{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:13:05Z","timestamp":1764785585425,"version":"build-2065373602"},"reference-count":21,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["scijournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Energy Science &amp; Engineering"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>The geological structure of coal mines and the precise prediction of coal seam gas content are key factors in creating the transparent working face, and they also represent an important aspect of intelligent coal mining. The traditional technology of coal seam geological construction and gas content prediction is not advanced. This paper presents a methodology for 3D implicit geological modeling and visualization using Gempy and PyVista libraries, as well as gas prediction and distribution based on the Scikit\u2010learn library, all of which are underpinned by machine learning techniques. Under this method, the geological modeling of coal seam was converted to the kriging interpolation algorithm based on machine learning of coal seam thickness data. The problem of coal seam gas content is converted into a regression prediction problem of coal seam characteristic values and gas content target values based on machine learning. The pykrige package under Python is used to interpolate the obtained coal seam thickness. Based on the linear regression prediction model, loss function and other prediction methods and algorithms, the accurate prediction of coal seam gas content based on borehole data is realized. Under the above various operations, a 3D geological model of the mine and the gas content distribution map of the coal seam are finally obtained. Compared to actual borehole data and gas geological maps, this method offers high precision and enhanced efficiency.<\/jats:p>","DOI":"10.1002\/ese3.2048","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T04:18:20Z","timestamp":1740111500000},"page":"1117-1131","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Exploration of 3D Coal Seam Geological Modeling Visualization and Gas Content Prediction Technology Based on Borehole Data"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0910-3972","authenticated-orcid":false,"given":"Xiangfeng","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Safety Science and Engineering Henan Polytechnic University Jiaozuo Henan Province China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianxuan","family":"Hao","sequence":"additional","affiliation":[{"name":"College of Safety Science and Engineering Henan Polytechnic University Jiaozuo Henan Province China"},{"name":"State Collaborative Innovation Center of Coal Work Safety and Clean\u2010efficiency Utilization Henan Polytechnic University Jiaozuo Henan Province China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiyan","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Safety Science and Engineering Henan Polytechnic University Jiaozuo Henan Province China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Safety Science and Engineering Henan Polytechnic University Jiaozuo Henan Province China"},{"name":"State Key Laboratory Cultivation Base for Gas Geology and Gas Control Henan Polytechnic University Jiaozuo Henan Province China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Safety Science and Engineering Henan Polytechnic University Jiaozuo Henan Province China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"issue":"7","key":"e_1_2_10_2_1","first-page":"156","article-title":"The Concept, Architecture and Key Techniques of Transparent Work Surface","volume":"47","author":"Cunfei R. W.","year":"2019","journal-title":"Coal Science and Technology"},{"key":"e_1_2_10_3_1","unstructured":"Z.Mengbo Dynamic Construction of High\u2010Precision 3D Geological Model for Coal Mining Panel(China Coal Research Institute 2021)."},{"issue":"08","key":"e_1_2_10_4_1","first-page":"2285","article-title":"Echelon Construction of Geological Model of Coal Intelligent Precise Mining Working Face and its Key Technologies","volume":"44","author":"Cheng J.","year":"2019","journal-title":"Journal of China Coal Society"},{"key":"e_1_2_10_5_1","unstructured":"J. I.Guang Zhong Research on Imaging Methods and Application of Reflected In\u2010Seam Wave at the Roadway Lateral Wll of Coal Seam(China Coal Research Institute 2017)."},{"key":"e_1_2_10_6_1","unstructured":"H. E.Wenxin Three Dimensions In\u2010Seam Wave Field Simulation and Detection Method Research of Working Face Fault. China University of Mining and Technology(China University of Mining and Technology 2017)."},{"issue":"4","key":"e_1_2_10_7_1","first-page":"1","article-title":"Experimental Study on the Real\u2010Time Advance Detection System of Earthquake Following Excavation","volume":"49","author":"Ji W.","year":"2021","journal-title":"Coal Geology and Exploration"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2012.2207727"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11004-014-9540-3"},{"issue":"12","key":"e_1_2_10_10_1","first-page":"45","article-title":"Research of Spatial Interpolation Methods About the 3D\u2010Reconstruction of Geological Models","volume":"34","author":"Yan\u2010tao X.","year":"2006","journal-title":"Journal of Computer & Digital Engineering"},{"volume-title":"Application of Support Vector Regression in Prediction of Tectonic Coal Thickness","year":"2021","author":"Xin W. A. N. 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A.Lei Research on Gas Content Prediction Method Based on Improved Extreme Learning Machine(Henan Polytechnic University 2022)."},{"issue":"2","key":"e_1_2_10_13_1","first-page":"333","article-title":"Automatic Lithology Recognition and Classification Method Based on Deep Learning of Rock Images","volume":"34","author":"Ye Z.","year":"2018","journal-title":"Acta Petrologica Sinica"},{"issue":"2","key":"e_1_2_10_14_1","first-page":"330","article-title":"Research on Coal Seam Gas Content Prediction Based on Stacking Ensemble Model","volume":"34","author":"Lin W.","year":"2024","journal-title":"Coal Engineering"},{"issue":"01","key":"e_1_2_10_15_1","first-page":"114","article-title":"Research on Coal Seam Gas Content Prediction Method Based on GRA\u2010GASA\u2010SVM","volume":"43","author":"Shuicheng T.","year":"2024","journal-title":"J Coal Technology."},{"issue":"11","key":"e_1_2_10_16_1","first-page":"55","article-title":"Research on Coal Seam Gas Content Prediction Method Based on LSSA\u2010BP Neural Network","volume":"54","author":"Enke H.","year":"2023","journal-title":"Coal Mine Safety"},{"issue":"4","key":"e_1_2_10_17_1","first-page":"141","article-title":"Prediction of Coal Seam Gas Content Based on LM\u2010BP Neural Network","volume":"42","author":"Chunxiang H.","year":"2023","journal-title":"Coal Technology"},{"issue":"1","key":"e_1_2_10_18_1","first-page":"174","article-title":"Gas Content Prediction Model Based on ACSOA\u2010BP Neural Network","volume":"53","author":"Wei Z.","year":"2022","journal-title":"Coal Mine Safety"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.5194\/gmd-17-957-2024"},{"issue":"11","key":"e_1_2_10_20_1","first-page":"3173","article-title":"Machine Learning\uff0cDeep Learning and Python Language in Field of Geology","volume":"34","author":"Zhou Y. Z.","year":"2018","journal-title":"Acta Petrologica Sinica"},{"issue":"9","key":"e_1_2_10_21_1","first-page":"1337","article-title":"Machine Learning\u2010Based Implicit 3D Geological Modeling From Borehole Data","volume":"40","author":"Guo J.","year":"2019","journal-title":"Journal of Northeastern University (Natural Science Edition)"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.5518"}],"container-title":["Energy Science &amp; Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/scijournals.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ese3.2048","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T16:05:24Z","timestamp":1760544324000},"score":1,"resource":{"primary":{"URL":"https:\/\/scijournals.onlinelibrary.wiley.com\/doi\/10.1002\/ese3.2048"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,21]]},"references-count":21,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10.1002\/ese3.2048"],"URL":"https:\/\/doi.org\/10.1002\/ese3.2048","archive":["Portico"],"relation":{},"ISSN":["2050-0505","2050-0505"],"issn-type":[{"type":"print","value":"2050-0505"},{"type":"electronic","value":"2050-0505"}],"subject":[],"published":{"date-parts":[[2025,2,21]]},"assertion":[{"value":"2024-09-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}