Computers & Geosciences最新文献

{"title":"TorchTEM3D: PyTorch-Driven forward modeling platform for fast 3D transient electromagnetic modeling and efficient sensitivity matrix calculation","authors":"Ziteng Li ,&nbsp;Hai Li ,&nbsp;Keying Li ,&nbsp;Ahmed M. Beshr","doi":"10.1016/j.cageo.2025.106063","DOIUrl":"10.1016/j.cageo.2025.106063","url":null,"abstract":"<div><div>The three-dimensional (3D) forward modeling of transient electromagnetic (TEM) data is often computationally demanding due to its high complexity and limited hardware acceleration, which also affects the efficiency of sensitivity matrix calculation. In recent years, deep learning frameworks, particularly PyTorch, have been widely used in various fields due to their high flexibility, parallel computing capabilities, and powerful automatic differentiation function. In this paper, we develop a time-domain finite-difference forward modeling platform for 3D TEM, named TorchTEM3D, based on the powerful parallel computing and GPU acceleration capabilities of PyTorch. By fully utilizing the automatic differentiation function of PyTorch, we achieve efficient and fast calculation of sensitivity matrix (the gradient of the electromagnetic response to the geoelectric model). Compared with existing open-source Python computing platforms such as SimPEG and custEM, our method improves computing speed by 15–60 times. Furthermore, high-precision sensitivity matrices can be obtained with a single forward modeling run.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106063"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269658","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":"PoreViT: Automated pore typing in carbonate rocks using vision transformers and neighborhood features","authors":"Yemna Qaiser ,&nbsp;Mohammed Ishaq ,&nbsp;Mohammed Yaqoob ,&nbsp;Mohammed Yusuf Ansari ,&nbsp;Isaac Sujay ,&nbsp;Talha Khan ,&nbsp;Harris Rabbani ,&nbsp;Juan Carlos Laya ,&nbsp;P.J. Moore ,&nbsp;Thomas Daniel Seers","doi":"10.1016/j.cageo.2025.106071","DOIUrl":"10.1016/j.cageo.2025.106071","url":null,"abstract":"<div><div>The classification of pores into their intrinsic depo-diagenetic or petrophysical morphotypes is a fundamental practice within carbonate petrography, providing linkage between pore-scale textures and their associated petrophysical signatures and/or paragenetic histories. Typically, pore classification is performed manually in a qualitative/semi-quantitative manner, which is hampered by inefficiency, subjectivity, and a lack of scalability. Though aimed at addressing the limitations of manual pore classification, efforts to automate petrographic pore-typing through artificial intelligence and computer vision techniques are limited by the inability of models to classify pores into genetic classes solely based upon simplistic size and shape features, which have been the focus of the existing literature. To address this nuanced classification problem, we present PoreViT: a Vision Transformer (ViT) model used to classify macropores observed in thin-sections into their respective Lucia classes (interparticle, touching vug, separate vug). The core novelty of PoreViT lies in its Feature Fusion block, which integrates ViT features, enhanced by a Global Token Addition layer, with spatial features extracted from a Convolutional Neural Network (CNN). Critically, our classifier leverages neighborhood information to provide the model with localized pore system topology, recognizing that pore types need to be identified not just by shape but also by their local spatial context. Trained and tested using 4115 labels obtained from 25 high-resolution thin-section scans, PoreViT provides an accurate, automated classification of carbonate macropores, achieving precision and recall values of 0.92 and 0.93 (macro-F1 0.92) corresponding to absolute improvements of ＋4.0% and ＋4.0%, and relative gains of ＋4.54% and ＋4.5%, respectively, over the best-performing CNN model (DenseNet121). The high throughput pore-textural classification capabilities demonstrated herein offer unprecedented opportunities in the integrated quantitative characterization of carbonates.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106071"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467036","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":"Dual watermarking algorithm for trajectory data based on vector decomposition","authors":"Heyan Wang ,&nbsp;Luanyun Hu ,&nbsp;Yuchen Hu ,&nbsp;Changqing Zhu ,&nbsp;Wang Zhang ,&nbsp;Na Ren","doi":"10.1016/j.cageo.2025.106072","DOIUrl":"10.1016/j.cageo.2025.106072","url":null,"abstract":"<div><div>Trajectory data security demands simultaneous copyright protection and spatiotemporal integrity authentication, yet existing watermarking methods struggle with irreconcilable trade-offs between robustness, reversibility, and temporally-aware tamper detection. Traditional approaches often introduce irreversible geometric distortions under affine transformations and lack mechanisms to validate timestamp authenticity, leaving critical vulnerabilities in precision-sensitive applications. This paper proposes a dual watermarking framework integrating geometric-invariant reversible embeddings and semi-fragile temporal authentication. The method decomposes trajectory coordinates into affine-invariant α/β coefficients using vector decomposition, enabling high-capacity copyright watermark embedding resilient to geometric attacks. Temporal integrity verification is achieved through Pearson-correlation analysis of timestamp sequences, detecting malicious deletions with complete accuracy while tolerating legitimate temporal shifts. A bidirectional quantization modulation scheme guarantees lossless coordinate recovery, reducing restoration errors to sub-microradian precision by synchronizing geometric invariants with relative distance preservation. Comprehensive evaluations across 68,632 real-world trajectories demonstrate superior performance: the framework achieves normalized correlations above 0.94 against 45° rotation/200 % scaling attacks, 21 % higher robustness than state-of-the-art methods under compression, and full-error detection of timestamp tampering. By unifying geometric invariance, temporal causality verification, and reversible modulation within a single architecture, this work establishes a new paradigm for dual-function security in spatiotemporal data management, with direct applicability to GIS.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106072"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417519","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":"Implicit neural representations for 3D gravity inversion","authors":"Xiong Li ,&nbsp;Jingtao Zhao ,&nbsp;Shuai Zhou","doi":"10.1016/j.cageo.2025.106082","DOIUrl":"10.1016/j.cageo.2025.106082","url":null,"abstract":"<div><div>Three-dimensional gravity inversion is vital for recovering subsurface density distribution, but resolving sparse geological anomalies with complex geometries from superimposed gravity signals remains difficult due to severe ill-posedness. Existing methods often face limitations in representational flexibility or rely heavily on predefined regularization strategies. We introduce the physics-driven Implicit Gravity Inversion (IGI) method to reconstruct the underground density distribution in geophysical exploration, which employs a neural network to implicitly represent the continuous density field directly from spatial coordinates. Critically, IGI is driven by physical laws embedded within the loss function, optimizing the neural network to predict a density field consistent with observed gravity anomalies, thus eliminating the need for extensive labeled training data typical of deep learning. Results show that IGI provides comparable anomaly distributions to those obtained using deep learning inversion. Ablation studies reveal that parameter coupling provides effective depth weighting, indicating that architectural choices control regularization strength within well-defined optimal ranges. Extensive experiments on synthetic benchmarks and the real-world San Nicolas deposit dataset demonstrate that IGI robustly recovers sparse and complex density models with high accuracy, effectively delineating intricate structures such as disconnected ore bodies and sharp contacts, surpassing traditional methods in these aspects.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"208 ","pages":"Article 106082"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694905","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":"Optimising the computational performance of high degree lithospheric field models","authors":"Michael Bareford ,&nbsp;William Brown ,&nbsp;Ciarán Beggan ,&nbsp;Callum Watson ,&nbsp;Mark Bull","doi":"10.1016/j.cageo.2025.106092","DOIUrl":"10.1016/j.cageo.2025.106092","url":null,"abstract":"<div><div>The British Geological Survey (BGS) World Magnetic Anomaly Model (WMAM) code calculates spherical harmonic models of the natural magnetisation of the rocks of Earth’s crust. These models allow us to estimate the value of the full magnetic field vector at any location, based on scattered pointwise marine or aero-magnetic measurements of only the scalar magnetic field. Modelling the magnetic field in this way serves many important purposes, such as geological research, navigation and safe resource extraction. Global spherical harmonic models of degree and order 1440 (<span><math><mo>∼</mo></math></span>28 km spatial resolution) have been successfully computed on the HPC facilities local to BGS, but such runs require nearly the full compute capacity for multiple days. Further, the available resolution of the scalar field measurements is too high to be fully exploited by the WMAM code, limiting models of the crustal magnetic field to a resolution of 28 km. To overcome these issues, we refactored the WMAM code such that models of spherical harmonic degree 1440 and 2000 (<span><math><mo>∼</mo></math></span>20 km resolution) can be produced in hours rather than days. For example, a degree 2000 model was calculated using 64 HPE Cray EX nodes (8<!--> <!-->192 cores) in 3 h and 44 mins. The resulting model power spectra and magnetic field maps showed excellent agreement with the existing degree 1440 model and the original input data. The performance of the WMAM code was further improved via offloading to GPU. We show the improvements due to GPU acceleration in terms of energy consumption as well as runtime. This fruitful collaboration between experts in the fields of Geoscience (BGS) and HPC (EPCC) has created the opportunity for the WMAM code to be used to gain new knowledge about crustal magnetic fields.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"208 ","pages":"Article 106092"},"PeriodicalIF":4.4,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791272","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":"Research on hyperspectral remote sensing alteration mineral mapping using an improved ViT model","authors":"Xinhang Feng ,&nbsp;Jiejun Huang ,&nbsp;Ximing Chen ,&nbsp;Han Zhou ,&nbsp;Ming Zhang ,&nbsp;Chuan Zhang ,&nbsp;Fawang Ye","doi":"10.1016/j.cageo.2025.106037","DOIUrl":"10.1016/j.cageo.2025.106037","url":null,"abstract":"<div><div>The distribution of altered minerals is a key indicator for finding strategic minerals such as uranium, cobalt, nickel, copper and zinc. In recent years, deep learning has shown outstanding advantages in the field of hyperspectral altered mineral mapping. However, constructing a large volume of high-quality training samples remains time-consuming and labor-intensive. Moreover, many models suffer from limited generalization capability—performing well on training data but exhibiting significant performance degradation on test datasets or in real-world applications. Therefore, a semi-automatic sample construction method was proposed. The sample construction involves three steps. Firstly, using mixed pixel decomposition to extract mineral abundance, then screening samples via mixed matching, and finally enhancing classification accuracy with spectral characteristic quantification. Experimental results show that the test accuracy of the dataset generated by the semi-automated method on the ViT model reached 92.81 %, which is close to that of manually labeled samples at 93.29 %. In terms of models, an improved Vision Transformer (ViT) model was proposed. The SpecPool-Transformer model (SPT) integrates the Grouped Spectral Embedding Module (GSE) and the Convolution-Pooling Module (CPM) to enhance the extraction of adjacent band features from the spectral curves. Additionally, the model's application to cross-source data was achieved through transfer learning. On the SASI dataset of the Baiyanghe uranium deposit, the overall accuracy (OA) and average accuracy (AA) of SpecPool-Transformer reached 96.76 % and 95.14 %, respectively, representing improvements of 3.95 % and 6.11 % over the original ViT model. In the generalization test, the proposed method achieved an OA of 86.10 % and an AA of 83.74 % on the SASI aerial dataset No.1007, outperforming the second-best model, LightGBM, by 20.22 % and 31.15 %, respectively. Field validation results further confirm the high reliability of the proposed model in large-scale alteration mineral mapping across data sources, making it suitable for rapid and extensive alteration mineral mapping applications.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106037"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865464","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":"Simulating major element diffusion in garnet using realistic 3D geometries","authors":"Hugo Dominguez ,&nbsp;Nathan Mäder ,&nbsp;Pierre Lanari","doi":"10.1016/j.cageo.2025.106023","DOIUrl":"10.1016/j.cageo.2025.106023","url":null,"abstract":"<div><div>Chemical diffusion of major elements in garnet is a common phenomenon in amphibolite to granulite facies metamorphic rocks. The study of this process has led to important constraints on the rate and timescale of metamorphism, for instance using geospeedometry and forward thermodynamic modelling. However, to date, most models have assumed spherical coordinates and simple geometries when modelling diffusion in garnet. In this study, we present a framework for running 3D multicomponent diffusion models from real grain geometries obtained by micro-computed tomography. We introduce an open-source code, DiffusionGarnet.jl, written for high performance in the Julia programming language. We demonstrate the high efficiency of the numerical solver, a stabilised explicit method, and its scalability using GPU acceleration. This approach is applied to two garnet grains with different characteristics, a euhedral well-shaped grain and a deformed sub-euhedral grain with a high connectivity to the matrix from core to rim. Starting from a similar initial composition and at constant conditions of 700 °C and 0.8 GPa for 10 Myr, the models show results with very different characteristics. The euhedral grain shows results similar to those predicted with a spherical assumption, largely preserving its original zoning. In contrast, the sub-euhedral grain shows significant re-equilibration, nearly erasing completely its initial zoning. This behaviour is caused by the high connectivity with the matrix. In addition to providing a robust solver for 3D diffusion modelling, these results demonstrate the role of grain geometry and matrix connectivity on intra-grain diffusion and highlight the power of 3D approaches to properly study the complexity of natural grains.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106023"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829937","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":"2026-01-01","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":"Seismic random noise attenuation using structure-oriented 3D curvelet transform","authors":"Minggui Liang ,&nbsp;Shaohuan Zu ,&nbsp;Zifei Li ,&nbsp;Wenlu Liu ,&nbsp;Haojun Chen ,&nbsp;Zhengyu Tan","doi":"10.1016/j.cageo.2025.106020","DOIUrl":"10.1016/j.cageo.2025.106020","url":null,"abstract":"<div><div>Sparse constraints based on curvelet transform have been widely applied in seismic noise suppression. Traditional schemes usually adopt global or multi-scale thresholding to constrain the coefficients corresponding to noise, which may ignore the structural characteristics and result in signal distortion. It is usually challenging to balance noise suppression and signal preservation. To address this problem, we develop a structure-oriented 3D denoising method based on the 3D curvelet transform, which uses dip information to analyze the complexity of local features. The non-stationary thresholding scheme based on local complexity is implemented, providing strong constraints for low-complexity data to suppress noise and weak constraints for high-complexity data to protect signal. Furthermore, the coarse scale coefficients are insensitive to noise interference; only the fine-scales coefficients are constrained in our proposed scheme. Numerical tests on synthetic and field data demonstrate that the proposed method has better denoising performance for large dip feature data than the conventional global and multi-scale thresholding schemes.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106020"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860331","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":"2026-01-01","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}