Pub Date : 2025-08-22DOI: 10.1016/j.cageo.2025.106039
Lixin Zhang , Ziang Li , Zhijun Dai , Hongmin Liu
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 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.
{"title":"EffiSeisM: An efficient multi-task deep learning model for earthquake monitoring","authors":"Lixin Zhang , Ziang Li , Zhijun Dai , Hongmin Liu","doi":"10.1016/j.cageo.2025.106039","DOIUrl":"10.1016/j.cageo.2025.106039","url":null,"abstract":"<div><div>Earthquake monitoring is essential for providing timely warnings, mitigating disaster impacts, advancing scientific research, and guiding urban planning. Precise seismic waveform analysis enables accurate event detection, magnitude estimation, and a deeper understanding of earthquake mechanisms. In this paper, we propose EffiSeisM, an efficient multi-task deep learning model that combines a state space model with a convolutional architecture for earthquake detection, phase picking, and magnitude estimation. EffiSeisM designed a novel Seismic Scale Conv Module and a Conv-SSM encoder, which effectively capture key seismic features while reducing computational complexity. This design ensures high accuracy and operational efficiency, enabling effective seismic analysis. We evaluate EffiSeisM on the DiTing Dataset and DiTing Dataset 2.0, comprising 3 million seismic samples from China and surrounding regions, and compare its performance with several baseline models. The results show that EffiSeisM consistently outperforms the baselines, achieving F1 scores of 0.98 for earthquake detection, 0.92 for phase-P picking, 0.84 for phase-S picking, and an R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> of 0.92 for magnitude estimation. Additionally, EffiSeisM demonstrates significant improvements in inference speed and accuracy, highlighting its potential as a scalable and efficient solution for large-scale seismic data analysis.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106039"},"PeriodicalIF":4.4,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-19DOI: 10.1016/j.cageo.2025.106024
Yufeng Jiang, Zining Yu, Haiyong Zheng
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.
{"title":"Short-term earthquake forecasting using electromagnetic and geoacoustic observations via contrastive learning","authors":"Yufeng Jiang, Zining Yu, Haiyong Zheng","doi":"10.1016/j.cageo.2025.106024","DOIUrl":"10.1016/j.cageo.2025.106024","url":null,"abstract":"<div><div>Different observations provide earthquake-related information from various perspectives, and effectively leveraging them is essential for enhancing forecasting. Existing data-driven methods primarily rely on concatenation, directly aligning features (e.g., the absolute mean) extracted from different observations side by side. However, this naive approach ignores that each observation reflects a different aspect of the same physical process and inadequately explores cross-observation interactions. To address these issues, we propose CL4EF, a contrastive learning framework that leverages electromagnetic and geoacoustic observations for earthquake forecasting. Specifically, we introduce the synchronous response consistency hypothesis, assuming that different observations within the same time window should respond consistently to the same physical process. Following this hypothesis, we design a contrastive loss that attracts observation pairs from the same station and time window (positives) and repulses others (negatives), enabling cross-observation interaction modeling for the downstream forecasting task. Experimental results demonstrate that CL4EF achieves state-of-the-art performance, improving AUC by 22%. The spatial distribution of forecast probabilities reveals alignment with active fault zones, suggesting the model’s capacity to extract meaningful information for earthquake forecasting. As a result, this study contributes a scalable approach for integrating heterogeneous observations in geosciences and offers new insights into short-term earthquake forecasting.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106024"},"PeriodicalIF":4.4,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-18DOI: 10.1016/j.cageo.2025.106019
Jinxuan Wang , Kejia Pan , Hongzhu Cai , Zhengguang Liu , Xu Han , Weiwei Ling
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.
{"title":"Large-scale 3-D magnetotelluric modeling in anisotropic media using extrapolation multigrid method on staggered grids","authors":"Jinxuan Wang , Kejia Pan , Hongzhu Cai , Zhengguang Liu , Xu Han , Weiwei Ling","doi":"10.1016/j.cageo.2025.106019","DOIUrl":"10.1016/j.cageo.2025.106019","url":null,"abstract":"<div><div>To improve the practicality and efficiency of 3D magnetotelluric (MT) data inversion, developing a 3D MT forward modeling algorithm with low computational cost in terms of time and memory is an important prerequisite. An extrapolation cascadic multigrid (EXCMG) method is developed on rectilinear grids to accelerate the solving process of large linear systems arising from the staggered-grid finite difference (SFD) discretization of Maxwell’s equations. Arbitrary anisotropic conductivity is considered, without adding extra unknowns to the SFD scheme. A new prolongation operator based on global extrapolation and mixed-order interpolation is developed to tackle the issue caused by non-nested unknown distribution. The divergence correction scheme for arbitrary anisotropy is employed to stabilize the smoothing process, especially for low-frequency cases. Several examples are tested to validate the accuracy and efficiency of the proposed algorithm, including synthetic models with anisotropy and topography, and the real-world Cascadia model. Results show that our EXCMG solver is more efficient than traditional iterative solvers (e.g., the preconditioned BiCGStab), the algebraic multigrid method and the geometric multigrid (GMG) method. The proposed method can efficiently solve large-scale problems with large grid stretching factors and arbitrary anisotropy, providing powerful engine for large-scale MT inversion.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106019"},"PeriodicalIF":4.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-14DOI: 10.1016/j.cageo.2025.106028
Yu-Mei Wang , Qiong Xu , Ziyu Qin , Shulin Pan , Fan Min
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.
{"title":"Exploiting global information and local edge detail for full waveform inversion","authors":"Yu-Mei Wang , Qiong Xu , Ziyu Qin , Shulin Pan , Fan Min","doi":"10.1016/j.cageo.2025.106028","DOIUrl":"10.1016/j.cageo.2025.106028","url":null,"abstract":"<div><div>Data-driven deep learning full waveform direct inversion (DL-FWI) has emerged as an advanced technique for predicting subsurface structures. Popular approaches frequently encounter blurry edge pixels and inaccurate velocity values. Here, we propose an algorithm called TU-Net that captures both global information and local edge detail to address these issues. With respect to the network design, we incorporate a texture warping module (TWM) into the skip connections of the U-Net backbone. Due to the multi-scale feature extraction ability of TWM, our network is able to learn details in complex regions. With respect to the loss function design, we introduce the mixed pixel and edge (MPE) loss, which is a combination of the mean absolute error, the mean square error, and the edge-based losses. The newly proposed loss function balances the model’s focus on global pixel features with the local edge characterization, driving the network to produce high-quality edges. We apply the proposed approach on publicly available OpenFWI, SEG salt and Marmousi II datasets. Quantitative results demonstrate that TU-Net achieves better performance in terms of MSE, MAE, LPIPS, PSNR, UIQ, and SSIM than four state-of-the-art deep networks. The source code is available at github.com/fansmale/TU-Net.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"206 ","pages":"Article 106028"},"PeriodicalIF":4.4,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-14DOI: 10.1016/j.cageo.2025.106026
Zhuofan Liu , Goodluck Archibong , Umair Bin Waheed , Sifan Wang , Chao Song
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.
{"title":"Physics-Informed Fourier-DeepONet for a generalized eikonal solution","authors":"Zhuofan Liu , Goodluck Archibong , Umair Bin Waheed , Sifan Wang , 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}
Pub Date : 2025-08-09DOI: 10.1016/j.cageo.2025.106037
Xinhang Feng , Jiejun Huang , Ximing Chen , Han Zhou , Ming Zhang , Chuan Zhang , Fawang Ye
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.
{"title":"Research on hyperspectral remote sensing alteration mineral mapping using an improved ViT model","authors":"Xinhang Feng , Jiejun Huang , Ximing Chen , Han Zhou , Ming Zhang , Chuan Zhang , 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}
Pub Date : 2025-08-09DOI: 10.1016/j.cageo.2025.106020
Minggui Liang , Shaohuan Zu , Zifei Li , Wenlu Liu , Haojun Chen , Zhengyu Tan
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.
{"title":"Seismic random noise attenuation using structure-oriented 3D curvelet transform","authors":"Minggui Liang , Shaohuan Zu , Zifei Li , Wenlu Liu , Haojun Chen , 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}
Pub Date : 2025-08-08DOI: 10.1016/j.cageo.2025.106023
Hugo Dominguez , Nathan Mäder , Pierre Lanari
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.
{"title":"Simulating major element diffusion in garnet using realistic 3D geometries","authors":"Hugo Dominguez , Nathan Mäder , 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}
Pub Date : 2025-08-05DOI: 10.1016/j.cageo.2025.106025
Guanxiang Ding , Pu Li , Xiaowu Luo , Qinghua Zhou , Hao Zhu , Qiang Zhang , Yanmin Liu
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.
{"title":"Semi-analytical method for thermal field analysis of multiple arbitrarily shaped inhomogeneities in heterogeneous geological media","authors":"Guanxiang Ding , Pu Li , Xiaowu Luo , Qinghua Zhou , Hao Zhu , Qiang Zhang , 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}
Pub Date : 2025-08-05DOI: 10.1016/j.cageo.2025.106021
Kirsten L. Siebach, Eleanor L. Moreland, Gelu Costin, Yueyang Jiang
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.
{"title":"MIST: An online tool automating mineral identification by stoichiometry","authors":"Kirsten L. Siebach, Eleanor L. Moreland, Gelu Costin, 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}
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