Computers & Geosciences最新文献

{"title":"Uncertainty quantification using Hamiltonian Monte Carlo for structural geological modelling with implicit neural representations (INR)","authors":"Kaifeng Gao ,&nbsp;Michael Hillier ,&nbsp;Florian Wellmann","doi":"10.1016/j.cageo.2026.106123","DOIUrl":"10.1016/j.cageo.2026.106123","url":null,"abstract":"<div><div>Three-dimensional geological modelling is an essential tool for understanding subsurface features, supporting advanced exploration of natural resources, their sustainable development, and the identification of optimal locations for carbon storage. Recently, efficient neural network approaches have been developed to handle large datasets and to integrate diverse observations and prior knowledge into geological models. Previous work has demonstrated that neural networks are powerful tools for geological modelling, but quantifying uncertainty in their predictions remains an open issue. In this work, we address the uncertainty arising from both network parameters and observational data. We explore the full space of possible geological model realizations using a Hamiltonian Monte Carlo sampler, and quantify the uncertainty of predicted geological interfaces within a Bayesian neural network framework. Our experimental results demonstrate that the Hamiltonian Monte Carlo sampler effectively explores the posterior distribution in function space and quantifies the uncertainty of predicted geological interfaces for both a noise-free borehole dataset from the North Sea and a noisy dataset interpreted from geophysical well logs in Saskatchewan, Canada. We also apply the method to a simple faulting scenario involving a normal fault in flat stratigraphy. Furthermore, in comparison with the commonly used Monte Carlo dropout approach, the Hamiltonian Monte Carlo sampler exhibits superior accuracy in assessing epistemic uncertainty in a noise-free dataset. However, computational efficiency remains a potential challenge in large dataset and network.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"209 ","pages":"Article 106123"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039331","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":"HSDL: A novel and practical method to refine automatic earthquake catalog using hybrid shallow and deep learning","authors":"Daniel Siervo,&nbsp;Yangkang Chen","doi":"10.1016/j.cageo.2025.106103","DOIUrl":"10.1016/j.cageo.2025.106103","url":null,"abstract":"<div><div>Most earthquake detection workflows are based on an optimized short-term-average/long-term-average (STA/LTA) ratio, especially in regions with relatively sparse station geometry and poor velocity models. As the magnitude threshold is lowered to enable more complete earthquake analysis, more false alarms are occurring daily in earthquake monitoring. Here, we propose a high-fidelity approach, called hybrid shallow and deep learning (HSDL), to automatically classify potential earthquakes detected by an optimized STA/LTA workflow as true positives or false positives. To facilitate classification, we leverage an advanced deep learning phase picker, the earthquake compact convolutional transformer (EQCCT), which provides several classification features. These features include the counts of P&amp;S picks, the average, minimum, maximum, and standard deviation of P&amp;S probabilities, and the S/P pick count ratios. On a moderate dataset containing 200 real earthquakes and 200 fake earthquake waveforms, we achieve 100% accuracy across all metrics for both the random forest and XGBoost methods. On a larger dataset of 1500 events, we still achieve a precision of 1.0, a recall above 0.99, and an F1 score above 0.99 for both the random forest and XGBoost methods, with XGBoost achieving slightly higher accuracy. We also analyzed the feature importance and found that the maximum S-pick probability and the S/P pick count ratio play the most critical roles in classification. The proposed method provides a highly effective and efficient approach for fine-tuning the automatic earthquake catalog using the optimized STA/LTA method, leveraging existing tools such as the deep-learning-based phase picker and XGBoost.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"209 ","pages":"Article 106103"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928789","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":"WaveDiffDecloud: Wavelet-domain conditional diffusion model for efficient cloud removal","authors":"Yingjie Huang ,&nbsp;Zewen Wang ,&nbsp;Min Luo ,&nbsp;Shufang Qiu","doi":"10.1016/j.cageo.2026.106121","DOIUrl":"10.1016/j.cageo.2026.106121","url":null,"abstract":"<div><div>Cloud cover frequently occludes up to 60% of optical satellite acquisitions, creating data gaps and radiometric distortions that impede continuous Earth-monitoring applications. Diffusion models have recently demonstrated significant potential for image restoration, but their direct use in cloud removal remains limited by two factors: slow inference due to iterative denoising in high-dimensional pixel space and insufficient preservation of fine structural details, often resulting in texture blurring and boundary artifacts. To address these limitations, we propose WaveDiffDecloud, a wavelet-domain conditional diffusion framework for efficient and high-fidelity cloud removal. Instead of generating pixels directly, our method learns to synthesize the wavelet coefficients of cloud-free images, conditioned on cloudy inputs. This design substantially reduces computational complexity while preserving more fine structures. To further enhance texture fidelity, we introduce a Structure- and Texture-aware High-Frequency Reconstruction module, optimized using a physics-inspired cloud-aware loss. This module explicitly models correlations among high-frequency subbands, enabling accurate recovery of surface textures and sharp boundaries at cloud edges. Experimental results on the RICE and NUAA-CR4L89 benchmarks demonstrate that WaveDiffDecloud achieves state-of-the-art performance. Notably, on the RICE-I dataset, our method achieves the best SSIM of 0.957 and LPIPS of 0.063, significantly outperforming existing methods in texture fidelity while maintaining competitive PSNR. Furthermore, our model exhibits exceptional robustness and spectral consistency across multi-band scenarios ranging from visible to thermal infrared wavelengths. These results highlight the potential of wavelet-based diffusion models to balance reconstruction fidelity and efficiency, paving the way for practical, large-scale cloud removal in optical remote sensing imagery.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"209 ","pages":"Article 106121"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039330","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":"Deep-learning-based hybrid model with iterative lithology constraints for the enhanced prediction of missing well-logs","authors":"Jaesung Park ,&nbsp;Jina Jeong ,&nbsp;Eun-Jung Holden","doi":"10.1016/j.cageo.2026.106106","DOIUrl":"10.1016/j.cageo.2026.106106","url":null,"abstract":"<div><div>Accurate reconstruction of missing well-log data is essential for subsurface characterization and reservoir modeling but remains challenging under conditions of stratigraphic heterogeneity and multi-log incompleteness. This study introduces a deep learning framework that enhance missing log prediction by embedding lithological information as a contextual constraint. The proposed framework integrates a Conditional Variational Autoencoder (CVAE) with a Long Short-Term Memory (LSTM)-based lithology predictor, namely the Iterative Lithology-Constrained Hybrid CVAE–LSTM Network (ILCH-Net), in an iterative refinement process. The model was trained and validated on 45,809 samples from six wells in the Volve oil field, Norwegian North Sea, comprising five commonly acquired logs (GR, RHOB, NPHI, DTC, DTS) across three lithologies (claystone, sandstone, limestone). Quantitative evaluation demonstrates that ILCH-Net surpasses baseline approaches (Autoencoder, Iteratively refined autoencoder, LSTM), achieving lower root mean squared error (10.43 vs. 12.84 for LSTM) and improved distributional similarity (median Kolmogorov–Smirnov statistic of 0.15 with an interquartile range of 0.09 across the six test wells). Lithology-specific analysis further shows that reconstruction accuracy is highest for limestone and claystone, reflecting their distinct well-log responses, while sandstone exhibits greater variability due to depth-dependent compaction effects. These results confirm that lithological constraints not only enhance accuracy but also reduce inter-well variability, thereby yielding geologically consistent reconstructions. By embedding geological priors within a data-driven framework, ILCH-Net provides a robust and scalable solution for applications in reservoir characterization, digital rock modeling, and geomechanical analysis where incomplete or irregular logs are prevalent.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"209 ","pages":"Article 106106"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928790","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":"Fast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorch","authors":"Lei Liu,&nbsp;Chao Song,&nbsp;Liangsheng He,&nbsp;Silin Wang,&nbsp;Xuan Feng,&nbsp;Cai Liu","doi":"10.1016/j.cageo.2025.106101","DOIUrl":"10.1016/j.cageo.2025.106101","url":null,"abstract":"<div><div>This study presents a fast and flexible three-dimensional dual-parameter full waveform inversion (FWI) framework for ground penetrating radar (GPR), enabled by a hybrid compilation strategy that integrates custom CUDA kernel functions with the PyTorch automatic differentiation ecosystem. In the proposed workflow, computationally intensive operations are executed by highly optimized CUDA kernels, while PyTorch is employed only for lightweight tasks. This selective integration substantially reduces memory usage and avoids the runtime bottlenecks often encountered in GPR FWI, achieving an effective balance between efficiency and algorithmic adaptability. The framework supports simultaneous inversion of relative permittivity and electrical conductivity in large-scale 3D domains, providing a practical solution for multi-parameter GPR imaging. Its modular Python-based architecture further allows users to easily customize loss functions, regularization schemes, and optimization settings without modifying code, making the method suitable for rapid prototyping and methodological development. Numerical experiments on 2D and 3D models demonstrate excellent scalability and stable reconstruction performance, while a field-data example confirms that the method can reliably detect subsurface anomalies even under challenging zero-offset acquisition conditions. Overall, the proposed CUDA-PyTorch hybrid framework advances the state of the art in GPR FWI by combining high-performance GPU computing with the flexibility of modern deep-learning toolchains, offering a practical and extensible platform for future GPR FWI research.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"209 ","pages":"Article 106101"},"PeriodicalIF":4.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038649","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":"Enhanced global soil moisture prediction through a sampling-weighted sensitive learning strategy applied to various LSTM-based models","authors":"Xiaoning Li ,&nbsp;Zhichao Zhong ,&nbsp;Qingliang Li ,&nbsp;Cheng Zhang ,&nbsp;Hongwei Zhao ,&nbsp;Xiaofeng Li ,&nbsp;Jinlong Zhu ,&nbsp;Sen Yan","doi":"10.1016/j.cageo.2025.106068","DOIUrl":"10.1016/j.cageo.2025.106068","url":null,"abstract":"<div><div>Soil moisture (SM) plays a critical role in land-atmosphere interactions, influencing both water and carbon cycles. Accurate SM predictions are essential for effective disaster response, optimized irrigation practices, and progress in environmental research. Deep learning (DL) models have become increasingly popular for predicting SM. However, many existing approaches overlook the imbalance in observed data—where moderate moisture levels are far more common than extreme dry or wet conditions. This skewed distribution limits the models' ability to accurately capture rare but critical extremes, ultimately reducing their overall effectiveness. To overcome this limitation, we propose a Sampling-Weighted Sensitive Learning Strategy that improves model generalization by assigning greater importance to rare samples during training. We evaluated this approach using three widely used DL architectures: Long Short-Term Memory Network (LSTM), Bidirectional Long Short-Term Memory Network (BiLSTM), and Gated Recurrent Unit (GRU). To ensure consistency across experiments, the same random seed was applied throughout. Our results demonstrate notable improvements in prediction accuracy when applying the proposed strategy. The BiLSTM model, in particular, showed the most significant gains: unbiased Root Mean Square Error (<em>ubRMSE</em>) decreased by 7.38 %, and <em>Bias</em> was reduced by 11.64 %. Its Kling-Gupta Efficiency (<em>KGE</em>) improved by 2.73 %—slightly below the 5.35 % gain observed for the unidirectional LSTM—but regional results were particularly strong. In data-scarce areas, especially North Africa and Western Asia, BiLSTM KGE improvements frequently exceeded 20 %. Models trained with the proposed strategy also produced narrower 95 % confidence intervals during high-variability periods (e.g., summer and dry seasons), indicating greater predictive robustness under challenging environmental. These findings underscore the importance of addressing sample imbalance in training data and demonstrate the effectiveness of our strategy in enhancing DL models for SM prediction.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106068"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321635","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":"openKARST: A novel open-source flow simulator for karst systems","authors":"Jannes Kordilla,&nbsp;Marco Dentz,&nbsp;Juan J. Hidalgo","doi":"10.1016/j.cageo.2025.106066","DOIUrl":"10.1016/j.cageo.2025.106066","url":null,"abstract":"<div><div>We introduce the open-source Python-based code <span>openKARST</span> for flow in karst conduit networks. Flow and transport in complex karst systems remain a challenging area of hydrogeological research due to the heterogeneous nature of conduit networks. Flow regimes in these systems are highly dynamic, with transitions from free-surface to fully pressurized and laminar to turbulent flow conditions and Reynolds numbers often exceeding one million. These transitions can occur simultaneously within a network, depending on conduit roughness properties and diameter distributions. <span>openKARST</span> solves the transient dynamic wave equation using an iterative scheme and is optimized through an efficient vectorized structure. Transitions from free-surface to pressurized flows in smooth and rough circular conduits are realized via a Preissmann slot approach in combination with an implementation of the Darcy–Weisbach and Manning equations to compute friction losses. To mitigate numerical fluctuations commonly encountered in the Colebrook–White equation, the dynamic switching from laminar to turbulent flows is modeled with a continuous Churchill formulation for the friction factor computation. <span>openKARST</span> supports common boundary conditions encountered in karst systems, as and includes functionalities for network import, export and visualization. The code is verified via comparison against several analytical solutions and validated against a laboratory experiment. Finally, we demonstrate the application of <span>openKARST</span> by simulating a synthetic recharge event in one of the largest explored karst networks, the Ox Bel Ha system in Mexico.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106066"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364038","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":"Integrating Variational Auto-Encoders (VAEs) and spatial interpolation for improving rock mass domaining in open pit mines","authors":"Yakin Hajlaoui ,&nbsp;Jean-François Plante ,&nbsp;Richard Labib ,&nbsp;Michel Gamache","doi":"10.1016/j.cageo.2025.106074","DOIUrl":"10.1016/j.cageo.2025.106074","url":null,"abstract":"<div><div>This study presents a novel method for identifying spatial zones with consistent rock hardness in open-pit mining, a process known as rock mass domaining. The objective is to leverage drilling sensor data to automate the classification of subsurface materials, thereby enhancing blasting efficiency and geological interpretation. The proposed approach combines a variational autoencoder – a type of deep generative model used for dimensionality reduction – with a learnable spatial interpolation mechanism that captures directional trends and geological continuity. Drilling measurements such as penetration rate, torque, weight on bit, and rotation speed are used to infer a latent indicator of rock hardness. This feature is spatially interpolated using a differentiable inverse distance weighting model, compatible with neural network backpropagation. Three neural architectures are compared: fully connected, convolutional, and radial basis function networks. The models were trained and tested on 40 drilling patterns from an iron mine in northern Quebec. The radial basis function variant with an inverse quadratic kernel achieved the best overall performance, with a median domain accuracy of 0.88 and an average pooled standard deviation of 0.31, indicating high spatial cohesion and internal cluster consistency. The approach also demonstrated strong alignment with expert-defined lithological domains and effective detection of disturbed collar zones near the surface. Model training was stable across architectures, and sensitivity analysis confirmed the robustness of hyperparameter choices. While limitations such as Gaussian priors in latent space restrict full modeling of geological multimodality, the framework remains extensible to future enhancements incorporating geological constraints. In summary, this method integrates deep representation learning with spatial modeling to provide interpretable, geologically meaningful domaining. It reduces sensitivity to noisy measurements and enables automated, data-driven mine planning and subsurface characterization.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106074"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467034","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":"MJOFormer: An adaptive land-ocean spatio-temporal transformer for Madden–Julian Oscillation forecasting","authors":"Hongliang Li ,&nbsp;Zhewen Xu ,&nbsp;Zelong Fang ,&nbsp;Nong Zhang ,&nbsp;Changzheng Liu ,&nbsp;Renge Zhou ,&nbsp;Xiaohui Wei","doi":"10.1016/j.cageo.2025.106097","DOIUrl":"10.1016/j.cageo.2025.106097","url":null,"abstract":"<div><div>The Madden–Julian Oscillation (MJO) represents the predominant driver of sub-seasonal variability within tropical regions. In deep learning of weather forecasting, achieving reliable accuracy in MJO prediction remains challenging, making sub-seasonal forecasts generally probabilistic. In this paper, we reveal the challenges that impede MJO forecasts, including distributional drift when the circulation passes from the Indian Ocean across Australia, hindered by obstacles like lands or shoals. In addition, we find it non-trivial to extract the sophisticated spatio-temporal relationships in climate data. To address these issues, we propose MJOFormer, an adaptive land-ocean spatio-temporal transformer, with (1) a land-ocean sampler to address distributional drift by adaptively partitioning across terrain; (2) a dynamic attention mechanism to compensate for the absence of spatial features by adaptively tackling the spatio-temporal correlation; (3) a cuboid method to improve efficiency by parallel training. Comprehensive experiments exhibit that MJOFormer possesses competitive, outperforming existing methods with better accuracy, stability, and efficiency.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"208 ","pages":"Article 106097"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885239","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":"Eigenvector decomposition for joint analysis of spatial characteristics in the North Atlantic from 1979 to 2024","authors":"Andrey K. Gorshenin ,&nbsp;Anastasiia A. Osipova ,&nbsp;Konstantin P. Belyaev","doi":"10.1016/j.cageo.2025.106062","DOIUrl":"10.1016/j.cageo.2025.106062","url":null,"abstract":"<div><div>The extension of the use of Itô stochastic differential equations (SDEs) for joint analysis of spatio-temporal characteristics in the North Atlantic region, such as sea surface temperature (SST), the sum of sensible and latent heat fluxes, and surface atmospheric pressure for the period between 1979 and 2024 is introduced. Previously, this model was used only for the fluxes. The joint point estimates for the random coefficients of SDEs as multidimensional matrices (the drift vector and the diffusion matrix) are obtained for the entire considered period. The numerical estimations of these values were carried out using high-performance computing equipment with software implementation in Python language using the reanalysis data from the ERA5 database. Developed methods and tools are used for the statistical analysis of the temporal evolution of the coefficients of the Itô equation, analysis of joint and marginal diffusion matrices, their finite-dimensional Karhunen–Loéve’s decomposition into eigenvalues and eigenvectors, determination of their interrelations, temporal trends, as well as dynamic visualization on geographical maps of the region under study. The spatial structure of the eigenvectors of the diffusion matrix, their time evolution and the relationship to jet streams and large-scale heat waves that determine latitudinal heat transfer in the North Atlantic are shown. It is also demonstrated that there is a positive trend in the interannual variability in drift and diffusion coefficients. This indicates a quantitative and qualitative increase in the air–sea interaction and the relationship between heat fluxes and ocean surface temperature. It also makes it possible to quantify the energy exchange between the ocean and atmosphere on an interannual scale. The way of using quantities from a stochastic model to improve the neural network forecasts is also discussed.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106062"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269659","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}