[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125159-en":3,"doc-seo-125159-105":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},125159,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predicting the Geothermal Gradient in Colombia - a Machine Learning Approach","Accurately determining the geothermal gradient is essential for evaluating geothermal energy potential, yet large parts of Colombia lack direct gradient measurements. This study presents a data-driven machine learning workflow that estimates the geothermal gradient using only global-scale geophysical datasets and low-resolution geological knowledge. A Gradient-Boosted Regression Tree model provides optimal results, validated extensively with prediction errors within 12%. The work also generates a geothermal gradient map with values from 16.75 to 41.20 °C/km, consistent with geological geothermal indicators and independent regional findings.","arXiv :2404 .05184v7 [physics .geo-ph] 5 Jun 2024  \nPredicting the Geothermal Gradient in Colombia: a Machine Learning Approach  \nJuan C. Mej´ıa-Fragoso 1*, Manuel A. Fl´orez 1 and  \nRoc´ıo Bernal-Olaya 1  \n1 Universidad Industrial de Santander, Carrera 27 Calle 9, Bucaramanga,  \n680002, Santander, Colombia.  \n*Corresponding author(s). E-mail(s): [juan.mejia3@correo.uis.edu.co](juan.mejia3@correo.uis.edu.co) ; Contributing authors: [maflotor@uis.edu.co](maflotor@uis.edu.co) ; [rbernalo@uis.edu.co](rbernalo@uis.edu.co) ;  \nAbstract  \nAccurately determining the geothermal gradient is crucial for assessing geothermal energy potential. In Colombia, despite an abundance of theoretical geothermal resources, large regions of the country lack gradient measurements. This study introduces a machine learning approach to estimate the geothermal gradient in regions where only global-scale geophysical datasets and course geological knowledge are available. We find that a Gradient-Boosted Regression Tree algorithm yields optimal predictions and extensively validates the trained model, obtaining predictions of our model within 12% accuracy. Finally, we present a geothermal gradient map of Colombia that serve as an indicator of potential regions for further exploration and data collection. This map displays gradient values ranging from 16.75 to 41.20°C/km and shows significant agreement with geological indicators of geothermal activity, such as faults and thermal manifestations. Additionally, our results are consistent with independent findings from other researchers in specific regions, which supports the reliability of our approach.  \nKeywords: Geothermal Energy, Machine Learning, Colombia, Geothermal Gradient, Prediction  \n1  \n1 Highlights  \n• A machine learning approach for geothermal gradient prediction using only globalscale geophysical datasets and low-resolution geological maps.  \n• Model predictions show good agreement with independent measurements and high accuracy.  \n• The model allows for the generation of a new geothermal gradient map of Colombia highlighting the potential of unexplored regions.  \n• Model predictions follow regional trends established by prior geological knowledge.  \n• Model predictions confirm high geothermal gradients in the Amazon, an unexplored region, giving a baseline for studying the area.  \n2 Introduction  \nThe tectonic and geological characteristics of Colombia give rise to thermal anomalies and regions exhibiting elevated geothermal gradients, which represent promising sources of geothermal energy. However, performing accurate estimations of the country’s potential for geothermal energy generation remains challenging. The most current geothermal gradient map for Colombia covers roughly half of the country’s territory (Alfaro et al., 2009), for the rest no systematic determinations of the geothermal gradient are available. Some studies have tried to address this issue. Uyeda and Watanabe (1970) observed normal to subnormal geothermal gradient values across South America, with higher values in the Andes related to geothermal activity. Bachu et al. (1995) focused on the Llanos Basin, finding that geothermal gradients decrease with depth and westward. In northwestern Colombia, Quintero et al. (2019) estimated geothermal gradients using grid systems and aeromagnetic data. Matiz-Le´on (2023) emphasized the importance of statistical methods and spatial prediction for estimating geothermal gradients in the Llanos Basin, especially in areas lacking in-situ data. Unfortunately, all of these studies are either focused on narrow areas of interest or scales that are too large for meaningful and detailed geothermal energy estimations. Measurements from boreholes drilled by the oil and gas industry provide the best direct constraint on the geothermal gradient; in the case of Colombia, they are either very sparse or clustered to specific regions, as the need to decarbonize the economy grows, it is unlikely that the past","cbCairLqE2RDF1uR","https://ap.wps.com/l/cbCairLqE2RDF1uR","pdf",6440316,1,35,"English","en",105,"# Abstract\n# Highlights\n# Introduction","[{\"question\":\"Why is geothermal gradient prediction important in Colombia?\",\"answer\":\"Geothermal gradient estimates are crucial for assessing geothermal energy potential, but many Colombian regions lack systematic gradient measurements despite abundant theoretical resources.\"},{\"question\":\"What data does the machine learning approach rely on?\",\"answer\":\"The method uses global-scale geophysical datasets and course geological knowledge where only limited local measurements are available.\"},{\"question\":\"Which model performs best and how accurate is it?\",\"answer\":\"A Gradient-Boosted Regression Tree yields optimal predictions and, after extensive validation, achieves predictions within about 12% accuracy.\"}]","Predicting the Geothermal Gradient in Colombia - 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