Computers & Geosciences最新文献

{"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":"2025-10-09","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}

{"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":"2025-10-04","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":"Distance Transform Loss: Boundary-aware segmentation of seismic data","authors":"Rafael Henrique Vareto ,&nbsp;Ricardo Szczerbacki ,&nbsp;Luiz A. Lima ,&nbsp;Pedro O.S. Vaz-de-Melo ,&nbsp;William Robson Schwartz","doi":"10.1016/j.cageo.2025.106061","DOIUrl":"10.1016/j.cageo.2025.106061","url":null,"abstract":"<div><div>The segmentation of seismic data is a challenging exercise given the complexity and high variability of subsurface sources. This arduous task is effective in the identification of geological features, including facies classification, fault detection, and horizon interpretation. As a result, this work introduces a new cost function entitled Distance Transform Loss (DTL) that punishes deep networks when class boundaries are misclassified in exchange for more accurate contour delineations, an important aspect in the geological field. DTL consists of four key steps: contour detection, distance transform mapping, pixel-wise multiplication, and the summation of all grid elements. We conduct a comprehensive evaluation of deep convolutional architectures using publicly available seismic datasets, demonstrating that the proposed approach consistently enhances semantic segmentation performance. The results highlight DTL as a robust and architecture-agnostic loss function, capable of addressing class imbalance and boundary delineation challenges that commonly arise in seismic interpretation tasks.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106061"},"PeriodicalIF":4.4,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269660","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":"Weakly supervised semantic segmentation of microscopic carbonates on marginal devices","authors":"Keran Li ,&nbsp;Yujie Gao ,&nbsp;Yingjie Ma ,&nbsp;Chengkun Li ,&nbsp;Junjie Ye ,&nbsp;Hao Yu ,&nbsp;Yiming Xu ,&nbsp;Dongyu Zheng ,&nbsp;Ardiansyah Koeshidayatullah","doi":"10.1016/j.cageo.2025.106059","DOIUrl":"10.1016/j.cageo.2025.106059","url":null,"abstract":"<div><div>Microscopic analysis is the cornerstone to uncover petrological and mineralogical characteristics of carbonate rocks. In addition, such information is critical for precise identification of carbonate microfacies and diagenetic evolution. This type of information is important, but relies too much on manual experience, which is time-consuming and laborious. Recently, several successful deep learning models showed great potential in the identification process. However, current deep learning models have typically complex model architectures greatly hinder the deployment-inference in practical and lightweight environments. To overcome the difficulty of deep learning models in reasoning in actual edge scenes, a three-stage segmentation method by weakly supervised learning was proposed. The approach embeds class activation mapping (CAM), grey level co-occurrence matrix (GLCM), and knowledge distillation (KD) modules to achieve attention transfer to the lightweight network (CamNet). Furthermore, based on the performance of the model algorithm and application requirements, a lightweight carbonate thin section image-assistant recognition system has been developed. Through ingenious control flow design, this system achieves an effective balance between runtime latency and resource consumption, demonstrating superior performance metrics. Experimental results indicate that CamNet’s total parameter count is only 800k. When deployed in embedded systems, CamNet achieves an inference speed of 6.87 fps. Our successful development verifies the efficiency and practicality in marginal devices.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106059"},"PeriodicalIF":4.4,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269657","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":"GeoFedNet: Federated learning for privacy-aware, robust, and generalizable seismic interpretation","authors":"Muhammad Saif ul Islam,&nbsp;Aamir Wali","doi":"10.1016/j.cageo.2025.106060","DOIUrl":"10.1016/j.cageo.2025.106060","url":null,"abstract":"<div><div>Seismic structural interpretation is crucial for understanding subsurface geology, particularly in hydrocarbon exploration, as it aids in identifying reservoir formations, assessing drilling risks, and optimizing resource extraction. However, developing a widely generalizable model for seismic interpretation remains challenging due to the limited availability of large-scale public datasets, variations in seismic surveys, and privacy constraints that hinder data sharing. These factors lead to inconsistencies in model performance across diverse datasets, limiting the applicability of existing approaches. To address this gap, we propose a federated learning-based framework for seismic interpretation, enabling distributed model training without requiring direct data sharing. In this approach, local models are trained independently across different clients, and a global model is aggregated to improve generalization across heterogeneous datasets. This method not only preserves data confidentiality but also mitigates challenges related to labeled data scarcity and class imbalance, allowing clients with limited data to benefit from collaborative learning. We evaluate GeoFedNet on three key seismic interpretation tasks: seismic structure classification, salt detection, and facies segmentation. Across all tasks, GeoFedNet achieves performance within 1%–3% of centralized models while significantly outperforming isolated local models by up to 15% in accuracy and generalization. These results demonstrate that our framework can effectively learn from non-IID and imbalanced data without compromising performance. GeoFedNet also shows improved robustness to client variability and better minority class recognition, which are critical in real-world subsurface interpretation scenarios. These findings highlight the potential of federated learning in enabling hydrocarbon companies to collaboratively train robust seismic interpretation models while maintaining data privacy, ultimately improving exploration efficiency and informed decision-making.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106060"},"PeriodicalIF":4.4,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222456","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":"Neural network-based framework for signal separation in spatio-temporal gravity data","authors":"Betty Heller-Kaikov,&nbsp;Roland Pail,&nbsp;Martin Werner","doi":"10.1016/j.cageo.2025.106057","DOIUrl":"10.1016/j.cageo.2025.106057","url":null,"abstract":"<div><div>Global, temporal gravity data such as those provided by the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow on (GRACE-FO) satellite missions contain signals from many mass redistribution processes on Earth. These include hydrological, atmospheric, oceanic, cryospheric and solid Earth-related processes. As the measured gravity changes represent the sum of all signals, an optimal exploitation of these data for scientific applications requires strategies for separating the individual contained signals. We provide a neural network algorithm using a multi-channel U-Net architecture that translates the sum of several signals to the individual contained components based on their typical space–time patterns. The software contains strategies for transforming spatio-temporal gravity data depending on latitude, longitude, and time to 2-D “image” training samples. The software also includes implementations of strategies for introducing additional knowledge about the physical behavior of the individual signals as constraints to the training. In a closed-loop simulation example, simulated gravity signals induced by processes in the atmosphere and oceans, hydrosphere, cryosphere and solid Earth are successfully separated at relative RMS prediction errors between 19 and 67%. This shows that neural network-based methods can help solving geodetic tasks if the considered data is transformed into a suitable data format. To apply the framework to real observational data, we suggest training the network on representative, physical forward-modeled signals and subsequently applying the trained network to real data. The latter will additionally require external validation strategies. The software is freely available on GitHub under <span><span>https://github.com/Betty-Heller/neural-gravity</span><svg><path></path></svg></span> and is, in general, also applicable for signal separation in any other dataset depending on three variables.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106057"},"PeriodicalIF":4.4,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160178","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":"A new multi-expert distance for clustering climate parameters: a Caribbean precipitation case study","authors":"Emmanuel Biabiany ,&nbsp;Ruben Bagghi ,&nbsp;Didier C. Bernard ,&nbsp;Vincent Pagé ,&nbsp;Stéphane Cholet ,&nbsp;Raphaël Cécé","doi":"10.1016/j.cageo.2025.106058","DOIUrl":"10.1016/j.cageo.2025.106058","url":null,"abstract":"<div><div>This study investigates precipitation patterns in the Caribbean region using a novel Multi-Expert Distance (MED) metric for clustering analysis. MED integrates multiple climate parameters, including Sea Surface Temperature (SST), wind components at 925 hPa, and Outgoing Longwave Radiation (OLR), with the objective of enhancing spatiotemporal precipitation analysis. This approach offers an alternative to conventional methods that rely on single datasets and Euclidean distances. It combines physical parameters during clustering to enhance accuracy and insights. The analysis encompasses a 43-year period (1979–2021), extending from the Gulf of Mexico to the Caribbean, with a spatial extent that covers the entire region. The MED metric incorporates zone-specific histograms and Kullback-Leibler divergence, enabling dynamic comparisons of atmospheric configurations. The analysis yielded six distinct clusters, each exhibiting unique seasonal and inter-annual precipitation patterns, influenced by regional atmospheric dynamics. The analysis revealed significant transitions and associations between clusters, precipitation levels, and atmospheric conditions. Clusters representing dry conditions exhibited negative SST anomalies, reflecting reduced moisture production. Conversely, clusters exhibiting high precipitation exhibited positive SST anomalies, which are conducive to moisture accumulation. Furthermore, tropical storms and hurricanes were predominantly observed in wetter clusters, underscoring the utility of MED in linking atmospheric phenomena with climatic impacts. The results highlight the effectiveness of the MED in improving both the accuracy and interpretability of clustering algorithms. Beyond its methodological contributions, this work highlights the MED's potential to advance the understanding and forecasting of precipitation regimes, thereby contributing to more robust climate analyses. Such insights are particularly relevant for informing climate adaptation strategies in vulnerable regions, notably the Caribbean. Future research could investigate automated domain segmentation as a means of further refining and optimizing this approach.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106058"},"PeriodicalIF":4.4,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109370","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":"Stratya2D: Enhancing kinematic backstripping through image-based 2D horizon integration","authors":"Harikrishnan Nalinakumar ,&nbsp;Patrick Makuluni ,&nbsp;Juerg Hauser ,&nbsp;Stuart R. Clark","doi":"10.1016/j.cageo.2025.106056","DOIUrl":"10.1016/j.cageo.2025.106056","url":null,"abstract":"<div><div>The study of sedimentary basins is crucial for understanding Earth’s evolution and geological history. Traditional basin analysis, often constrained by 1D subsidence analysis, limits the spatial understanding of geological processes. This study introduces Stratya2D, a Python-based tool that extends traditional methodologies by extending 1D decompaction and backstripping to a 2D framework allowing for detailed basin analysis. The tool extracts horizon annotations from pre-interpreted seismic images, enabling coordinate-based reconstruction of depositional surfaces. Using advanced image processing techniques, Stratya2D integrates horizon extraction, depth normalisation, and Monte Carlo Simulation (MCS) to quantify uncertainties in tectonic subsidence and layer evolution at each time step, offering a breakthrough in geoscientific analysis. This innovative approach offers a more cost-effective alternative to traditional software and improves prediction reliability. The tool’s effectiveness was validated through comparisons with established literature and specific case studies, including data from the NDI Carrara 1 well in the South Nicholson region, Northern Territory, Australia, along the 17GA-SN1 seismic line. The results closely align with previously published data and PetroMod simulations, accurately replicating the tectonic subsidence curve and offering extended insights into the complex geological context of the South Nicholson Region. Comparative analysis with PetroMod confirms the robustness of Stratya2D, while the inclusion of MCS highlights the critical role of uncertainty quantification in subsurface modelling. Stratya2D offers a robust and versatile tool for regional-scale basin modelling, effectively addressing diverse geoscientific challenges.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106056"},"PeriodicalIF":4.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160177","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":"EFKAN: A KAN-integrated neural operator for efficient magnetotelluric forward modeling","authors":"Feng Wang ,&nbsp;Hong Qiu ,&nbsp;Yingying Huang ,&nbsp;Xiaozhe Gu ,&nbsp;Renfang Wang ,&nbsp;Bo Yang","doi":"10.1016/j.cageo.2025.106052","DOIUrl":"10.1016/j.cageo.2025.106052","url":null,"abstract":"<div><div>Forward modeling is the cornerstone of magnetotelluric (MT) inversion. Neural operators have been successfully applied to solve partial differential equations, demonstrating encouraging performance in rapid MT forward modeling. In particular, they can obtain the electromagnetic field at arbitrary locations and frequencies, which is meaningful for MT forward modeling. In conventional neural operators, the projection layers have been dominated by classical multi-layer perceptrons, which may reduce the precision of solution because they usually suffer from the disadvantages of multi-layer perceptrons, such as lack of interpretability, overfitting, etc. Therefore, to improve the accuracy of the MT forward modeling with neural operators, we integrate the Fourier neural operator with the Kolmogorov–Arnold network (KAN). Specifically, we adopt KAN as the trunk network instead of the classic multi-layer perceptrons to project the resistivity and phase, determined by the branch network-Fourier neural operator, to the desired locations and frequencies. Experimental results demonstrate that the proposed method can achieve high precision in obtaining apparent resistivity and phase at arbitrary frequencies and/or locations with rapid computational speed.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106052"},"PeriodicalIF":4.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098950","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":"Assessment of automated stratigraphic interpretations of boreholes with geology-informed metrics","authors":"Sebastián Garzón ,&nbsp;Willem Dabekaussen ,&nbsp;Freek S. Busschers ,&nbsp;Eva De Boever ,&nbsp;Siamak Mehrkanoon ,&nbsp;Derek Karssenberg","doi":"10.1016/j.cageo.2025.106043","DOIUrl":"10.1016/j.cageo.2025.106043","url":null,"abstract":"<div><div>Stratigraphic interpretation of borehole data is a fundamental aspect of subsurface geological models, providing critical insights into the distribution of stratigraphic units. However, expert interpretation of all available borehole data is impractical for large-scale regional mapping involving thousands of boreholes. Automated interpretations using machine learning models can significantly increase the number of boreholes included in subsurface geological models. Nevertheless, these predictions must adhere to strict spatial and stratigraphic relationships (e.g. superposition) to ensure geological plausibility, which often requires post-processing tasks. Traditional evaluation metrics commonly used for general-domain classification tasks (e.g. accuracy, F1-score) do not necessarily reflect the geological plausibility of predictions, as they fail to account for the sequential nature and spatial relationships inherent in borehole interpretation. To address this limitation, we propose and evaluate a set of geology-informed metrics that focus on three key aspects of stratigraphic interpretation, namely the expected geographical extent of units (extent metrics), their sequential relationships (sequence metrics), and their vertical positioning along boreholes (position metrics). Using a dataset of 1394 boreholes from the Cenozoic Roer Valley Graben (southeast Netherlands), which covers <span><math><mo>∼</mo></math></span>3000 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> and includes 15 lithostratigraphic units, we demonstrate that Random Forest and Neural Network models with similar performance on traditional metrics (e.g. accuracy, Cohen’s kappa, and F1-score) can differ significantly in their ability to produce geologically plausible predictions. For example, while many model configurations achieve <span><math><mo>∼</mo></math></span>75%–80% agreement between expected and predicted classes, the Neural Network models better capture the sequential stratigraphic relationships expected in the study area. Our results underscore the need for domain-specific metrics that offer a more accurate and interpretable assessment of model performance.</div></div>","PeriodicalId":55221,"journal":{"name":"Computers & Geosciences","volume":"207 ","pages":"Article 106043"},"PeriodicalIF":4.4,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145098952","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}