Applied Computing and Geosciences最新文献

{"title":"Stacking modeling with genetic algorithm-based hyperparameter tuning for uniaxial compressive strength prediction","authors":"Tanveer Alam Munshi,&nbsp;Khanum Popi,&nbsp;Labiba Nusrat Jahan,&nbsp;M. Farhad Howladar,&nbsp;Mahamudul Hashan","doi":"10.1016/j.acags.2025.100276","DOIUrl":"10.1016/j.acags.2025.100276","url":null,"abstract":"<div><div>Measuring rock strength using an uniaxial testing machine is destructive and costly, requiring high-quality rock samples. This work suggests an alternate approach that makes use of machine learning techniques to predict uniaxial compressive strength (UCS). The input parameters for this investigation were derived from 180 datasets containing well log variables such as resistivity (RT), sonic travel time (DT), and gamma-ray (GR), as well as rock properties like density. All these datasets came from a shaly sand reservoir in the Bengal Basin. To forecast UCS, a number of methods were used, such as multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multiple variable regression (MVR). Additionally, a hybrid stacking model that combines these algorithms was developed. Hyperparameter optimization was conducted using grid search and genetic algorithm. A notable contribution of this study lies in the application of both grid search and genetic algorithm (GA) for hyperparameter optimization, implemented across both individual base learners and the stacking ensemble model. Regression metrics including coefficient of determination (R²), mean absolute error (MAE), root mean square error (RMSE), maximum error (MaxE), and minimum error (MinE) were used to assess the effectiveness of the models. The proposed stacking model achieved a high testing R² of 0.9762, outperforming individual models. The methodology provided in this paper can assist engineers and researchers in quickly and precisely determining the strength of reservoir rock by using a few log features, hence decreasing the reliance on labor-intensive and time-consuming laboratory work.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100276"},"PeriodicalIF":3.2,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809487","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":"Development of a technique to identify μm-sized organic matter in asteroidal material: An approach using machine learning","authors":"Rahul Kumar,&nbsp;Katsura Kobayashi,&nbsp;Christian Potiszil,&nbsp;Tak Kunihiro","doi":"10.1016/j.acags.2025.100277","DOIUrl":"10.1016/j.acags.2025.100277","url":null,"abstract":"<div><div>Asteroidal materials contain organic matter (OM), which records a number of extraterrestrial environments and thus provides a record of Solar System processes. OM contain essential compounds for the origin of life. To understand the origin and evolution of OM, systematic identification and detailed observation using in-situ techniques is required. While both nm- and μm-sized OM were studied previously, only a small portion of a given sample surface was investigated in each study. Here, a novel workflow was developed and applied to identify and classify μm-sized OM on mm-sized asteroidal materials. The workflow involved image processing and machine learning, enabling a comprehensive and non-biased way of identifying, classifying, and measuring the properties of OM. We found that identifying OM is more accurate by classification with machine learning than by clustering. On the approach of classification with machine learning, five algorithms were tested. The random forest algorithm was selected as it scored the highest in 4 out of 5 accuracy parameters during evaluation. The workflow gave modal OM abundances that were consistent with those identified manually, demonstrating that the workflow can accurately identify 1-15 μm-sized OM. The size distribution of OM was modeled using the power-law distribution, giving slope α values that were consistent with fragmentation processes. The shape of the OM was quantified using circularity and solidity, giving a positive correlation and indicating these properties are closely related. Overall, the workflow enabled identification of many OM quickly and accurately and the obtainment of chemical and petrographic information. Such information can help the selection of OM for further in-situ techniques, and elucidate the origin and evolution of OM preserved in asteroidal materials.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100277"},"PeriodicalIF":3.2,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826846","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":"On-board camera-based automatic zoning method for heading face by using computerized rock drilling cart","authors":"Yong-Feng Li ,&nbsp;Huan Li ,&nbsp;Jing Xiao ,&nbsp;Weidong Ren ,&nbsp;Mohammed Abdalla Elsharif Ibrahim","doi":"10.1016/j.acags.2025.100275","DOIUrl":"10.1016/j.acags.2025.100275","url":null,"abstract":"<div><div>During construction, drilling parameters are manually adjusted by the operator, which can affect the blasting effect due to inappropriate initial parameters. To address this issue, an automatic optimal drilling method based on image partitioning of the heading face is proposed: i) Obtain images of the heading face using a suitable vehicle camera, and calculate pixel coordinates on the virtual heading face through rock drilling cart positioning and virtual heading face positioning; ii) Apply the region growth algorithm to extract the image region of the heading face, segment the image into several super-pixel units using the linear iterative clustering algorithm, followed by combining super-pixels based on the gray difference criterion. The resulting super-pixel blocks serve as the training sample set for the rock-partition method based on super-pixels and support vector machine (SVM); iii) Establish a database of drilling parameters. The results demonstrate that, compared to the region growth algorithm, the classification method based on super-pixels and SVM has higher accuracy. The algorithm has high accuracy of partition effect and good real-time performance, providing a reliable basis for optimizing the opening parameters.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100275"},"PeriodicalIF":3.2,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827362","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":"Orange-Volcanoes: A new open and collaborative platform to perform data-driven investigations and machine learning analyses in petrology and volcanology","authors":"Alessandro Musu ,&nbsp;Valerio Parodi ,&nbsp;Marko Toplak ,&nbsp;Alessandro Carfì ,&nbsp;Mónica Ágreda-López ,&nbsp;Fulvio Mastrogiovanni ,&nbsp;J. ZhangZhou ,&nbsp;Diego Perugini ,&nbsp;Donato Belmonte ,&nbsp;Penny E. Wieser ,&nbsp;Blaž Zupan ,&nbsp;Maurizio Petrelli","doi":"10.1016/j.acags.2025.100270","DOIUrl":"10.1016/j.acags.2025.100270","url":null,"abstract":"<div><div>Orange-Volcanoes is an extension of the open-source Orange data mining platform specifically tailored for geochemical, petrological, and volcanological investigations. Orange-Volcanoes enhances the original platform by incorporating specialized tools to enable interactive data-driven investigations in geochemistry, such as performing Compositional Data Analysis (CoDA). Applying CoDA transformations enables the use of many standard and multivariate statistical methods like principal component analysis, discriminant analysis, and hierarchical clustering on compositional data. In this way, Orange-Volcanoes allows for the application of a wide range of data mining and statistical methods implemented in Orange using geochemical data. Moreover, Orange allows the use of advanced methods in the field of explainable artificial intelligence, such as feature importance and Shapley additive explanations. Also, within Orange-Volcanoes, we demonstrate the flexibility of the Orange platform by developing visual tools that allow for conducting mineral-liquid equilibrium tests and calculating thermo-barometric estimates. The Orange-Volcanoes supports collaborative efforts and reproducibility by offering a visual programming interface that requires no coding experience, making it accessible to a wide range of users, including scientists, educators, and students. We provide a series of case studies, including interactive petrological data exploration and clustering in tephra studies to highlight Orange-Volcanoes’ potential and versatility in volcanological applications. Orange-Volcanoes can be downloaded using pip, and its documentation is available at <span><span>https://orange3-volcanoes.readthedocs.io/en/latest/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"27 ","pages":"Article 100270"},"PeriodicalIF":3.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780986","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":"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}