Applied Computing and Geosciences最新文献

{"title":"Recent advances in explainable Machine Learning models for wildfire prediction","authors":"Abira Sengupta,&nbsp;Brendon J. Woodford","doi":"10.1016/j.acags.2025.100266","DOIUrl":"10.1016/j.acags.2025.100266","url":null,"abstract":"<div><div>Climate change has caused increasingly frequent occurrences of forest fires around the world. Machine Learning (ML) and Artificial Intelligence models have emerged to predict both the onset of wildfires and evaluate the extent of damage a wildfire would cause. However, understanding what factors lead to generating models that exhibit optimal performance and providing insight into the importance of features on model outcomes is the subject of ongoing research. To help answer these questions, we propose a framework which adopts recent advances in methods for obtaining optimal models along with the application of SHAP (SHapley Additive exPlanations) values to obtain the most important features which affect the performance of wildfire prediction models. We use this framework as a classification task to predict the likelihood of wildfire occurrence based on environmental conditions, using a data set which represents instances of forest fires in Algerian, and as a regression task to predict the burned area once a wildfire has begun, using a data set from Portugal that recorded the area burned after a fire event. Insights provided by this framework allow us to assess the efficacy of specific ML models for wildfire prediction, ultimately making recommendations as to which ML models are more suited towards these challenging tasks.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100266"},"PeriodicalIF":3.2,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780987","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":"Classification of microscopic images of rock thin sections based on TLCA-ResNet34","authors":"Zhenyu Zhao ,&nbsp;Shucheng Tan ,&nbsp;Hui Chen ,&nbsp;Pengwei Wang ,&nbsp;Qinghua Zhang ,&nbsp;Haoyu Wei ,&nbsp;Zhenlin Zhang","doi":"10.1016/j.acags.2025.100272","DOIUrl":"10.1016/j.acags.2025.100272","url":null,"abstract":"<div><div>Identifying microscopic images of rocks is a crucial method for rock identification, playing a vital role in geological exploration and mineral mining. To facilitate the quick classification and identification of rock thin sections under a microscope, a dataset with 3116 microscopic images of 9 types of rock thin sections was developed using publicly accessible network datasets. By adopting the transfer learning method, a context-aware residual block was designed using the coordinate attention(CA) mechanism, and a targeted TLCA-ResNet34 neural network model was developed. This model is capable of extracting deep-layer feature information from entire rock thin section images, thus achieving the classification and identification of microscopic images. The experimental results show that, compared with several other common models, TLCA-ResNet34, while maintaining the light weight of the model, has the best recognition accuracy, recall rate, and Matthews correlation coefficient (MCC) for the microscopy image test set. It can efficiently and accurately identify microscopic images of rocks.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100272"},"PeriodicalIF":3.2,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757227","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":"SRT-Ai: Identifying seismic reflection terminations using deep learning","authors":"Waleed M. AlGharbi ,&nbsp;Rebecca E. Bell ,&nbsp;Cédric M. John","doi":"10.1016/j.acags.2025.100271","DOIUrl":"10.1016/j.acags.2025.100271","url":null,"abstract":"<div><div>Seismic stratigraphy entails a regional scanning (reconnaissance) of seismic data to identify and annotate seismic reflection terminations. To identify these terminations in modern 3D seismic datasets, interpreters have to examine thousands of inlines and crosslines, which is a time-consuming process. Furthermore, accurate identification of these features relies heavily on human visual observation along with individual expertise.</div><div>A growing number of studies have shown promising results applying machine learning techniques to identify geological features from seismic data such as salt bodies and faults. However, the identification of seismic reflection terminations has not received the same level of interest and remains a manual process. One of the barriers to utilizing machine learning techniques in seismic interpretation is the lack of “labelled” data. In this study, we evaluate the ability of deep learning Convolutional Neural Networks (CNN) trained on synthetic seismic images to identify seismic reflection terminations.</div><div>A dataset comprising 160 000 synthetic seismic images that represent conformable and four types of seismic reflection terminations (truncation, toplap, onlap, and downlap) were created using geometric geological modelling and 1D convolution seismic modelling. The dataset was then split into two classes (“Contains Termination” and “No Termination”). A new CNN model architecture named “Seismic Reflection Terminations Attribute (SRT-Ai)” was trained on 80 % of the synthetic seismic dataset. SRT-Ai predicted the test set (remaining 20 %) with an accuracy and precision of 99.9 %. To test its generalization, SRT-Ai was also evaluated on real seismic images, achieving 91 % accuracy and 96 % precision against published interpretations used as reference labels. Qualitative analysis of predictions along seismic sections shows a strong correspondence between the model predictions and manual regional interpretations.</div><div>SRT-Ai is proposed as a screening tool that will assist seismic interpreters with the identification of major seismic terminations, minimise seismic interpretation uncertainties, reduce the time taken for seismic reconnaissance, and limit the reliance on human visual observation at the early stage of seismic interpretation process.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100271"},"PeriodicalIF":3.2,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721214","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":"Electrical anisotropy calculation of the continental crust by resistor network-based circuit simulations","authors":"Song Luo ,&nbsp;Haiying Hu ,&nbsp;Lidong Dai","doi":"10.1016/j.acags.2025.100265","DOIUrl":"10.1016/j.acags.2025.100265","url":null,"abstract":"<div><div>Electrical anisotropy has been broadly observed by magnetotelluric (MT) surveys in the continental crust. It is proposed to be caused by rock microfabrics, lithologic layering, or oriented alignment of fluid or melt in rocks, whereas the validity of these mechanisms has not yet been verified due to the lack of experimental and computational evidence. Laboratory measurements on the electrical anisotropy of crustal rocks are extremely challenging when considering microfabrics and oriented microcracks filled with fluid. In contrast, numerical modeling, being an efficient approach, can be used to compute the anisotropic physical properties of rocks. In this study, the electrical anisotropy of crustal rocks was first modeled by circuit simulation techniques using a random resistor network model, based on the lattice-preferred orientation, modal compositions, and mineral electrical conductivity. The results indicate that the conversion from single crystals to the corresponding aggregates leads to a great reduction in electrical anisotropy, particularly for quartz single crystal with high anisotropy. Moreover, the electrical anisotropy of two-phase aggregates decreases with the increasing proportion of the second low-anisotropy minerals (e.g., plagioclase), such as from quartzite to granite. For layered lithology, the lower-crustal gabbro has higher electrical anisotropy compared to middle-crustal quartz-bearing rocks. The modeled electrical anisotropy from the middle to lower crust matches well with the geophysical observations in the Central Great Basin.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100265"},"PeriodicalIF":2.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714167","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":"Attention and deformable convolution-based dual-task high-precision fault recognition","authors":"Zhen Peng ,&nbsp;Danping Cao ,&nbsp;Huiqun Xu ,&nbsp;Dan Zhu","doi":"10.1016/j.acags.2025.100267","DOIUrl":"10.1016/j.acags.2025.100267","url":null,"abstract":"<div><div>Deep learning has been widely applied in fault recognition task. However, current two-dimensional (2D) deep learning-based training methods fail to adequately consider the overall spatial characteristics of faults, resulting in discontinuous fault recognition results and unable to achieve the effect of three-dimensional (3D) deep learning training methods. To address this issue, we propose an attention and deformable convolution-based dual-task high-precision fault recognition method (ADTFM), which introduces a dual-task deep learning network architecture within the 2D training framework, effectively improving the fault recognition accuracy and reliability. ADTFM consists of two tasks (Main task and Auxiliary task) with the same network structure based on the deformable convolution operators and U-Net. The main task uses the Inline direction for training, and uses the deformable convolution operator to capture more accurate fault feature. At the same time, the auxiliary task is trained in the Time-slice direction, and the features generated by auxiliary task direction are transferred to the main task in training process. The two tasks are connected through the attention mechanism, so as to increase the spatial characteristics of faults in 2D training process, and effectively compensate for the spatial limitations of 2D training. By testing the public 3D datasets and the field 3D datasets, and comparing with the current high-precision FaultSeg3D fault recognition method, the results show that our method can improve the accuracy of fault recognition. Moreover, through the quantitative evaluation of computing consumption time and memory, it is shown that the proposed method effectively reduces the computational complexity and decreases the consumption of computational resources, and provide a more efficient solution for fault recognition task.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100267"},"PeriodicalIF":3.2,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721213","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":"Lithological mapping and spectroscopic studies of carbonatite and clinopyroxenite from Hogenakkal carbonatite complex, India","authors":"Saraah Imran ,&nbsp;Sourav Bhattacharjee ,&nbsp;Ajanta Goswami ,&nbsp;Aniket Chakrabarty","doi":"10.1016/j.acags.2025.100269","DOIUrl":"10.1016/j.acags.2025.100269","url":null,"abstract":"<div><div>The Paleoproterozoic Hogenakkal carbonatite complex, situated within the Mettur shear zone, Southern Granulite Terrain, Tamil Nadu, India, is known for its enigmatic carbonatite-clinopyroxenite association and lithology specific rare earth elements (REE) mineralization. The complex comprises two types of carbonatites (silicate-rich carbonatite-I, and silicate-poor carbonatite-II), intruding the clinopyroxenite as isolated pods or ovoid bodies, and are together emplaced within the granulite country rocks. This study employs Landsat 8 multispectral data to map the spatial distribution and extent of the clinopyroxenite dykes. These dykes serve as geological tracers for locating the spatially associated carbonatite bodies. In addition, the present work investigate the spectroscopic properties of REE-bearing carbonatites and clinopyroxenite. Petrography, Raman spectroscopy of minerals, and spectroradiometric measurements of rock samples support the interpretations derived from Principal Component Analysis (PCA), Spectral Angle Mapper (SAM), Support Vector Machine (SVM), Decision Tree, and Random Forest algorithms, thereby aiding in the identification of lithological variations and potential clinopyroxenite occurrences. Carbonatite-II shows more prominent REE absorption features compared to carbonatite-I. This is consistent with petrographic observations and Raman spectroscopy, which show that the REE mineralization in carbonatite-II is dominated by monazite-(Ce) and hydroxylbastnäsite-(Ce), whereas carbonatite-I contains allanite-(Ce) as the primary REE-bearing phase. This study exhibits the efficacy of Landsat series data and non-destructive spectroscopic methods for preliminary mineral exploration and evaluating REE potential before detailed field investigation.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100269"},"PeriodicalIF":2.6,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711663","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":"A lightweight knowledge graph-driven question answering system for field-based mineral resource survey","authors":"Mingguo Wang ,&nbsp;Chengbin Wang ,&nbsp;Jianguo Chen ,&nbsp;Bo Wang ,&nbsp;Wei Wang ,&nbsp;Xiaogang Ma ,&nbsp;Jiangtao Ren ,&nbsp;Zichen Li ,&nbsp;Yicai Ye ,&nbsp;Jiakai Zhang ,&nbsp;Yue Wang","doi":"10.1016/j.acags.2025.100268","DOIUrl":"10.1016/j.acags.2025.100268","url":null,"abstract":"<div><div>Geoscience data associated with mineral resource surveys have become essential digital assets for governments and mining companies. The rapid increase in the volume of geoscience data makes it challenging to acquire knowledge quickly. In this study, we proposed and built a workflow that employs knowledge graph techniques, deep learning, question templates, and matching algorithms to provide a lightweight question-answering service for field-based geologists involved in mineral resource surveys. Initially, we utilized deep-learning-based geological entities and their semantic relation recognition, along with relational data mapping, to construct the mineral resource survey knowledge graph based on the ontology model. We then employed question template matching, a geological entity recognition model, and a sentence transformer to determine the optimal question template and generate a query statement for knowledge acquisition from a knowledge graph based on the Cypher language. Subsequently, we utilized a subgraph and a short abstract to express the results. The comparison with large language models and retrieval-augmented generation indicates that our solution is suitable for field-based mineral source surveys in a poor network environment with low-performance devices, data privacy concerns, and narrowly focused topics. The results also suggest that further studies on geoscience pre-trained models, an informative library of question templates, and multimodal knowledge graphs are necessary to improve the performance of the knowledge graph-driven question-answering system.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100268"},"PeriodicalIF":2.6,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711662","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":"Automated fault network extraction in complex tectonic regimes: A hybrid machine learning and structural attributes approach","authors":"Muhammad Khan ,&nbsp;Andy Anderson Bery ,&nbsp;Yasir Bashir ,&nbsp;Sya'rawi Muhammad Husni Sharoni ,&nbsp;Syed Sadaqat Ali","doi":"10.1016/j.acags.2025.100264","DOIUrl":"10.1016/j.acags.2025.100264","url":null,"abstract":"<div><div>Interpreting seismic faults is crucial for prospect generation, reservoir modeling, and CO₂ storage assessment. However, identifying faults in complex tectonic regimes remains challenging, particularly in regions that have experienced multiple phases of tectonic activity. Despite advancements in structural seismic attributes and machine learning, interpreters often still rely on manual methods to analyze intricate fault systems, such as those found in the Poseidon study area located in the Browse basin, Northwestern Australia, where the fault network is shaped by both extensional and compressional tectonic events. This paper introduces a hybrid approach that combines machine learning with seismic structural attributes to extract complex fault networks from 3D seismic data. The method begins by using pre-trained models to generate a fault probability cube, which is then refined through re-training with manually labeled data to incorporate local structural knowledge. To address false negatives, the model is further retrained using an ant-tracking volume generated from the fault probability cube of the manually trained model as automatically labeled data. The fault probability cube is regenerated from the automatically labeled trained model and further enhanced by post-processing techniques, such as ant-tracking, to improve fault connectivity and streamline the automated fault identification process. This hybrid approach effectively detects and extracts both major and minor discontinuities from 3D seismic data with high accuracy, significantly reducing the time and effort required for interpretation compared to traditional techniques.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100264"},"PeriodicalIF":2.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711698","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":"Assessing data reliability for AI-driven volcanic rock dating: A comparison of electron microprobe and laser ablation mass spectroscopy","authors":"Ali Salimian ,&nbsp;Megan Watfa ,&nbsp;Ram Grung ,&nbsp;Lorna Anguilano","doi":"10.1016/j.acags.2025.100263","DOIUrl":"10.1016/j.acags.2025.100263","url":null,"abstract":"<div><div>This study explores the integrationof artificial intelligence (AI) and modern data analytics for accurately predicting and classifying three distinct periods of volcanic activity. By leveraging previously dated volcanic samples, we assess whether existing age and geochemical data can reliably group and predict volcanic episodes. Our study focuses on the Kula Volcanic Province (Turkey). We compare the effectiveness of two analytical techniques—Electron Microprobe Analysis (EPMA) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS)—in producing high-quality datasets for training deep learning models. While EPMA provides major and minor elemental compositions, LA-ICP-MS offers a broader range of trace elements, which may improve classification accuracy. Two experiments were conducted to evaluate the feasibility of AI-based volcanic rock age estimation. In the first experiment, an autoencoder and unsupervised clustering were applied to reduce dimensionality and group samples based on their elemental composition. The results revealed that EPMA data lacked sufficient detail to form well-defined clusters, whereas LA-ICP-MS data produced clusters that closely aligned with true age classes due to their higher sensitivity to trace elements. In the second experiment, a deep neural network (DNN) was trained to classify rock ages. The LA-ICP-MS-based model achieved a classification accuracy of 95 %, significantly outperforming the EPMA-based model (72 %). These findings underscore the importance of data quality and analytical technique selection in AI-powered geochronology, demonstrating that high-quality trace element data enhances AI model performance for volcanic rock age estimation.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100263"},"PeriodicalIF":2.6,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144604315","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":"Natural fracture network model using Gaussian simulation and machine learning algorithms","authors":"Timur Merembayev,&nbsp;Yerlan Amanbek","doi":"10.1016/j.acags.2025.100258","DOIUrl":"10.1016/j.acags.2025.100258","url":null,"abstract":"<div><div>In this paper, a fracture network model is proposed to enhance the understanding of subsurface fracture characterization. The model combines geostatistical methods such as sequential indicators and Gaussian simulations. The model uses data from natural faults in Kazakhstan to predict the segment, azimuth, and length of fractures in unknown areas. The model is validated by comparing the simulated fracture networks with the original fracture data and by hiding some regions within the fracture network. The results show that the geostatistical methods perform better than the machine learning algorithm for azimuth prediction, while the machine learning algorithm performs better for length prediction. In addition, the validation of the fracture network model is conducted by comparing the production curve profiles in the tracer test setting. They are in good agreement.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100258"},"PeriodicalIF":2.6,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563203","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}