[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125065-en":3,"doc-seo-125065-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125065,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","aiION - A Machine Learning Approach to Geothermal Exploration - Insight & Methodology","aiION presents a data-driven machine learning workflow for early-stage geothermal exploration, grounded in geochemical evidence from groundwater samples. The material reviews geothermal’s renewable value and the key exploration bottlenecks, including high predevelopment financial risks, difficulty detecting blind resources, and over-reliance on expert judgment. It details geochemical and geothermometry foundations, then explains dataset construction, data integrity screening, feature selection, transformation, clustering, and comparative ML model development for improved reservoir temperature prediction and global transferability.","AiION: a machine learning approach to geothermal exploration .  \nALGAIAR, M.M.  \n2025  \nThis document was downloaded from [https://openair.rgu.ac.uk](https://openair.rgu.ac.uk)  \nGaining Momentum   \n26–27 February 2025 —Virtual Event 2025  \naiION: A Machine Learning Approach to  \nGeothermal Exploration  \nMahmoud M. AlGaiar  \nSchool of Computing, Engineering & Technology  \nRobert Gordon University  \nFebruary 2025  \nGaining Momentum  \n26–27 February 2025 —Virtual Event  \n2025  \nGeothermal Overview  \nRenewable Source  \nGeothermal energy is derived from Earth's heat, providing stable, sustainable power.  \nGlobal Growth  \nGeothermal energy could meet 15% of global electricity demand by 2050, with a potential global capacity of 800 gigawatts-equivalent to the current electricity demand of the US and India combined (IEA 2025) .  \nKey Countries  \nSignificant in USA, Indonesia, Philippines, Türkiye, and New Zealand.  \n[Visit](Visit www.spe-aberdeen.org/events)[ www.spe-aberdeen.org/events](Visit www.spe-aberdeen.org/events) for more information.   \nGaining Momentum  \n26–27 February 2025 —Virtual Event  \n2025  \nExploration Challenges  \n1  \n2  \n3  \nFinancial Risks  \nHigh costs during predevelopment stages, including surface surveys and exploratory drilling.  \nHidden Resources  \nDifficulty in identifying blind geothermal resources without surface manifestations.  \nExpert Reliance  \nTraditional methods heavily depend on expert knowledge, leading to uncertainties.  \n[Visit](Visit www.spe-aberdeen.org/events)[ www.spe-aberdeen.org/events](Visit www.spe-aberdeen.org/events) for more information.   \nGaining Momentum  \n26–27 February 2025 —Virtual Event  \n2025  \nGeochemical Analysis  \nCost-Effective  \nGeochemical data from groundwater samples are crucial in early exploration stages.  \n[Visit](Visit www.spe-aberdeen.org/events)[ www.spe-aberdeen.org/events](Visit www.spe-aberdeen.org/events) for more information.   \nInsightful  \nProvides valuable information on subsurface characteristics and reservoir properties.  \nAnalytical  \nHelps determine reservoir temperature, heat flow, and boundary conditions.  \nGaining Momentum  \n26–27 February 2025 —Virtual Event  \n2025  \nGeochemical Applications  \nWater Types  \nAqueous species, major cations/anions, isotopes, and trace elements.  \nGeothermometers  \nEstimate subsurface reservoir temperatures based on chemical composition.  \nReservoir Insights  \nIdentify geothermal characteristics and potential for energy extraction.  \n[Visit](Visit www.spe-aberdeen.org/events)[ www.spe-aberdeen.org/events](Visit www.spe-aberdeen.org/events) for more information.   \n[Visit](Visit www.spe-aberdeen.org/events)[ www.spe-aberdeen.org/events](Visit www.spe-aberdeen.org/events) for more information.  \nGaining Momentum  \n26–27 February 2025 —Virtual Event  \n2025  \nGeothermometry  \n1  \nClassical Geothermometers  \nfunction based on temperature-dependent mineral-fluid equilibrium reactions, primarily  \nutilizing silica concentrations and cation ratios (Na-K, Na-K-Ca, K-Mg) in geothermal waters.  \n2  \nMulticomponent Geothermometry  \nanalyzes the equilibrium between multiple minerals, focusing on the  \nconvergence of mineral saturation indices at the true reservoir temperature.  \n3  \nData-Driven Geothermometers  \na modern approach that utilizes machine learning and statistical methods to establish correlations between fluid chemistry and reservoir temperatures.  \nGaining Momentum  \n26–27 February 2025 —Virtual Event  \n2025  \n[Visit](Visit www.spe-aberdeen.org/events)[ www.spe-aberdeen.org/events](Visit www.spe-aberdeen.org/events) for more information.  \nMethodology  \n1  \nComprehensive Dataset  \nUtilizing water samples from diverse geothermal regions in Nevada  \n2  \nAdvanced Methodology  \nIntegrating classical geothermometers, multi-component geothermometry, and machine learning  \n3  \nImproved Accuracy  \nDemonstrating excellent performance, explaining variance in training and test data  \n4  \nGlobal Applicability  \nEvaluation on new","cbCaifSSL9TizSms","https://ap.wps.com/l/cbCaifSSL9TizSms","pdf",3160383,1,22,"English","en",105,"# Geothermal Overview\n## Renewable Energy and Global Growth\n## Exploration Challenges\n# Geochemical Analysis and Applications\n## Water Types and Reservoir Insights\n## Geothermometry Approaches\n# Methodology\n## Comprehensive Dataset\n## Data Processing and Transformation\n## Clustering and ML Model Development\n# Exploratory Data Analysis\n## Dataset Scale and Data Integrity\n## Feature Selection for Temperature","[{\"question\":\"What problem does aiION address in geothermal exploration?\",\"answer\":\"It targets the difficulty of identifying promising geothermal reservoirs early by using geochemical data and machine learning to improve reservoir temperature prediction beyond traditional expert-dependent approaches.\"},{\"question\":\"What geochemical data sources and features does the method use?\",\"answer\":\"It uses groundwater sample geochemical measurements and selects key temperature-influencing features such as potassium, sodium, magnesium, calcium, chloride, fluorine, silica, and pH.\"},{\"question\":\"How is data quality handled before model training?\",\"answer\":\"A charge balance error calculation is applied, rejecting samples outside an acceptable range of ±5% to maintain reliability of the inputs.\"}]","aiION - A Machine Learning Approach to Geothermal Exploration - Insight & Methodology | 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