The final output is a prediction model that synthesizes spatial structure information and prediction uncertainties. For susceptibility mapping, this study proposes a dynamic optimization approach combining Natural Breaks, Frequency Ratio, and Equal Interval methods, determining optimal threshold combinations through relative density distribution of landslide occurrences to enhance susceptibility classification rationality. Model performance was evaluated and compared using area under roc curve (AUC), where a larger AUC signifies higher predictive accuracy. The results show that the ensemble model outperformed all others with an AUC of 0.95, compared to the base models' AUCs of 0.82 (LR), 0.84 (RF), and 0.87 (GNN). This demonstrates that the ensemble learning methods that incorporate uncertainty achieve higher accuracy in risk identification than conventional models. The dynamic classification method also shows a better performance over conventional approaches in high-susceptibility classification precision and landslide density differentiation.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106042"},"PeriodicalIF":4.4,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925125","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":"Controlled latent diffusion models for 3D porous media reconstruction","authors":"Danilo Naiff ,&nbsp;Bernardo P. Schaeffer ,&nbsp;Gustavo Pires ,&nbsp;Dragan Stojkovic ,&nbsp;Thomas Rapstine ,&nbsp;Fabio Ramos","doi":"10.1016/j.cageo.2025.106038","DOIUrl":"10.1016/j.cageo.2025.106038","url":null,"abstract":"<div><div>Three-dimensional digital reconstruction of porous media presents a fundamental challenge in geosciences, requiring simultaneous resolution of fine-scale pore structures while capturing representative elementary volumes. This work introduces a computational framework that addresses this challenge through latent diffusion models operating within the Elucidated Diffusion Models (EDM) framework. The proposed approach reduces dimensionality via a custom Variational Autoencoder trained in binary geological volumes, improving efficiency and also enabling the generation of larger volumes than previously possible with diffusion models. A key innovation is the controlled unconditional sampling methodology, which enhances distribution coverage by first sampling target statistics from their empirical distributions, and then generating samples conditioned on these values. Extensive testing on four distinct rock types demonstrates that conditioning on porosity – a readily computable statistic – is sufficient to ensure a consistent representation of multiple complex properties, including permeability, two-point correlation functions, and pore size distributions. The framework achieves better generation quality than pixel-space diffusion while enabling significantly larger volume reconstruction (<span><math><mrow><mn>25</mn><msup><mrow><mn>6</mn></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span> voxels) with substantially reduced computational requirements, establishing a new state-of-the-art for digital rock physics applications.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106038"},"PeriodicalIF":4.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907447","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":"Segmentation of stochastic scalar fields in unstructured meshes","authors":"Tommaso Sorgente ,&nbsp;Marianna Miola ,&nbsp;Simone Pittaluga ,&nbsp;Daniela Cabiddu ,&nbsp;Michela Mortara ,&nbsp;Marino Vetuschi Zuccolini","doi":"10.1016/j.cageo.2025.106041","DOIUrl":"10.1016/j.cageo.2025.106041","url":null,"abstract":"<div><div>We present an algorithm for segmenting a (stochastic) scalar field defined on an unstructured mesh into a given number of parts. It can be applied to any type of mesh, such as triangular/tetrahedral meshes, 2D/3D grids, and generic polygonal/polyhedral meshes, inducing a classification of the mesh elements into regions with limited noise and smooth boundaries. The algorithm offers multiple output options, providing valuable information about the segmentation and the mesh regions in various file formats, thus making it suitable for practical applications. We show the algorithm at work in different application scenarios, ranging from environmental geochemistry to marine sciences and groundwater modeling, proving its efficacy and versatility.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106041"},"PeriodicalIF":4.4,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913354","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":"SmartMagDL: Smartphone geomagnetic mapping using deep learning","authors":"Elad Fisher ,&nbsp;Roger Alimi ,&nbsp;Miki Vizel ,&nbsp;Itzik Klein","doi":"10.1016/j.cageo.2025.106040","DOIUrl":"10.1016/j.cageo.2025.106040","url":null,"abstract":"<div><div>Magnetic field mapping is an essential tool in geoscience, for identifying anomalies and understanding subsurface structures, requiring systematic and methodical data acquisition. The use of smartphones’ built-in magnetometers for this task offers advantages such as cost-effectiveness, accessibility, and simplicity. Recent works relied on model-based interpolation techniques significantly limited by sparse data collection, sensor noise, orientation-dependent distortions, and overall low data quality. As a result, magnetic maps were often noisy and unreliable for practical applications. In this work, we aim to fill this gap by introducing a deep learning (DL) approach to overcome these challenges and produce accurate, high-resolution magnetic field maps from smartphone data. To address the limitations of extensive real-world data collection, we developed an innovative two-stage simulation framework to generate the required training datasets. First, the theoretical magnetic field produced by ferromagnetic objects in a 30 m × 30 m area was computed to serve as ground truth data for the network. Second, a simulation model was implemented to replicate the data acquisition process of smartphone magnetometers. This model included real-world survey protocols, noise factors, sensor behavior, and simulated trajectories based on real world recorded data. Compared to the model-based baseline, our method improves anomaly localization, reduces noise, and enhances accuracy. At the 80th percentile the MSE and LPIPS metrics showed 75% and 55% improvements respectively, further validated by visual analysis of the reconstructed maps.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106040"},"PeriodicalIF":4.4,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903468","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":"EffiSeisM: An efficient multi-task deep learning model for earthquake monitoring","authors":"Lixin Zhang ,&nbsp;Ziang Li ,&nbsp;Zhijun Dai ,&nbsp;Hongmin Liu","doi":"10.1016/j.cageo.2025.106039","DOIUrl":"10.1016/j.cageo.2025.106039","url":null,"abstract":"<div><div>Earthquake monitoring is essential for providing timely warnings, mitigating disaster impacts, advancing scientific research, and guiding urban planning. Precise seismic waveform analysis enables accurate event detection, magnitude estimation, and a deeper understanding of earthquake mechanisms. In this paper, we propose EffiSeisM, an efficient multi-task deep learning model that combines a state space model with a convolutional architecture for earthquake detection, phase picking, and magnitude estimation. EffiSeisM designed a novel Seismic Scale Conv Module and a Conv-SSM encoder, which effectively capture key seismic features while reducing computational complexity. This design ensures high accuracy and operational efficiency, enabling effective seismic analysis. We evaluate EffiSeisM on the DiTing Dataset and DiTing Dataset 2.0, comprising 3 million seismic samples from China and surrounding regions, and compare its performance with several baseline models. The results show that EffiSeisM consistently outperforms the baselines, achieving F1 scores of 0.98 for earthquake detection, 0.92 for phase-P picking, 0.84 for phase-S picking, and an R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.92 for magnitude estimation. Additionally, EffiSeisM demonstrates significant improvements in inference speed and accuracy, highlighting its potential as a scalable and efficient solution for large-scale seismic data analysis.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106039"},"PeriodicalIF":4.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004001","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":"Short-term earthquake forecasting using electromagnetic and geoacoustic observations via contrastive learning","authors":"Yufeng Jiang,&nbsp;Zining Yu,&nbsp;Haiyong Zheng","doi":"10.1016/j.cageo.2025.106024","DOIUrl":"10.1016/j.cageo.2025.106024","url":null,"abstract":"<div><div>Different observations provide earthquake-related information from various perspectives, and effectively leveraging them is essential for enhancing forecasting. Existing data-driven methods primarily rely on concatenation, directly aligning features (e.g., the absolute mean) extracted from different observations side by side. However, this naive approach ignores that each observation reflects a different aspect of the same physical process and inadequately explores cross-observation interactions. To address these issues, we propose CL4EF, a contrastive learning framework that leverages electromagnetic and geoacoustic observations for earthquake forecasting. Specifically, we introduce the synchronous response consistency hypothesis, assuming that different observations within the same time window should respond consistently to the same physical process. Following this hypothesis, we design a contrastive loss that attracts observation pairs from the same station and time window (positives) and repulses others (negatives), enabling cross-observation interaction modeling for the downstream forecasting task. Experimental results demonstrate that CL4EF achieves state-of-the-art performance, improving AUC by 22%. The spatial distribution of forecast probabilities reveals alignment with active fault zones, suggesting the model’s capacity to extract meaningful information for earthquake forecasting. As a result, this study contributes a scalable approach for integrating heterogeneous observations in geosciences and offers new insights into short-term earthquake forecasting.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106024"},"PeriodicalIF":4.4,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879268","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":"Large-scale 3-D magnetotelluric modeling in anisotropic media using extrapolation multigrid method on staggered grids","authors":"Jinxuan Wang ,&nbsp;Kejia Pan ,&nbsp;Hongzhu Cai ,&nbsp;Zhengguang Liu ,&nbsp;Xu Han ,&nbsp;Weiwei Ling","doi":"10.1016/j.cageo.2025.106019","DOIUrl":"10.1016/j.cageo.2025.106019","url":null,"abstract":"<div><div>To improve the practicality and efficiency of 3D magnetotelluric (MT) data inversion, developing a 3D MT forward modeling algorithm with low computational cost in terms of time and memory is an important prerequisite. An extrapolation cascadic multigrid (EXCMG) method is developed on rectilinear grids to accelerate the solving process of large linear systems arising from the staggered-grid finite difference (SFD) discretization of Maxwell’s equations. Arbitrary anisotropic conductivity is considered, without adding extra unknowns to the SFD scheme. A new prolongation operator based on global extrapolation and mixed-order interpolation is developed to tackle the issue caused by non-nested unknown distribution. The divergence correction scheme for arbitrary anisotropy is employed to stabilize the smoothing process, especially for low-frequency cases. Several examples are tested to validate the accuracy and efficiency of the proposed algorithm, including synthetic models with anisotropy and topography, and the real-world Cascadia model. Results show that our EXCMG solver is more efficient than traditional iterative solvers (e.g., the preconditioned BiCGStab), the algebraic multigrid method and the geometric multigrid (GMG) method. The proposed method can efficiently solve large-scale problems with large grid stretching factors and arbitrary anisotropy, providing powerful engine for large-scale MT inversion.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106019"},"PeriodicalIF":4.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886346","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":"Exploiting global information and local edge detail for full waveform inversion","authors":"Yu-Mei Wang ,&nbsp;Qiong Xu ,&nbsp;Ziyu Qin ,&nbsp;Shulin Pan ,&nbsp;Fan Min","doi":"10.1016/j.cageo.2025.106028","DOIUrl":"10.1016/j.cageo.2025.106028","url":null,"abstract":"<div><div>Data-driven deep learning full waveform direct inversion (DL-FWI) has emerged as an advanced technique for predicting subsurface structures. Popular approaches frequently encounter blurry edge pixels and inaccurate velocity values. Here, we propose an algorithm called TU-Net that captures both global information and local edge detail to address these issues. With respect to the network design, we incorporate a texture warping module (TWM) into the skip connections of the U-Net backbone. Due to the multi-scale feature extraction ability of TWM, our network is able to learn details in complex regions. With respect to the loss function design, we introduce the mixed pixel and edge (MPE) loss, which is a combination of the mean absolute error, the mean square error, and the edge-based losses. The newly proposed loss function balances the model’s focus on global pixel features with the local edge characterization, driving the network to produce high-quality edges. We apply the proposed approach on publicly available OpenFWI, SEG salt and Marmousi II datasets. Quantitative results demonstrate that TU-Net achieves better performance in terms of MSE, MAE, LPIPS, PSNR, UIQ, and SSIM than four state-of-the-art deep networks. The source code is available at github.com/fansmale/TU-Net.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106028"},"PeriodicalIF":4.4,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852424","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}