{"id":"https://openalex.org/W4390175104","doi":"https://doi.org/10.3390/rs16010062","title":"Physics-Informed Deep Learning Inversion with Application to Noisy Magnetotelluric Measurements","display_name":"Physics-Informed Deep Learning Inversion with Application to Noisy Magnetotelluric Measurements","publication_year":2023,"publication_date":"2023-12-22","ids":{"openalex":"https://openalex.org/W4390175104","doi":"https://doi.org/10.3390/rs16010062"},"language":"en","primary_location":{"id":"doi:10.3390/rs16010062","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16010062","pdf_url":"https://www.mdpi.com/2072-4292/16/1/62/pdf?version=1703257854","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/16/1/62/pdf?version=1703257854","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5058315243","display_name":"Wei Liu","orcid":"https://orcid.org/0000-0003-1541-4153"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Central South University, Changsha 410083, China","Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), Central South University, Changsha 410083, China","School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"],"affiliations":[{"raw_affiliation_string":"Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Central South University, Changsha 410083, China","institution_ids":["https://openalex.org/I139660479"]},{"raw_affiliation_string":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), Central South University, Changsha 410083, China","institution_ids":["https://openalex.org/I139660479"]},{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100810379","display_name":"He Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"He Wang","raw_affiliation_strings":["Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Central South University, Changsha 410083, China","Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), Central South University, Changsha 410083, China","School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"],"affiliations":[{"raw_affiliation_string":"Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Central South University, Changsha 410083, China","institution_ids":["https://openalex.org/I139660479"]},{"raw_affiliation_string":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), Central South University, Changsha 410083, China","institution_ids":["https://openalex.org/I139660479"]},{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114195592","display_name":"Zhenzhu Xi","orcid":"https://orcid.org/0000-0001-8239-9489"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhenzhu Xi","raw_affiliation_strings":["Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Central South University, Changsha 410083, China","Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), Central South University, Changsha 410083, China","School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"],"affiliations":[{"raw_affiliation_string":"Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, Central South University, Changsha 410083, China","institution_ids":["https://openalex.org/I139660479"]},{"raw_affiliation_string":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Ministry of Education), Central South University, Changsha 410083, China","institution_ids":["https://openalex.org/I139660479"]},{"raw_affiliation_string":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115602506","display_name":"Liang Wang","orcid":"https://orcid.org/0000-0001-5224-8647"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang Wang","raw_affiliation_strings":["Hunan 5D Geosciences Co., Ltd., Changsha 410083, China"],"affiliations":[{"raw_affiliation_string":"Hunan 5D Geosciences Co., Ltd., Changsha 410083, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5114195592"],"corresponding_institution_ids":["https://openalex.org/I139660479"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":2.221,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.87096876,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"16","issue":"1","first_page":"62","last_page":"62"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10572","display_name":"Geophysical and Geoelectrical Methods","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10572","display_name":"Geophysical and Geoelectrical Methods","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11757","display_name":"Seismic Waves and Analysis","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.720209538936615},{"id":"https://openalex.org/keywords/magnetotellurics","display_name":"Magnetotellurics","score":0.6868114471435547},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.655464768409729},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5843476057052612},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5008420944213867},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4956780672073364},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4936579167842865},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4511689841747284},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44461941719055176},{"id":"https://openalex.org/keywords/occam","display_name":"occam","score":0.43309032917022705},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36109232902526855},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.15766441822052002},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.10737118124961853},{"id":"https://openalex.org/keywords/electrical-resistivity-and-conductivity","display_name":"Electrical resistivity and conductivity","score":0.0968865156173706},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.08836409449577332},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07566580176353455}],"concepts":[{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.720209538936615},{"id":"https://openalex.org/C112313211","wikidata":"https://www.wikidata.org/wiki/Q1413106","display_name":"Magnetotellurics","level":3,"score":0.6868114471435547},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.655464768409729},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5843476057052612},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5008420944213867},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4956780672073364},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4936579167842865},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4511689841747284},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44461941719055176},{"id":"https://openalex.org/C78469957","wikidata":"https://www.wikidata.org/wiki/Q838062","display_name":"occam","level":2,"score":0.43309032917022705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36109232902526855},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.15766441822052002},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.10737118124961853},{"id":"https://openalex.org/C69990965","wikidata":"https://www.wikidata.org/wiki/Q65402698","display_name":"Electrical resistivity and conductivity","level":2,"score":0.0968865156173706},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.08836409449577332},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07566580176353455},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C77928131","wikidata":"https://www.wikidata.org/wiki/Q193343","display_name":"Tectonics","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/rs16010062","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16010062","pdf_url":"https://www.mdpi.com/2072-4292/16/1/62/pdf?version=1703257854","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9d739ddc83da4baaa03f1dac35f375b6","is_oa":true,"landing_page_url":"https://doaj.org/article/9d739ddc83da4baaa03f1dac35f375b6","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 16, Iss 1, p 62 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/rs16010062","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs16010062","pdf_url":"https://www.mdpi.com/2072-4292/16/1/62/pdf?version=1703257854","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.4099999964237213,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G8764071032","display_name":null,"funder_award_id":"2022YFC","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8823041833","display_name":null,"funder_award_id":"2022YFC2903404","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4390175104.pdf"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1972717105","https://openalex.org/W1973303079","https://openalex.org/W1975551669","https://openalex.org/W2013459156","https://openalex.org/W2016045532","https://openalex.org/W2018636632","https://openalex.org/W2057853719","https://openalex.org/W2086236003","https://openalex.org/W2098712263","https://openalex.org/W2108064160","https://openalex.org/W2108982325","https://openalex.org/W2109606373","https://openalex.org/W2133454905","https://openalex.org/W2391839529","https://openalex.org/W2511343645","https://openalex.org/W2776585113","https://openalex.org/W2789400040","https://openalex.org/W2906386705","https://openalex.org/W2968094316","https://openalex.org/W2980536111","https://openalex.org/W2983807332","https://openalex.org/W3003964717","https://openalex.org/W3015858546","https://openalex.org/W3041523127","https://openalex.org/W3093501093","https://openalex.org/W3131639001","https://openalex.org/W3138516171","https://openalex.org/W3195235709","https://openalex.org/W4224281859","https://openalex.org/W4283808523","https://openalex.org/W4288081040","https://openalex.org/W4307358091","https://openalex.org/W4309457030","https://openalex.org/W4312349930","https://openalex.org/W4362500580","https://openalex.org/W4385757733","https://openalex.org/W4386696841","https://openalex.org/W6687483927","https://openalex.org/W6773365314","https://openalex.org/W6846661870","https://openalex.org/W6855092420"],"related_works":["https://openalex.org/W2379471362","https://openalex.org/W2377084220","https://openalex.org/W2081467492","https://openalex.org/W2809693971","https://openalex.org/W2080106596","https://openalex.org/W2037493368","https://openalex.org/W2973208711","https://openalex.org/W2522016499","https://openalex.org/W3021715158","https://openalex.org/W2620971794"],"abstract_inverted_index":{"Despite":[0],"demonstrating":[1],"exceptional":[2],"inversion":[3,14,38,51,102,193,242],"production":[4],"for":[5,39,222],"synthetic":[6,106,184],"data,":[7],"the":[8,73,93,127,146,153,158,186,210,228,238,246],"application":[9],"of":[10,75,96,196,212],"deep":[11],"learning":[12],"(DL)":[13],"methods":[15],"to":[16,35,91,120,161,177,217],"invert":[17],"realistic":[18,40],"magnetotelluric":[19],"(MT)":[20],"measurements,":[21,42],"which":[22,170],"are":[23,199],"inevitably":[24],"contaminated":[25,200],"by":[26,53,201],"noise":[27,74,84,98,108,125,188,247],"in":[28,257],"acquisition,":[29],"poses":[30],"a":[31,47,61,114,163,171,207],"significant":[32],"challenge.":[33],"Hence,":[34],"facilitate":[36],"DL":[37,50,101,165,241,252],"MT":[41,49,100,259],"this":[43],"work":[44],"explores":[45],"developing":[46],"noise-robust":[48,240],"method":[52,243],"generating":[54],"targeted":[55],"noisy":[56],"training":[57,136,142],"datasets":[58],"and":[59,79,87,123,131,226,250],"constructing":[60],"physics-informed":[62,172,251],"neural":[63],"network.":[64],"Different":[65],"from":[66,126],"most":[67],"previous":[68],"works":[69],"that":[70,237],"only":[71],"considered":[72],"one":[76],"fixed":[77],"distribution":[78],"level,":[80],"we":[81,151],"propose":[82,113],"three":[83,213],"injection":[85,189,248],"strategies":[86,190,214,249],"compare":[88],"their":[89],"combinations":[90],"mitigate":[92],"adverse":[94],"effect":[95],"measurement":[97],"on":[99,145,169,245],"results:":[103],"(1)":[104],"add":[105],"relative":[107],"obeying":[109],"Gaussian":[110],"distribution;":[111],"(2)":[112],"multiwindow":[115],"Savitzky\u2013Golay":[116],"(MWSG)":[117],"filtering":[118],"scheme":[119],"extract":[121],"potential":[122],"possible":[124],"target":[128],"field":[129,208,260],"data":[130],"then":[132],"introduce":[133],"them":[134],"into":[135],"data;":[137],"(3)":[138],"create":[139],"an":[140],"augmented":[141],"dataset":[143],"based":[144,168,244],"former":[147],"two":[148],"strategies.":[149],"Moreover,":[150],"employ":[152],"powerful":[154],"Swin":[155],"Transformer":[156],"as":[157],"backbone":[159],"network":[160],"construct":[162],"U-shaped":[164],"model":[166],"(SwinTUNet),":[167],"SwinTUNet":[173],"(PISwinTUNet)":[174],"is":[175],"implemented":[176],"further":[178],"enhance":[179],"its":[180],"generalization":[181],"ability.":[182],"In":[183,206],"examples,":[185],"proposed":[187,239],"demonstrate":[191],"impressive":[192],"effects,":[194],"regardless":[195],"whether":[197],"they":[198],"familiar":[202],"or":[203],"unfamiliar":[204],"noise.":[205],"example,":[209],"combination":[211],"drives":[215],"PISwinTUNet":[216],"produce":[218],"considerably":[219],"faithful":[220],"reconstructions":[221],"subsurface":[223],"resistivity":[224],"structures":[225],"outperform":[227],"classical":[229],"deterministic":[230],"Occam":[231],"inversions.":[232],"The":[233],"experimental":[234],"results":[235],"show":[236],"architecture":[253],"holds":[254],"great":[255],"promise":[256],"processing":[258],"data.":[261]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}