{"id":"https://openalex.org/W4381163608","doi":"https://doi.org/10.1109/lgrs.2023.3287553","title":"Three-Dimensional Initial Salt Body Building for Full-Waveform Inversion Assisted by Deep Learning","display_name":"Three-Dimensional Initial Salt Body Building for Full-Waveform Inversion Assisted by Deep Learning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4381163608","doi":"https://doi.org/10.1109/lgrs.2023.3287553"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2023.3287553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3287553","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063538206","display_name":"C. P\u00e9rez","orcid":"https://orcid.org/0000-0002-0480-0369"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Carlos H. Bastidas P\u00e9rez","raw_affiliation_strings":["National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum-Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0480-0369","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum-Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014653096","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0001-9786-2093"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum-Beijing, Beijing, China","School of Petroleum, China University of Petroleum-Beijing, Karamay, Xinjiang, China"],"raw_orcid":"https://orcid.org/0000-0001-9786-2093","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum-Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293"]},{"raw_affiliation_string":"School of Petroleum, China University of Petroleum-Beijing, Karamay, Xinjiang, China","institution_ids":["https://openalex.org/I204553293"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.0477,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72166866,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"20","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","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/T10271","display_name":"Seismic Imaging and Inversion Techniques","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":0.9991000294685364,"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/T13067","display_name":"Geological Modeling and Analysis","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"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/computer-science","display_name":"Computer science","score":0.6521129012107849},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.6032789945602417},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5806615948677063},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5081558227539062},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.45731163024902344},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4434199035167694},{"id":"https://openalex.org/keywords/model-building","display_name":"Model building","score":0.4323641359806061},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4248828887939453},{"id":"https://openalex.org/keywords/salt","display_name":"Salt (chemistry)","score":0.4232960045337677},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3821484446525574},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3454691767692566},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.14103630185127258}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6521129012107849},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.6032789945602417},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5806615948677063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5081558227539062},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.45731163024902344},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4434199035167694},{"id":"https://openalex.org/C189474733","wikidata":"https://www.wikidata.org/wiki/Q917912","display_name":"Model building","level":2,"score":0.4323641359806061},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4248828887939453},{"id":"https://openalex.org/C2776371256","wikidata":"https://www.wikidata.org/wiki/Q12370","display_name":"Salt (chemistry)","level":2,"score":0.4232960045337677},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3821484446525574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3454691767692566},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.14103630185127258},{"id":"https://openalex.org/C147789679","wikidata":"https://www.wikidata.org/wiki/Q11372","display_name":"Physical chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2023.3287553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3287553","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3623185241","display_name":null,"funder_award_id":"2017GXZ025938","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G6566515563","display_name":null,"funder_award_id":"42274147","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1971588070","https://openalex.org/W1987869189","https://openalex.org/W2001412060","https://openalex.org/W2292089642","https://openalex.org/W2507486451","https://openalex.org/W2616386424","https://openalex.org/W2617132647","https://openalex.org/W2912052494","https://openalex.org/W2912818797","https://openalex.org/W2933456541","https://openalex.org/W3008181685","https://openalex.org/W3090979856","https://openalex.org/W4206567543","https://openalex.org/W4220934948","https://openalex.org/W4291755194","https://openalex.org/W4296546761","https://openalex.org/W4362704989","https://openalex.org/W4378229643","https://openalex.org/W6639824700","https://openalex.org/W6851473554"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W1980470275","https://openalex.org/W2079613018","https://openalex.org/W2731899572","https://openalex.org/W4304166257","https://openalex.org/W2081037766","https://openalex.org/W2344971351","https://openalex.org/W4230611425","https://openalex.org/W4294635752"],"abstract_inverted_index":{"Salt":[0],"bodies":[1,40,123],"represent":[2],"a":[3,73,100,134,158],"significant":[4],"challenge":[5],"when":[6,15],"performing":[7,179],"full-waveform":[8],"inversion":[9],"(FWI)":[10],"due":[11],"to":[12,30,44,60,92,162],"convergence":[13],"issues":[14],"retrieving":[16],"high-contrast":[17],"geological":[18],"structures.":[19],"Recent":[20],"advances":[21],"in":[22],"FWI":[23],"promote":[24],"the":[25,35,39,46,55,77,128,146,151,164,173],"adoption":[26],"of":[27,49,57,172],"data":[28,82,184],"regularization":[29],"address":[31],"this":[32,62,96],"problem.":[33],"However,":[34],"model":[36,161,166,175],"complexity":[37],"where":[38,181],"are":[41,87,185],"enclosed":[42],"tends":[43],"be":[45],"leading":[47],"cause":[48],"unsatisfactory":[50],"results.":[51,189],"In":[52,95],"recent":[53],"years,":[54],"implementation":[56],"deep":[58,113,147],"learning":[59],"tackle":[61],"issue":[63],"has":[64],"been":[65],"proposed;":[66],"however,":[67],"these":[68],"solutions":[69],"generally":[70],"rely":[71],"on":[72,124],"direct":[74],"mapping":[75],"between":[76],"input":[78],"(gathers)":[79],"and":[80,102],"output":[81],"(2D":[83],"salt":[84,108,122,131,137,153],"bodies)":[85],"that":[86],"usually":[88],"builtusing":[89],"large":[90],"datasets":[91],"become":[93],"effective.":[94],"study,":[97],"we":[98],"propose":[99],"simple":[101],"robust":[103],"methodology":[104],"for":[105,168],"3D":[106,125,130,135,152],"initial":[107,136,165,174],"body":[109,138,154],"building":[110,167],"assisted":[111],"by":[112,178],"learning.":[114],"Two":[115],"pre-trained":[116],"models":[117],"identify":[118],"reflections":[119],"associated":[120],"with":[121,142],"shot":[126],"gathersfrom":[127],"SEG/EAGE":[129],"model.":[132],"Then,":[133],"is":[139,155,176],"outlinedand":[140],"built":[141],"RTM":[143],"using":[144],"only":[145],"learning-identified":[148],"reflections.":[149],"Finally,":[150],"embedded":[156],"into":[157],"background":[159],"velocity":[160],"conclude":[163],"FWI.":[169],"The":[170],"accuracy":[171],"tested":[177],"FWI,":[180],"observedand":[182],"modeled":[183],"compared,":[186],"showing":[187],"satisfactory":[188]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}