Applied Computing and Geosciences最新文献

{"title":"Machine Learning model interpretability using SHAP values: Application to Igneous Rock Classification task","authors":"Antonella S. Antonini ,&nbsp;Juan Tanzola ,&nbsp;Lucía Asiain ,&nbsp;Gabriela R. Ferracutti ,&nbsp;Silvia M. Castro ,&nbsp;Ernesto A. Bjerg ,&nbsp;María Luján Ganuza","doi":"10.1016/j.acags.2024.100178","DOIUrl":"10.1016/j.acags.2024.100178","url":null,"abstract":"<div><p>El Fierro intrusive body is one of the bodies that compose the La Jovita–Las Aguilas mafic–ultramafic belt, located in the Sierra Grande de San Luis, Argentina. The units of this belt carry a base metal sulfide (BMS) mineralization and platinum group minerals (PGM). The macroscopic description of mafic and ultramafic rocks, as is usually done by the mining exploration companies, leads to an imprecise modal classification of the rocks. In this study, we develop a random forest-based prediction model, which uses geochemical parameters to classify mafic and ultramafic rocks intercepted by drill cores. This model showed an accuracy of between 86% and 94%, and an f1_score of 96%. Random forest classification is a widely adopted Machine Learning approach to construct predictive models across various research domains. However, as models become more complex, their interpretation can be considerably difficult. To interpret the model results, we use both global and local perspectives, incorporating the SHAP (SHapley Additive exPlanations) method. The SHAP technique allows us to analyze individual samples using force plots, and provides a measure of the importance of each geochemical input attribute in the model output. As a result of analyzing the contribution of each input feature to the model, the three variables with the highest contributions were identified in the following order: <span><math><mrow><msub><mrow><mi>Al</mi></mrow><mrow><mn>2</mn></mrow></msub><msub><mrow><mi>O</mi></mrow><mrow><mn>3</mn></mrow></msub></mrow></math></span>, <span><math><mi>MgO</mi></math></span>, and <span><math><mi>Sr</mi></math></span>.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100178"},"PeriodicalIF":2.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000259/pdfft?md5=4c1e0ad425c657a335a51d5db628874f&pid=1-s2.0-S2590197424000259-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141838464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User-friendly carbon-cycle modelling and aspects of Phanerozoic climate change","authors":"Trond H. Torsvik ,&nbsp;Dana L. Royer ,&nbsp;Chloe M. Marcilly ,&nbsp;Stephanie C. Werner","doi":"10.1016/j.acags.2024.100180","DOIUrl":"10.1016/j.acags.2024.100180","url":null,"abstract":"<div><p>Carbon-cycle modelling is essential for testing the main carbon sources and sinks as climate forcings, and we introduce and describe <em>GEOCARB_NET,</em> a graphical user interface for the geologic carbon and sulfur cycle model <em>GEOCARBSULFvolc</em>. The software system is menu-driven, user-friendly, and the user is never far removed from the basic input parameters from which atmospheric CO₂ and O₂ concentrations can be derived. <em>GEOCARB_NET</em> is supplied with several published models and the user can easily test and refine these models with different parametrizations. <em>GEOCARB_NET</em> also contains libraries of models and proxy data, which easily can be compared with each other. Our examples focus on how to use <em>GEOCARB_NET</em> in the context of Phanerozoic climate change and highlights how certain key input parameters can seriously affect reconstructed CO₂ levels.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100180"},"PeriodicalIF":2.6,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000272/pdfft?md5=c911e4a4b4e93c4ca86a4883260225da&pid=1-s2.0-S2590197424000272-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141950520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using multiple-point geostatistics for geomodeling of a vein-type gold deposit","authors":"Aida Zhexenbayeva ,&nbsp;Nasser Madani ,&nbsp;Philippe Renard ,&nbsp;Julien Straubhaar","doi":"10.1016/j.acags.2024.100177","DOIUrl":"10.1016/j.acags.2024.100177","url":null,"abstract":"<div><p>Geostatistical cascade modeling of Mineral Resources is challenging in vein-type gold deposits. The narrow shape and long-range features of these auriferous veins, coupled with the paucity of drill-hole data, can complicate the modeling process and make the use of two-point geostatistical algorithms impractical. Instead, multiple-point geostatistics techniques can be a suitable alternative. However, the most challenging part in implementing the MPS is to use a suitable training data set or training image (TI). In this paper, we suggest using the radial basis function algorithm to build a training image and the DeeSse algorithm, one of the multiple-point statistics (MPS) methods, to model two long-range veins in a gold deposit. It is demonstrated that DeeSse can replicate long-range vein features better than plurigaussian simulation techniques when there is a lack of conditioning data. This is shown by several validation processes, such as comparing simulation results with an interpretive geological block model and replicating geological proportions.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100177"},"PeriodicalIF":2.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000247/pdfft?md5=6267aeb1f34a82ff3e55ae08fe0d7c7d&pid=1-s2.0-S2590197424000247-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pore-to-Darcy scale permeability upscaling for media with dynamic pore structure using graph theory","authors":"Achyut Mishra ,&nbsp;Lin Ma ,&nbsp;Sushma C. Reddy ,&nbsp;Januka Attanayake ,&nbsp;Ralf R. Haese","doi":"10.1016/j.acags.2024.100179","DOIUrl":"10.1016/j.acags.2024.100179","url":null,"abstract":"<div><p>Permeability is a key rock property important for scientific applications that require simulation of fluid flow. Although permeability is determined using core flooding experiments, recent advancements in micro-CT imaging and pore scale fluid flow simulations have made it possible to constrain permeability honoring pore scale rock structure. Previous studies have reported that complex association of pores and solid grains often results in preferential flow paths which influence the resulting velocity field and, hence, the upscaled permeability value. Additionally, the pore structure may change due to geochemical processes such as microbial growth, mineral precipitation and dissolution. This could result in a flow field which dynamically evolves spatially and temporally. It would require numerous experiments or full physics simulations to determine the resultant upscaled Darcy permeability for such dynamically changing systems. This study presents a graph theory-based approach to upscale permeability from pore-to-Darcy scale for changing pore structure. The method involves transforming a given micro-CT rock image to a graph network map followed by the identification of the least resistance path between the inlet and the outlet faces using Dijkstra's algorithm where resistance is quantified as a function of pore sizes. The least resistance path is equivalent to the path of lowest resistance within the domain. The method was tested on micro-CT images of the samples of Sherwood Sandstone, Ketton Limestone and Estaillades Limestone. The three micro-CT images were used to generate 30 synthetic scenarios for geochemically induced pore structure changes covering a range of pore and solid grain growth. The least resistance value obtained from Dijkstra's algorithm was observed to correlate with upscaled permeability value determined from full physics simulations, while improving computational efficiency by a factor of 250. This provides confidence in using graph theory method as a proxy for full physics simulations for determining effective permeability for samples with changing pore structure.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100179"},"PeriodicalIF":2.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000260/pdfft?md5=99af3beb4235c62c8f4403fc8f64f548&pid=1-s2.0-S2590197424000260-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141732182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning approach for predicting monsoon dynamics of regional climate zones of India","authors":"Yajnaseni Dash ,&nbsp;Naween Kumar ,&nbsp;Manish Raj ,&nbsp;Ajith Abraham","doi":"10.1016/j.acags.2024.100176","DOIUrl":"10.1016/j.acags.2024.100176","url":null,"abstract":"<div><p>The complex interplay of various complicated meteorological and oceanic processes has made it more difficult to accurately predict Indian monsoon rainfall. A future-oriented and one of the most potential methods for predictive analytics is deep learning. The proposed work exploits empirical Mode Decomposition-Detrended Fluctuation Analysis (EMD-DFA) and long short-term memory (LSTM) deep neural networks (EMD-LSTM) to build novel predictive models and analyze predictability effectively. The time series data of each homogeneous monsoon zone are decomposed into different empirical time series components known as intrinsic mode functions (IMFs). The proposed work's obtained results report that the EMD-LSTM hybrid strategy consistently outperforms other methods in terms of accuracy. Furthermore, we examined possible relationships between each homogeneous monsoon zone and multiple climate drivers, shedding light on the complicated relationships that influence monsoon patterns. This study presents a unique way of predicting complex monsoon rainfall in homogenous regions of India and marks the first application of the EMD-LSTM technique for this purpose to the best of our knowledge which is necessary for improving water conservation and distribution at different climate zones of India.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100176"},"PeriodicalIF":2.6,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000235/pdfft?md5=684e87c5524b3fffd400848eea44d76b&pid=1-s2.0-S2590197424000235-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Normalized Difference Vegetation Index forecasting from air temperature, soil moisture and precipitation using a deep neural network","authors":"Loghman Fathollahi ,&nbsp;Falin Wu ,&nbsp;Reza Melaki ,&nbsp;Parvaneh Jamshidi ,&nbsp;Saddam Sarwar","doi":"10.1016/j.acags.2024.100174","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100174","url":null,"abstract":"<div><p>The complexity of the relationship between climate variables including temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI) arises from the complex interaction between these factors. NDVI is a widely used index to analyze the characteristics of vegetation cover, including its dynamic patterns. It is a crucial parameter for examining vegetation stability, which is vital for ensuring sustainable food production. This study aims to develop a global-scale NDVI forecasting model based on deep learning algorithms that consider climate variables. The model was trained using three years of global data, including NDVI, temperature, precipitation, and soil moisture. The results of this study demonstrate the effectiveness of the deep learning model for forecasting NDVI. The model accurately predicted NDVI values, as evidenced by the high coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) values and the negligible average disparity between predicted and observed NDVI values. The study conducted an analysis of the model’s performance both temporally and spatially. The performance of the model was examined for each month and the overall performance of the model for months presented as the model’s temporal performance overall. Additionally, the model’s performance was analyzed at different latitudes, categorized as mid-latitude and low-latitude performance. The temporal analysis of the model demonstrated an overall <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> value of 0.85 and an RMSE of 0.096. Meanwhile, the spatial analysis of the model showed that it performed well at low-latitude, with an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> value of 0.84 and an RMSE of 0.098, and at mid-latitude, with an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> value of 0.82 and an RMSE of 0.095. This suggests that the model’s forecasted NDVI values showed a small average difference compared to actual values in both temporal and spatial analyses. Overall, the study supports the idea that deep learning models can effectively forecast NDVI using climate variables across various geographical zones and throughout different months of the year.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100174"},"PeriodicalIF":2.6,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000211/pdfft?md5=e09ef08540e46827c2642d96f512f5c1&pid=1-s2.0-S2590197424000211-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PixelSWAT: A user-friendly ArcGIS tool for preparing inputs to run SWAT in a distributed discretization scheme","authors":"Nyigam Bole,&nbsp;Arnab Bandyopadhyay,&nbsp;Aditi Bhadra","doi":"10.1016/j.acags.2024.100175","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100175","url":null,"abstract":"<div><p>This paper documents the development of PixelSWAT, a Graphical User interface (GUI) python toolbox developed with the motive of creating gridded watershed and stream features to run the Soil and Water Assessment Tool (SWAT) in a distributed discretization scheme thus allowing optimum utilization of gridded weather datasets. Additionally, the tool also aims to automate the preparation of SWAT weather input files from Network Common Data (NetCDF) files for any SWAT user along with the option to interpolate the weather files for each grid. A case study was conducted in the Mago basin of Tawang, Arunachal Pradesh, using gridded weather datasets for hydrological simulation. Three SWAT models were prepared – a conventional SWAT model; a 500 m and a 1000 m gridded watershed PixelSWAT models. Statistical indices Nash Sutcliffe (NSE), Coefficient of Determination (R²) and Percent Bias (PBIAS) showed that the PixelSWAT projects performed marginally better than the conventional model and also incorporated the weather data more meaningfully.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100175"},"PeriodicalIF":2.6,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000223/pdfft?md5=015d448ba5469b5c76c4bfae001af77e&pid=1-s2.0-S2590197424000223-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational fluid dynamics in carbonate rock wormholes using magnetic resonance images as structural information","authors":"Gustavo Solcia,&nbsp;Bernd U. Foerster,&nbsp;Mariane B. Andreeta,&nbsp;Tito J. Bonagamba,&nbsp;Fernando F. Paiva","doi":"10.1016/j.acags.2024.100172","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100172","url":null,"abstract":"<div><p>Computational fluid dynamics (CFD) is an essential tool with growing applications in many fields. In petrophysics, it is common to use computed tomography in those simulations, but in medicine, magnetic resonance imaging (MRI) is also being used as a basis for structural information. Wormholes are high-permeability structures created by the acidification of carbonate reservoirs and can impact reservoir production. CFD combined with MRI can benefit the study of wormholes in petrophysics, but combining both techniques is still a challenge. The objective of this study is to develop a pipeline for performing CFD in wormholes with MRI data. Using three samples of carbonate rocks acidified with 1.5% hydrochloric acid at 0.1, 1, and 10 ml/min, we acquired <span><math><mrow><mn>300</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> resolution T2-weighted images and experimental measurements of pressure data within flow rates of 5 to 50 ml/min. We applied cropping, bias field correction, non-local means denoising, and segmentation in the image processing step. For the 3D reconstruction, we used marching cubes to generate the surface mesh, the Taubin filter for surface smoothing, and boundary modeling with Blender. Finally, for the CFD, we generated volumetric meshes with cfMesh and used the OpenFOAM simpleFoam solver to simulate an incompressible, stationary, and laminar flow. We analyzed the effect of surface smoothing, estimating edge displacements, and measured the simulation pressure at the same flow rates as the experiments. Surface smoothing had a negligible impact on the overall edge position. For most flow rates, the simulation and experimental pressure measurements matched. A possible reason for the discrepancies is that we did not consider the surrounding porous media in the simulations. In summary, our work had satisfactory results, demonstrating CFD’s feasibility in studying wormholes using MRI.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100172"},"PeriodicalIF":2.6,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000193/pdfft?md5=059cc5ef82a79ca57be4db288a9600db&pid=1-s2.0-S2590197424000193-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141444287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing estuary salinity prediction: A Machine Learning and Deep Learning based approach","authors":"Leonardo Saccotelli ,&nbsp;Giorgia Verri ,&nbsp;Alessandro De Lorenzis ,&nbsp;Carla Cherubini ,&nbsp;Rocco Caccioppoli ,&nbsp;Giovanni Coppini ,&nbsp;Rosalia Maglietta","doi":"10.1016/j.acags.2024.100173","DOIUrl":"https://doi.org/10.1016/j.acags.2024.100173","url":null,"abstract":"<div><p>As critical transitional ecosystems, estuaries are facing the increasingly urgent threat of salt wedge intrusion, which impacts their ecological balance as well as human-dependent activities. Accurately predicting estuary salinity is essential for water resource management, ecosystem preservation, and for ensuring sustainable development along coastlines. In this study, we investigated the application of different machine learning and deep learning models to predict salinity levels within estuarine environments. Leveraging different techniques, including Random Forest, Least-Squares Boosting, Artificial Neural Network and Long Short-Term Memory networks, the aim was to enhance the predictive accuracy in order to better understand the complex interplay of factors influencing estuarine salinity dynamics. The Po River estuary (Po di Goro), which is one of the main hotspots of salt wedge intrusion, was selected as the study area. Comparative analyses of machine learning models with the state-of-the-art physics-based Estuary box model (EBM) and Hybrid-EBM models were conducted to assess model performances. The results highlighted an improvement in the machine learning performance, with a reduction in the RMSE (from 4.22 psu obtained by physics-based EBM to 2.80 psu obtained by LSBoost-Season) and an increase in the <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> score (from 0.67 obtained by physics-based EBM to 0.85 by LSBoost-Season), computed on the test set. We also explored the impact of different variables and their contributions to the predictive capabilities of the models. Overall, this study demonstrates the feasibility and effectiveness of ML-based approaches for estimating salinity levels due to salt wedge intrusion within estuaries. The insights obtained from this study could significantly support smart management strategies, not only in the Po River estuary, but also in other location.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"23 ","pages":"Article 100173"},"PeriodicalIF":2.6,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S259019742400020X/pdfft?md5=e1745be59526ad5e06d93bdaa293674d&pid=1-s2.0-S259019742400020X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge-based query system for the critical minerals","authors":"Armita Davarpanah ,&nbsp;Hassan A. Babaie ,&nbsp;W. Crawford Elliott","doi":"10.1016/j.acags.2024.100167","DOIUrl":"10.1016/j.acags.2024.100167","url":null,"abstract":"<div><p>Critical minerals are increasingly used in advanced, modern technologies. Exploration for these minerals require efficient mechanisms to search for the latest geological knowledge about the petrogenesis and spatial distribution of these essential resources. Although the current text-based deposit classification schemes help geoscientists to understand how and where these critical minerals form, they cannot easily be queried by software without extensive natural language processing and knowledge modeling. Ontologies can explicitly specify the knowledge scattered in the texts and tables of these schemes and the Critical Minerals Mapping Initiative (CMMI) database by way of logical structures whose results can automatically be processed and queried. They can also draw new knowledge by inference from the ones that are explicitly specified in them. These qualities make ontologies a perfect choice for digital knowledge storage, search, and extraction. The Critical Minerals Ontology (CMO) is described herein by reusing the logical class and property structures of the top-level Basic Formal Ontology (BFO) and mid-level Common Core Ontologies (CCO) and Relation Ontology (RO). The CMO formally models the knowledge about the critical mineral systems using the latest deposit classification scheme and the CMMI database schema. The ontology specifies the geochemical and geological processes that operate in various geotectonic environments of mineral systems to form the critical minerals in different deposit types. It models the properties of both the host minerals that contain the rare-earth elements and those that bear other types of elements. The CMO also represents uses of specific critical minerals in the manufacturing of industrial products, their alternate substitutes, and countries that produce, import, and export them. A query system, applying the Python programming language, accesses the knowledge modeled in the CMO and allows users through interactive web pages to query the ontology and extract different types of information from it. The ontology and the query system are useful for research in ore mineralogy and critical mineral prospecting. The information modeled by the ontology and served by the query system allows users to classify their ore specimen data into specific deposit types.</p></div>","PeriodicalId":33804,"journal":{"name":"Applied Computing and Geosciences","volume":"22 ","pages":"Article 100167"},"PeriodicalIF":3.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590197424000144/pdfft?md5=2d9e8afd7172f753322344e24e6d8d5b&pid=1-s2.0-S2590197424000144-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141144553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}