{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T14:43:34Z","timestamp":1787496214526,"version":"build-2736575974"},"reference-count":31,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Garibaldi Geothermal Energy Project"},{"name":"Critical Mineral Geoscience Data (CMGD) program of Natural Resources Canada"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this study, we describe a deep learning (DL)-based workflow for the three-dimensional (3D) geophysical inversion of magnetotelluric (MT) data. We derived a mathematical connection between a 3D resistivity model and the surface-observed electric\/magnetic field response by using a fully connected neural network framework (U-Net). Limited by computer hardware functionality, the resistivity models were generated by using a random walk technique to enlarge the generalization coverage of the neural network model, and 15,000 paired datasets were utilized to train and validate it. Grid search was used to select the optimal configuration parameters. With the optimal model framework from the parameter tuning phase, the metrics showed stable convergence during model training\/validation. In the test period, the trained model was applied to predict the resistivity distribution by using both the simulated synthetic and the real MT data from the Mount Meager area, British Columbia. The reliability of the model prediction was verified with noised input data from the synthetic model. The calculated results can be used to reconstruct the position and shape trends of bodies with anomalous resistivity, which verifies the stability and performance of the DL-based 3D inversion algorithm and showcases its potential practical applications.<\/jats:p>","DOI":"10.3390\/rs16183400","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T03:03:37Z","timestamp":1726196617000},"page":"3400","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Estimating Three-Dimensional Resistivity Distribution with Magnetotelluric Data and a Deep Learning Algorithm"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6324-5932","authenticated-orcid":false,"given":"Xiaojun","family":"Liu","sequence":"first","affiliation":[{"name":"Natural Resources Canada, Geological Survey of Canada, Calgary, AB T2L 2A7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James A.","family":"Craven","sequence":"additional","affiliation":[{"name":"Natural Resources Canada, Geological Survey of Canada, Ottawa, ON K1A 0G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2400-0550","authenticated-orcid":false,"given":"Victoria","family":"Tschirhart","sequence":"additional","affiliation":[{"name":"Natural Resources Canada, Geological Survey of Canada, Ottawa, ON K1A 0G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen E.","family":"Grasby","sequence":"additional","affiliation":[{"name":"Natural Resources Canada, Geological Survey of Canada, Calgary, AB T2L 2A7, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"ref_1","unstructured":"Craven, J.A., Farquharson, C.G., Mackie, R.L., Siripunvaraporn, W., Tuncer, V., and Unsworth, M.J. 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