Applied Computing and Geosciences最新文献

{"title":"MapEX: A tool for X-ray map analysis","authors":"Divyadeep Harbola,&nbsp;George Mathew","doi":"10.1016/j.acags.2025.100300","DOIUrl":"10.1016/j.acags.2025.100300","url":null,"abstract":"<div><div>MapEX is an open-source Python toolkit for analysing multi-channel X-ray maps acquired through diverse analytical platforms, including electron probe microanalysis with wavelength-dispersive spectroscopy (EPMA-WDS), scanning and transmission electron microscopy with energy-dispersive spectroscopy (SEM/TEM-EDS), micro-X-ray fluorescence (μ-XRF), and synchrotron-based mapping techniques. The software reads native CSV or text file exports, records key acquisition metadata, and packages data in a HDF5 structure that supports fast access and fully reproducible workflows. Using a linear fit, a calibration panel implements region-of-interest regressions from map intensity to composition. It reports the fitted equation, coefficient of determination, and root-mean-square error, with pointwise inclusion or exclusion of standards. For phase classification, principal-component features are combined with unsupervised clustering methods to classify phases directly from elemental distributions; parameters can be tuned and results updated interactively. An interactive interface links elemental maps, correlation plots, and phase classification, with linked selection so that pixels chosen in plot space are highlighted across all images and vice versa. Line-profile tools extract compositional trends along user-defined paths, enabling targeted inspection of grain boundaries, reaction fronts, and alteration rims. By emphasising open formats, explicit assumptions, and pixel-level validation, MapEX offers a rigorous and transparent alternative to proprietary software and lowers barriers to routine X-ray map analysis in petrology and materials science.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"28 ","pages":"Article 100300"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing satellite image quality with the edge-based wavelet transformer for super-resolution","authors":"Chieh Tsai ,&nbsp;Pei-Jun Lee ,&nbsp;Shimaa Bergies ,&nbsp;John Liobe ,&nbsp;Vaidotas Barzdėnas","doi":"10.1016/j.acags.2025.100302","DOIUrl":"10.1016/j.acags.2025.100302","url":null,"abstract":"<div><div>High-quality satellite imagery is critical in environmental monitoring, disaster response, and urban planning applications, where detailed and accurate images are essential for informed decision-making. However, images from small satellites often have low resolution, limiting their effectiveness in addressing precise analysis challenges. To overcome these limitations, this paper presents the Edge-Based Wavelet Transformer for Super-Resolution (EBWT-SR), an innovative technique designed to enhance satellite image resolution while optimizing computational efficiency. EBWT-SR combines Spatial-Wavelet Multi-Head Attention Mechanisms and a Multi-Modal Convolutional Shallow Feature Extractor within a Convolutional Transformer architecture, allowing for the refinement of object contours and textures. By incorporating edge-based wavelet transform convolutional layers and a specialized multi-modal loss function for fine-tuning, the developed EBWT-SR improves local feature representation without increasing computational complexity. The new model can improve the results by approximately 0.67 in Peak Signal-to-Noise Ratio (PSNR) and 0.63 in Perceptually <strong>Uniform Peak Signal-to-Noise Ratio</strong> (puPSNR) metrics, along with a 7.7 % reduction in Giga Floating-Point Operations Per Second (GFLOPS) compared to recent methods on the fine-grained satellite image dataset focused on ship classification and super-resolution tasks (FGCSR-42) dataset. highlighting its ability to enhance satellite image quality while significantly maintaining computational efficiency.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"28 ","pages":"Article 100302"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyper-spectral Unmixing algorithms for remote compositional surface mapping: a review of the state of the art","authors":"Alfredo Gimenez Zapiola,&nbsp;Andrea Boselli,&nbsp;Alessandra Menafoglio,&nbsp;Simone Vantini","doi":"10.1016/j.acags.2025.100297","DOIUrl":"10.1016/j.acags.2025.100297","url":null,"abstract":"<div><div>This work concerns a detailed review of data analysis methods used for remotely sensed images of large areas of the Earth and of other solid astronomical objects. Focus is on the problem of inferring the materials that cover the surfaces captured by hyper-spectral images and estimating their abundances and spatial distributions within the region. Different hyper-spectral unmixing methods are reported as well as compared. The most important public data-sets in this setting, which are vastly used in the testing and validation of the former, are also systematically explored. Typically, a pixel-wise constrained regression is used assuming linear mixing. Yet, more recent methodologies go beyond such assumption and are thus analysed. Data-based testing of assumptions and uncertainty quantification are found to be scarce in the literature. Open problems are spotlighted and concrete recommendations for future research are provided.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"28 ","pages":"Article 100297"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph-based evidence accumulation for clustering 3D orientation measurements in planetary surface mapping under relational constraints","authors":"Orhun Aydin","doi":"10.1016/j.acags.2025.100309","DOIUrl":"10.1016/j.acags.2025.100309","url":null,"abstract":"<div><div>Groups of structural measurements based on orientation similarity are indicative of deformation mechanisms and are important measures to infer the deformation history of planetary surfaces. Despite methods for defining groups based only on orientation or spatial proximity, a general framework for defining orientation-based groups under multiple constraints is lacking. A second challenge pertains to the computational challenge of clustering large structural data due to the large volume and velocity of the data collected as a part of field-based, earth-observing, and planetary missions. In this paper, we propose a general clustering framework for defining groups in angular data based on orientation similarity that can be constrained with relational constraints, such as spatial proximity or prior knowledge of geologic units. We represent the similarity of geologic measurements with a similarity graph where similarity links (graph edges) are defined via the clustering evidence accumulated by re-clustering of data with varying parameters. We showcase the use of a spectral gap measure to define the optimal number of clusters for the evidence graph. We apply the proposed method to define groups of compaction bands using field data collected from the Valley of Fire, NV. We compare our results to a state-of-the-art Bingham mixture model. Results indicate the realism of the proposed method in terms of mapping distinct structural groups under different spatial proximity constraints.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"28 ","pages":"Article 100309"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging generative AI and hyperspectral drill-core imaging for fully automated mineral mapping of unconventional reservoir","authors":"Izzul Qudsi ,&nbsp;Hilary Corlett ,&nbsp;Ardiansyah Koeshidayatullah","doi":"10.1016/j.acags.2025.100298","DOIUrl":"10.1016/j.acags.2025.100298","url":null,"abstract":"<div><div>The acquisition of hyperspectral imagery on core samples significantly enhances the ability of geoscientists to conduct preliminary reservoir characterization particularly for unconventional exploration. Hyperspectral analysis is particularly powerful in providing qualitative and quantitative mineral distribution maps on geological samples. Information about mineral distribution on unconventional shale cores (e.g., clay-dominated vs calcite-dominated) may aid in determining zones of sweet spots for hydraulic fracturing. Conventionally, the mapping of mineralogy in hyperspectral imagery requires considerable human involvement, ranging from the extraction of mineral endmembers, process of defining the representative reflectance spectra signature of every minerals in the hyperspectral dataset, to the selection of parameters for classification algorithms. We used a generative deep learning approach with modified Deep Embedded Clustering (DEC) and variational autoencoders (VAE) to automate mineral classification in hyperspectral imagery of Devonian unconventional reservoir cores from the Western Canada Sedimentary Basin. The cores are characterized by nodular carbonates and Si- and clay-rich shales with interbedded detrital carbonates, covering the upper portion of the Waterways Formation, the entire Majeau Lake and Duvernay formations, and a portion of the lower Ireton Formation. Our study demonstrates that the proposed generative AI approach is effective in qualitatively and quantitatively identifying key mineral endmembers, including calcite, non-clay silica-rich minerals, and clay minerals (illite and muscovite) and their mixtures. In addition, the algorithm is also able to extract and identify the spectral variation of the silica-rich minerals based on its organic content. The VAE + DEC algorithm was able to capture not only the thin interbedded layers but also its complex mineralogy that aligns well with findings from previous research on the studied formations. In addition, the spatial distribution of organic content variation is also identified correctly and matched with the laboratory measurements. Therefore, the proposed workflow emerges as a promising, less labor-intensive alternative for lithological mapping and brief rock properties analysis from hyperspectral datasets, offering potential further applications in unconventional reservoir drill core data.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"28 ","pages":"Article 100298"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing key controlling factors of shale reservoir heterogeneity in \"thin\" stratigraphic settings: A deep learning-aided case study of the Wufeng-Longmaxi Formations, Fuyan Syncline, Northern Guizhou","authors":"Ye Tao,&nbsp;Zhidong Bao,&nbsp;Fukang Ma","doi":"10.1016/j.acags.2025.100293","DOIUrl":"10.1016/j.acags.2025.100293","url":null,"abstract":"<div><div>The Wufeng-Longmaxi Formation shales are key targets for shale gas exploration, but they are often studied as a single stratigraphic unit with limited analysis of internal differences. This study combines traditional geological methods with deep learning to compare the reservoir characteristics of the Wufeng Formation, the first member of the Longmaxi Formation (Long 1), and the second member of the Longmaxi Formation (Long 2), identifying the main controlling factors of differences. We found that: (1) The Wufeng Formation primarily develops siliceous shale lithofacies (S), mixed siliceous shale lithofacies (S-2), and clay siliceous shale lithofacies (S-3). Long 1 develops mixed siliceous shale lithofacies (S-2) and clay siliceous shale lithofacies (S-3), while Long 2 exhibits clay and siliceous mixed shale lithofacies (M-2) and siliceous clay shale lithofacies (CM-1). (2) The YOLO-v8 model demonstrates higher accuracy in shale pore type detection than the YOLO-v10 model, with a maximum mAP of 78.9 %. Using the YOLO-v8 model, it was found that S, S-2, and S-3 lithofacies are dominated by dissolution pores and organic pores with larger specific surface areas and porosities, while CM-1 and M-2 lithofacies are characterized by dissolution pores with smaller specific surface areas and porosities. (3) Based on evaluation indicators such as TOC content, BET surface area, porosity, brittleness index, and gas content, S and S-2 are classified as Class I lithofacies, S-3 as Class II lithofacies, and M-2 and CM-1 as Class III lithofacies. The main controlling factor for the heterogeneity of the shale reservoirs in the study area is lithofacies.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"28 ","pages":"Article 100293"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable ore classification using SHAP-enhanced LightGBM: A case study from the Qiaomaishan deposit, China","authors":"Mingming Zhang ,&nbsp;Xiaoyuan Wang ,&nbsp;Cong Chen ,&nbsp;Jing Ding ,&nbsp;Xinsuo Zhou ,&nbsp;Jiangyanyu Qu","doi":"10.1016/j.acags.2025.100295","DOIUrl":"10.1016/j.acags.2025.100295","url":null,"abstract":"<div><div>Accurate ore classification is essential for geological exploration and mineral resource assessment, particularly in geologically complex settings. This study presents an interpretable classification framework that integrates the Light Gradient Boosting Machine (LightGBM) algorithm with post hoc model interpretation using SHapley Additive exPlanations (SHAP). The framework is applied to geochemical and spatial data from the Qiaomaishan Cu-S polymetallic deposit in Anhui Province, China. A dataset comprising 1588 samples-each containing concentrations of 29 geochemical elements along with 3D spatial coordinates-was used to train and evaluate 5 machine learning models: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF), LightGBM, and CatBoost. Among these, LightGBM achieved the best performance, with an average F1 score of 0.990 across 10 ore and lithological categories. An ablation experiment further confirmed the critical role of spatial coordinates, as removing them led to a notable drop in classification accuracy, particularly for underrepresented ore types. To interpret the LightGBM model, SHAP analysis was used to quantify feature contributions, identifying key geochemical elements such as W, Sr, Ca, and Fe as significant drivers of classification-consistent with the known mineralogical characteristics of the deposit. Moreover, SHAP beeswarm plots provided insights into the direction and magnitude of each feature's influence, enhancing the model's geological interpretability. SHAP-based feature selection further improved classification performance for underrepresented classes, including copper–sulphur–tungsten ore and copper ore. The proposed framework demonstrates strong potential for facilitating automated ore identification and supporting data-driven decision-making in complex geological environments.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"28 ","pages":"Article 100295"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fine-tuning small and open LLMs to automate geoscience data analysis workflows: A scalable approach","authors":"Jiyin Zhang,&nbsp;Wenjia Li,&nbsp;Xiang Que,&nbsp;Weilin Chen,&nbsp;Chenhao Li,&nbsp;Xiaogang Ma","doi":"10.1016/j.acags.2025.100311","DOIUrl":"10.1016/j.acags.2025.100311","url":null,"abstract":"<div><div>With the recent integration of Large Language Models (LLMs) into geoscience applications, agentic LLM-driven workflows have emerged as an innovative approach to streamline automated data analysis processes. Advanced proprietary LLMs like ChatGPT demonstrate strong performance in customized workflows due to their substantial computational resources and extensive pretraining on diverse datasets. However, deploying such workflows with commercial LLMs can incur significant costs, especially in terms of token consumption, necessitating a shift toward open-source models. In this study, we fine-tuned an open-source LLM (Llama 3.1) to handle geoscience data analysis tasks, leveraging the self-instruct method to generate synthetic training datasets. The proposed pipeline for designing LLM-driven workflows and fine-tuning open-source models using synthetic datasets enables scalability, allowing the integration of additional LLM agents to accommodate more complex tasks. Furthermore, this workflow serves as a template for researchers in other domains to develop similar solutions tailored to their specific needs. Our experimental evaluation compares the performance of ChatGPT-4o with the fine-tuned Llama 3.1 in the context of the proposed geoscience data analysis workflow. Results demonstrate that the fine-tuned open-source model achieves performance comparable to proprietary models, extending the applicability of open LLMs to domain-specific agentic workflows in data analysis.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"28 ","pages":"Article 100311"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing land use land cover changes in Mysuru taluk, Karnataka state, India using vision transformers","authors":"H.N. Mahendra ,&nbsp;V. Pushpalatha ,&nbsp;S. Mallikarjunaswamy ,&nbsp;S. Rama Subramoniam ,&nbsp;Arjun Sunil Rao ,&nbsp;N.C. Sanjay Shekar","doi":"10.1016/j.acags.2025.100308","DOIUrl":"10.1016/j.acags.2025.100308","url":null,"abstract":"<div><div>This study presents an analysis of land use and land cover changes in Mysuru Taluk, Karnataka, India, over a two-decade period (2004–2014 and 2014–2024), using Vision Transformers (ViTs) to enhance classification accuracy. Using Linear Imaging Self-Scanning Sensor (LISS-III) remote sensing data, our approach combines the powerful feature extraction capabilities of ViTs to address the complexities inherent in multi-temporal satellite data. Traditional methods for LULC mapping face challenges due to variability in land features and temporal changes, which impact classification accuracy. By employing ViTs, we aim to overcome these limitations through their self-attention approach, which can capture long-range dependencies in the data, thus offering a more refined classification process. Our study results show improved overall classification accuracy across the assessed years, achieving 95.07 % in 2004, 95.79 % in 2014, and reaching 96.74 % in 2024. These progressive results highlight the efficiency of ViTs in accurately classifying and detecting subtle land cover changes over time. Further, change detection analysis results show that the built-up area increased by 17.25 %, and agricultural land decreased by 16.24 % over two decades. The findings will assist policymakers and urban planners develop strategies to manage urbanization effectively while minimizing environmental impacts.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"28 ","pages":"Article 100308"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145690943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automating fault detection in seismic data: integrating image processing with deep learning","authors":"Ahmad Ashtari","doi":"10.1016/j.acags.2025.100286","DOIUrl":"10.1016/j.acags.2025.100286","url":null,"abstract":"<div><div>Fault interpretation in seismic images is crucial for identifying fluid accommodation and flow migration pathways in the oil and gas industry. Several algorithms have been developed to calculate seismic attributes which help identify faults. Despite these advancements, challenges still remain in fault interpretation due to the complexity of fault networks, noise, and quality of seismic data. Hybrid seismic attributes extracted through artificial neural networks can enhance fault interpretation. In the case of neural network-based approaches used for geological feature extraction, picking precise samples for training neural networks is vital. In this study, an innovative method based on the Shi–Tomasi corner detection algorithm has been introduced to automatically pick fault samples on seismic data to be used as input to deep neural networks to predict faults. The method has been tested on two field seismic images that were acquired at different surveys. The field examples indicate that the trained neural networks could give a precise and clear estimation of faults with different azimuths. This proves the proposed sampling method can effectively provide a high-quality training data set for deep neural networks to automatically predict faults from seismic data.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"28 ","pages":"Article 100286"},"PeriodicalIF":3.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}