{"id":"https://openalex.org/W6893321685","doi":"https://doi.org/10.5281/zenodo.15055846","title":"Enhanced 3D Gravity Inversion Using ResU-Net with Density Logging Constraints: A Dual-Phase Training Approach","display_name":"Enhanced 3D Gravity Inversion Using ResU-Net with Density Logging Constraints: A Dual-Phase Training Approach","publication_year":2025,"publication_date":"2025-03-20","ids":{"openalex":"https://openalex.org/W6893321685","doi":"https://doi.org/10.5281/zenodo.15055846"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.15055846","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.15055846","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.15055846","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Dong, Siyuan","orcid":"https://orcid.org/0000-0002-6897-0164"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dong, Siyuan","raw_affiliation_strings":["Xi'an Jiaotong University"],"raw_orcid":"https://orcid.org/0000-0002-6897-0164","affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Gao, Jinghuai","orcid":"https://orcid.org/0000-0002-0448-8555"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gao, Jinghuai","raw_affiliation_strings":["Xi'an Jiaotong University"],"raw_orcid":"https://orcid.org/0000-0002-0448-8555","affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhou, Shuai","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhou, Shuai","raw_affiliation_strings":["Jilin University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University","institution_ids":["https://openalex.org/I4210136497","https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wu, Baohai","orcid":null},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wu, Baohai","raw_affiliation_strings":["Xi'an Jiaotong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi'an Jiaotong University","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":null,"display_name":"Jia, Hongfa","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210136497","display_name":"Jilin Medical University","ror":"https://ror.org/03mzw7781","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136497"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia, Hongfa","raw_affiliation_strings":["Jilin University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jilin University","institution_ids":["https://openalex.org/I4210136497","https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.8532999753952026},{"id":"https://openalex.org/keywords/inverse-transform-sampling","display_name":"Inverse transform sampling","score":0.5875999927520752},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5672000050544739},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4074999988079071},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.4041999876499176},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.38760000467300415},{"id":"https://openalex.org/keywords/prior-information","display_name":"Prior information","score":0.37119999527931213},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.3711000084877014}],"concepts":[{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.8532999753952026},{"id":"https://openalex.org/C143606050","wikidata":"https://www.wikidata.org/wiki/Q1377019","display_name":"Inverse transform sampling","level":3,"score":0.5875999927520752},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5672000050544739},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.5049999952316284},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4609000086784363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40779998898506165},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4074999988079071},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C39267094","wikidata":"https://www.wikidata.org/wiki/Q7446968","display_name":"Seismic inversion","level":3,"score":0.31439998745918274},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.313400000333786},{"id":"https://openalex.org/C35817400","wikidata":"https://www.wikidata.org/wiki/Q2383566","display_name":"Well logging","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.30790001153945923},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.29429998993873596},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.2912999987602234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2816999852657318},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5281/zenodo.15055846","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.15055846","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"dataset"}],"best_oa_location":{"id":"doi:10.5281/zenodo.15055846","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.15055846","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Gravity":[0],"exploration":[1,74],"has":[2,186],"become":[3],"an":[4],"important":[5],"geophysical":[6],"method":[7,185,207,217],"due":[8],"to":[9,53,77,92,103,169,190,218,249],"its":[10],"low":[11],"cost":[12],"and":[13,61,70,108,159,178,203,230,260],"high":[14],"efficiency.":[15],"With":[16],"the":[17,65,94,104,112,120,123,132,144,162,167,171,180,191,209,219,223],"rise":[18],"of":[19,122,183,205],"artificial":[20],"intelligence,":[21],"data-driven":[22,193],"gravity":[23,236],"inversion":[24,54,66,129,134,181,195,211,237],"methods":[25,39,44,238],"based":[26,239],"on":[27,240],"deep":[28],"learning":[29],"(DL)":[30],"possess":[31],"physical":[32],"property":[33],"recovery":[34],"capabilities":[35],"that":[36,79],"conventional":[37],"regularization":[38],"lack.":[40],"However,":[41],"existing":[42],"DL":[43,194],"suffer":[45],"from":[46,72,222],"insufficient":[47],"prior":[48],"information":[49,165],"constraints,":[50],"which":[51],"leads":[52],"models":[55],"with":[56,131,208,233],"large":[57,155,247],"data":[58,138,221],"fitting":[59,139],"errors":[60],"unreliable":[62],"results.":[63],"Moreover,":[64],"results":[67,78],"lack":[68],"constraints":[69],"matching":[71],"other":[73],"methods,":[75],"leading":[76],"may":[80],"contradict":[81],"known":[82],"geological":[83],"conditions.In":[84],"this":[85,216,267],"paper,":[86],"we":[87,98,142,151,198,214],"propose":[88],"a":[89,100,154,201],"novel":[90],"approach":[91],"address":[93],"above":[95],"issues.":[96],"First,":[97],"introduce":[99],"depth-weighting":[101],"function":[102],"neural":[105],"network":[106,146,157,174],"(NN)":[107],"train":[109,153],"it":[110,232],"in":[111,228],"weighted":[113,124],"density":[114,163],"parameter":[115],"domain.":[116],"The":[117,270],"NN,":[118],"under":[119],"constraint":[121,168],"forward":[125],"operator,":[126],"demonstrates":[127],"improved":[128,188],"performance,":[130],"resulting":[133],"model":[135],"exhibiting":[136],"smaller":[137],"errors.":[140],"Next,":[141],"divide":[143],"entire":[145],"training":[147],"into":[148],"two":[149,234],"phases:":[150],"first":[152],"pre-trained":[156],"Net-I,":[158],"then":[160],"use":[161],"logging":[164],"as":[166,253],"get":[170],"optimized":[172],"fine-tuning":[173],"Net-II.":[175],"Through":[176],"testing":[177],"comparison,":[179],"quality":[182],"our":[184,206],"significantly":[187],"compared":[189],"unconstrained":[192],"method.":[196,212],"Additionally,":[197],"also":[199],"conduct":[200],"comparison":[202],"discussion":[204],"focusing":[210],"Finally,":[213],"apply":[215],"measured":[220],"San":[224],"Nicolas":[225],"mining":[226],"area":[227],"Mexico,":[229],"compare":[231],"recent":[235],"DL.":[241],"====================================================================================":[242],"We":[243],"have":[244],"uploaded":[245],"multiple":[246],"files":[248],"Quark":[250],"Cloud.":[251],"(such":[252],"\"Supported_Data\"":[254],"folder,":[255,257,259],"\"Net_Model\"":[256],"\"Tra&val.zip\"":[258],"\"Syn.rar\"":[261],"folder)":[262],"Please":[263],"download":[264],"them":[265],"through":[266],"link:":[268],"https://pan.quark.cn/s/7df7fb8635ce":[269],"extraction":[271],"code":[272],"is":[273],"evi6":[274]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}