Computers & Geosciences最新文献

{"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":"2025-08-14","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":"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":"2025-08-09","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":"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":"2025-08-09","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":"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":"2025-08-08","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":"Semi-analytical method for thermal field analysis of multiple arbitrarily shaped inhomogeneities in heterogeneous geological media","authors":"Guanxiang Ding ,&nbsp;Pu Li ,&nbsp;Xiaowu Luo ,&nbsp;Qinghua Zhou ,&nbsp;Hao Zhu ,&nbsp;Qiang Zhang ,&nbsp;Yanmin Liu","doi":"10.1016/j.cageo.2025.106025","DOIUrl":"10.1016/j.cageo.2025.106025","url":null,"abstract":"<div><div>Natural geological formations typically exhibit heterogeneous thermal properties due to the presence of multiple inhomogeneities, such as mineral inclusions, fractures, or pore clusters, which significantly influence subsurface heat transport. In this work, an effective semi-analytical approach is proposed to investigate the heterogeneous thermal field containing multiple inhomogeneities with arbitrary shapes and various conductivities. Temperature solutions for rectangular elements are constructed from integrated line element temperatures, from which temperature gradients and heat flux are analytically derived. The work features a unified formulation for both the interior and exterior thermal responses of inhomogeneities, avoiding separate treatment of field regions. By Combing the Numerical Equivalent Inclusion Method (NEIM) with two-dimensional Fast Fourier Transform (2D-FFT) algorithms, the proposed approach efficiently solves thermal fields involving both stiff and soft inhomogeneities in heterogeneous media. Furthermore, the method is applied to geostructures, analyzing the thermal distributions of multiple arbitrarily shaped inhomogeneities subjected to remote heat flux. The semi-analytical method demonstrates high accuracy, computational efficiency, and robustness, providing a valuable tool for geoscientific thermal studies.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106025"},"PeriodicalIF":4.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144781276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"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":"2025-08-05","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":"Super-resolution of 3D Micro-CT images using generative adversarial Networks: Enhancing resolution and segmentation accuracy","authors":"Evgeny Ugolkov ,&nbsp;Xupeng He ,&nbsp;Hyung Kwak ,&nbsp;Hussein Hoteit","doi":"10.1016/j.cageo.2025.106018","DOIUrl":"10.1016/j.cageo.2025.106018","url":null,"abstract":"<div><div>We develop a procedure for substantially improving the quality of segmented 3D micro-Computed Tomography (micro-CT) images of rocks with a Machine Learning (ML) Generative Model. The proposed model enhances the resolution eightfold (8x) and addresses segmentation inaccuracies due to the overlapping X-ray attenuation in micro-CT measurement for different rock minerals and phases. The proposed generative model is a 3D Deep Convolutional Wasserstein Generative Adversarial Network with Gradient Penalty (3D DC WGAN-GP). The algorithm is trained on segmented 3D low-resolution micro-CT images and segmented unpaired complementary 2D high-resolution Laser Scanning Microscope (LSM) images. The algorithm was demonstrated on multiple samples of Berea sandstones. We achieved high-quality super-resolved 3D images with a resolution of 0.44 μm/voxel and accurate segmentation for constituting minerals and pore space. The proposed procedure can significantly expand the modern capabilities of digital rock physics.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106018"},"PeriodicalIF":4.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763906","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":"An improved method for pore size distribution measurement of porous geomaterials based on microscopic images","authors":"Shijia Ma,&nbsp;Jiangfeng Liu,&nbsp;Zhipeng Wang,&nbsp;Ruinian Sun,&nbsp;Xinyue Zhang,&nbsp;Hongyang Ni","doi":"10.1016/j.cageo.2025.106017","DOIUrl":"10.1016/j.cageo.2025.106017","url":null,"abstract":"<div><div>Pore size distribution (PSD) is vital for characterizing microscopic information and fluid transport in geomaterials, but traditional methods struggle with irregular pore shapes and digital imaging errors, often leading to inaccurate results. This study presents an improved morphological transformation-based algorithm that iteratively fills voids with maximal circles or spheres and introduces an optimized scheme for small-pore representation, significantly reducing measurement errors. Validation on eight 2D scanning electron microscope and six 3D computer tomography images shows the proposed method achieves up to 67 % lower relative error for small pore sizes and produces permeability predictions with a mean deviation within 3 % of experimental values, outperforming established techniques. Statistical analysis confirms that, for most samples, predicted permeability values fall within or approaching the 95 % confidence interval of measured data, demonstrating robust consistency across imaging sources and magnifications. Furthermore, the quantitative evaluation of pore geometry and PSD curves using different methods reveals that complex and randomly distributed pore geometries strongly influence PSD curve morphology, underscoring the importance of geometric characterization. These advancements enable more reliable and repeatable pore structure quantification, offering practical value for geoscience and engineering applications.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106017"},"PeriodicalIF":4.4,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DisperPy: A machine learning based tool to automatically pick group velocity dispersion curves from earthquakes","authors":"André V.S. Nascimento ,&nbsp;Carlos A.M. Chaves ,&nbsp;Susanne T.R. Maciel ,&nbsp;George S. França ,&nbsp;Giuliano S. Marotta","doi":"10.1016/j.cageo.2025.106015","DOIUrl":"10.1016/j.cageo.2025.106015","url":null,"abstract":"<div><div>Seismology has made significant progress in high-resolution Earth imaging, largely driven by the increasing volume of freely available data. As a result, automated tools and machine learning algorithms are becoming essential for processing this vast amount of information. We present <em>DisperPy</em>, an open-source Python library developed to automatically extract group velocity dispersion curves from earthquake data. The analysis framework of <em>DisperPy</em> is structured around two primary tasks: (1) assessing the quality of waveforms to determine if dispersion extraction is feasible, and (2) measuring the group velocity dispersion curve for suitable waveforms. To address the first task, <em>DisperPy</em> uses a convolutional neural network trained on dispersion spectrograms to classify waveform quality. The model, based on the ResNet-34 architecture, is initialized with ImageNet-pretrained weights and fine-tuned using the fastai deep learning library. In the test set, the network achieves an accuracy of 92 % in distinguishing between high- and low-quality dispersion images. For the second task, <em>DisperPy</em> employs unsupervised learning techniques, starting with a Gaussian mixture model to separate dispersion energy from background noise, followed by <em>k-means</em> to separate the dispersion energy into clusters, making it easier to track amplitude maxima and then construct initial dispersion curves. Finally, a refinement of the initial dispersion is achieved using both the density-based spatial clustering of applications with noise algorithm and data quality criteria to remove possible outliers. To further test <em>DisperPy</em>, we conduct a surface wave tomography experiment across the contiguous United States using freely available vertical-component broadband waveforms. After processing the data with <em>DisperPy</em> and removing low-quality waveforms, the final dataset consisted of 194,325 unique dispersion curves. Consistent with previous studies, our maps reveal a prominent velocity dichotomy, with low velocities in the tectonically active western US and high velocities in the stable central and eastern US.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106015"},"PeriodicalIF":4.2,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Adaptive Preconditioned Conjugate Gradient Regularization (APCGR) algorithm with Sigmoid Function (SF) constraint for efficient three-dimensional (3D) gravity focusing inversion","authors":"Wenjin Chen,&nbsp;Xiaolong Tan","doi":"10.1016/j.cageo.2025.106014","DOIUrl":"10.1016/j.cageo.2025.106014","url":null,"abstract":"<div><div>We introduce a novel focused gravity inversion algorithm and develop corresponding software, highlighting three key innovations. First, we propose the Adaptive Preconditioned Conjugate Gradient Regularization algorithm, which efficiently and adaptively determines the regularization parameter. Second, we incorporate the Sigmoid Function to stabilize the inversion process, significantly accelerating iterative convergence. Third, we have developed a user-friendly software with a graphical user interface for this new method, utilizing the popular high-level and interactive programming language MATLAB. To promote knowledge sharing and resource accessibility, we have made the software open-source. To validate our approach, we tested the algorithm on both synthetic and real gravity data, demonstrating its exceptional capability to accurately reconstruct the 3D density distribution of complex subsurface structures. Furthermore, we conducted a comparative analysis between the new algorithm, the conjugate gradient method constrained by SF, and the standard conjugate gradient method. The results indicate that the new method requires fewer iterations and exhibits higher computational efficiency.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"205 ","pages":"Article 106014"},"PeriodicalIF":4.2,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654450","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}