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With the continuous development of machine learning, geophysical inversion methods based on deep learning have achieved good results. Geophysical inversion methods based on deep learning often employ large-scale data sets to obtain inversion networks with strong generalization. They are widely used but face a problem of lacking information constraints. Therefore, a self-constrained network is proposed to optimize the inversion results, composed of two networks with similar structures but different functions. At the same time, a fine-tuning strategy is also introduced. On the basis of data-driven deep learning, we further optimized the results by controlling the self-constrained network and optimizing fine-tuning strategy. The results of model testing show that the method proposed in this study can effectively improve inversion precision and obtain more reliable and accurate inversion results. Finally, the method is applied to the field data of Gonghe Basin, Qinghai Province, and the 3D inversion results are used to effectively delineate the geothermal storage area.<\/jats:p>","DOI":"10.3390\/rs16060995","type":"journal-article","created":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T12:17:16Z","timestamp":1710245836000},"page":"995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Deep Learning Gravity Inversion Method Based on a Self-Constrained Network and Its Application"],"prefix":"10.3390","volume":"16","author":[{"given":"Shuai","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyu","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangrui","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart Earth, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Smart Earth, Dalian 116023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Jiao","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"G37","DOI":"10.1190\/geo2013-0393.1","article-title":"3D inversion of airborne gravity-gradiometry data using cokriging","volume":"79","author":"Geng","year":"2014","journal-title":"Geophysics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1190\/1.1444302","article-title":"3-D inversion of gravity data","volume":"63","author":"Li","year":"1998","journal-title":"Geophysics"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s00531-005-0471-6","article-title":"Using a genetic algorithm for 3-D inversion of gravity data in Fuerteventura (Canary Islands)","volume":"94","author":"Montesinos","year":"2005","journal-title":"Int. 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