Computers & Geosciences最新文献

{"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":"2026-01-01","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":"MIST: An online tool automating mineral identification by stoichiometry","authors":"Kirsten L. Siebach,&nbsp;Eleanor L. Moreland,&nbsp;Gelu Costin,&nbsp;Yueyang Jiang","doi":"10.1016/j.cageo.2025.106021","DOIUrl":"10.1016/j.cageo.2025.106021","url":null,"abstract":"<div><div>The identification of minerals is fundamental to the use and interpretation of earth and planetary materials. Minerals are defined by their chemistry and crystalline structure. A common way to identify minerals involves using instruments such as an Electron Probe Micro-Analyzer (EPMA) to measure the chemistry of a grain or crystal and compare element ratios to known minerals, i.e. stoichiometry, but this requires user expertise and often some prior knowledge of expected minerals. Here, we present MIST (Mineral Identification by SToichiometry), a mineral-stoichiometry-based model to identify geochemical observations with elemental ratios that match natural mineral compositions. MIST uses normalized oxide weight percentages and stoichiometric ratios between elements in a detailed hierarchical rules-based classification scheme based on validated mineral formulas and compositions to identify mineral phases. The model includes tolerances allowing the vacancies and elemental substitutions common in natural mineral analyses. MIST is focused on rock-forming mineral species containing oxygen and is tested against a standard dataset of validated mineral analyses. The current version of MIST, 3.0, can identify 246 mineral species or stoichiometrically indistinguishable sets of species, with the capability to expand the number of species recognized in future versions. MIST outputs precise mineral formulas, relevant mineral endmembers, and values used in intermediate calculations. As with other mineral identification methods, stoichiometric mineral identifications should be compared to other datasets, including oxide totals, textures, or structural information. We used MIST to filter over a million mineral chemistry analyses in the GEOROC database, resulting in over 875,000 natural mineral analyses with standardized labels, formulas, and mineral descriptors that can be used for machine learning models. MIST provides a rapid, accurate, standardized way to recognize minerals in high-resolution chemical datasets while minimizing required mineralogical expertise.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106021"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879269","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":"2026-01-01","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":"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":"2026-01-01","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":"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":"2026-01-01","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":"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":"2026-01-01","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}

{"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":"2026-01-01","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":"Physics-Informed Fourier-DeepONet for a generalized eikonal solution","authors":"Zhuofan Liu ,&nbsp;Goodluck Archibong ,&nbsp;Umair Bin Waheed ,&nbsp;Sifan Wang ,&nbsp;Chao Song","doi":"10.1016/j.cageo.2025.106026","DOIUrl":"10.1016/j.cageo.2025.106026","url":null,"abstract":"<div><div>The accurate calculation of seismic traveltime based on the eikonal equation has numerous applications in geophysics, such as microseismic localization and tomography. With the advancement of deep learning, the emergence of neural operators has enabled neural networks to learn general solutions to partial differential equations (PDEs). Moreover, Physics-Informed Neural Network (PINN) allows deep learning models to learn under the supervision of PDEs rather than relying solely on training labels. In this context, we propose utilizing a hybrid model that combines the Deep Operator Network (DeepONet) with the Fourier Neural Operator (FNO) to simulate seismic traveltime under the guidance of eikonal equation, thereby yielding a general solution. We refer to this approach as the Physics-Informed Fourier-DeepONet (PI-Fourier-DeepONet). The loss function of the eikonal equation is calculated by finite difference scheme. We evaluate this method across four different types of seismic structures, and the results demonstrate that PI-Fourier-DeepONet is applicable to a wide range of complex geological structures.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106026"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865465","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":"2026-01-01","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":"Regularization by double complementary priors for full waveform inversion","authors":"Hongyu Qi ,&nbsp;Zhenwu Fu ,&nbsp;Yang Li ,&nbsp;Bo Han ,&nbsp;Longsuo Li","doi":"10.1016/j.cageo.2024.105753","DOIUrl":"10.1016/j.cageo.2024.105753","url":null,"abstract":"<div><div>Full-waveform inversion (FWI) represents an advanced geophysical imaging technique focused on intricately depicting subsurface physical properties by iteratively minimizing the differences between the simulated and observed seismograms. Unfortunately, the conventional FWI utilizing a least-squares loss function suffers from various drawbacks, including the challenge of local minima and the necessity for human intervention in parameter fine-tuning. It is particularly problematic when handling noisy data and inadequate initial models. Recent works have exhibited promising performance in two-dimensional FWI by integrating structural sparse representation to procure adaptive dictionaries. Drawing inspiration from the competitiveness of structural sparse representation, we introduce a paradigm of group sparse residuals that integrates two types of complementary prior information by harnessing both the internal and external subsurface media models. The proposed algorithm is based on an alternate minimization algorithm to guarantee workflow flexibility and efficient optimization capabilities. We experimentally validate our method for two baseline geological models, and a comparison of the results demonstrates that the proposed algorithm faithfully recovers the velocity models and consistently outperforms other traditional or learning-based algorithms. A further benefit from the group sparse coding used in this method is that it reduces the sensitivity to data noise.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"194 ","pages":"Article 105753"},"PeriodicalIF":4.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659017","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}