{"id":"https://openalex.org/W3118882744","doi":"https://doi.org/10.1109/ssci47803.2020.9308446","title":"Inpainting Electrical Logging Images Based on Deep CNN with Attention Mechanisms","display_name":"Inpainting Electrical Logging Images Based on Deep CNN with Attention Mechanisms","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3118882744","doi":"https://doi.org/10.1109/ssci47803.2020.9308446","mag":"3118882744"},"language":"en","primary_location":{"id":"doi:10.1109/ssci47803.2020.9308446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci47803.2020.9308446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-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/A5033706827","display_name":"Chunyu Du","orcid":"https://orcid.org/0009-0006-0707-1241"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunyu Du","raw_affiliation_strings":["College of Control Science and Engineering, China University of Petroleum(East China), Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, China University of Petroleum(East China), Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020267535","display_name":"Qiang Xing","orcid":"https://orcid.org/0000-0001-9129-1146"},"institutions":[{"id":"https://openalex.org/I106994412","display_name":"Sinopec (China)","ror":"https://ror.org/0161q6d74","country_code":"CN","type":"company","lineage":["https://openalex.org/I106994412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Xing","raw_affiliation_strings":["Well Logging Company of Sinopec Shengli Petroleum Engineering Co., LTD, Dongying, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Well Logging Company of Sinopec Shengli Petroleum Engineering Co., LTD, Dongying, China","institution_ids":["https://openalex.org/I106994412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115590107","display_name":"Jinyan Zhang","orcid":"https://orcid.org/0009-0001-2001-1773"},"institutions":[{"id":"https://openalex.org/I106994412","display_name":"Sinopec (China)","ror":"https://ror.org/0161q6d74","country_code":"CN","type":"company","lineage":["https://openalex.org/I106994412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinyan Zhang","raw_affiliation_strings":["Well Logging Company of Sinopec Shengli Petroleum Engineering Co., LTD, Dongying, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Well Logging Company of Sinopec Shengli Petroleum Engineering Co., LTD, Dongying, China","institution_ids":["https://openalex.org/I106994412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100384874","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0003-1581-8369"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Wang","raw_affiliation_strings":["College of Control Science and Engineering, China University of Petroleum(East China), Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, China University of Petroleum(East China), Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091703383","display_name":"Baodi Liu","orcid":"https://orcid.org/0000-0002-1408-5514"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baodi Liu","raw_affiliation_strings":["College of Control Science and Engineering, China University of Petroleum(East China), Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, China University of Petroleum(East China), Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101722534","display_name":"Yanjiang Wang","orcid":"https://orcid.org/0000-0001-9910-7884"},"institutions":[{"id":"https://openalex.org/I4210162190","display_name":"China University of Petroleum, East China","ror":"https://ror.org/05gbn2817","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210162190"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanjiang Wang","raw_affiliation_strings":["College of Control Science and Engineering, China University of Petroleum(East China), Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Control Science and Engineering, China University of Petroleum(East China), Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0979,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.4386232,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"607","last_page":"610"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9962000250816345,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/blank","display_name":"Blank","score":0.7261306047439575},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7107825875282288},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6916525363922119},{"id":"https://openalex.org/keywords/inpainting","display_name":"Inpainting","score":0.6901803016662598},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6562711000442505},{"id":"https://openalex.org/keywords/borehole","display_name":"Borehole","score":0.5788058042526245},{"id":"https://openalex.org/keywords/logging","display_name":"Logging","score":0.5393208861351013},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5300984978675842},{"id":"https://openalex.org/keywords/strips","display_name":"STRIPS","score":0.5046340227127075},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5043643712997437},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46777310967445374},{"id":"https://openalex.org/keywords/well-logging","display_name":"Well logging","score":0.43403488397598267},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4302728772163391},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4202900528907776},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.3707866072654724},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.34637266397476196},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3093098998069763},{"id":"https://openalex.org/keywords/petroleum-engineering","display_name":"Petroleum engineering","score":0.1277824342250824},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11284348368644714}],"concepts":[{"id":"https://openalex.org/C2778089247","wikidata":"https://www.wikidata.org/wiki/Q368951","display_name":"Blank","level":2,"score":0.7261306047439575},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7107825875282288},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6916525363922119},{"id":"https://openalex.org/C11727466","wikidata":"https://www.wikidata.org/wiki/Q1628157","display_name":"Inpainting","level":3,"score":0.6901803016662598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6562711000442505},{"id":"https://openalex.org/C150560799","wikidata":"https://www.wikidata.org/wiki/Q502102","display_name":"Borehole","level":2,"score":0.5788058042526245},{"id":"https://openalex.org/C125620115","wikidata":"https://www.wikidata.org/wiki/Q845249","display_name":"Logging","level":2,"score":0.5393208861351013},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5300984978675842},{"id":"https://openalex.org/C200925200","wikidata":"https://www.wikidata.org/wiki/Q7624170","display_name":"STRIPS","level":2,"score":0.5046340227127075},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5043643712997437},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46777310967445374},{"id":"https://openalex.org/C35817400","wikidata":"https://www.wikidata.org/wiki/Q2383566","display_name":"Well logging","level":2,"score":0.43403488397598267},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4302728772163391},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4202900528907776},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3707866072654724},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.34637266397476196},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3093098998069763},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.1277824342250824},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11284348368644714},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci47803.2020.9308446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci47803.2020.9308446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W603908379","https://openalex.org/W1514535095","https://openalex.org/W1850742715","https://openalex.org/W1986785884","https://openalex.org/W2163605009","https://openalex.org/W2550553598","https://openalex.org/W2884585870","https://openalex.org/W2950178297","https://openalex.org/W2962741254","https://openalex.org/W2963420686","https://openalex.org/W6618372016"],"related_works":["https://openalex.org/W2365338673","https://openalex.org/W3141837860","https://openalex.org/W2056114932","https://openalex.org/W2378520239","https://openalex.org/W2030923182","https://openalex.org/W2355423019","https://openalex.org/W2388521818","https://openalex.org/W38497042","https://openalex.org/W2385227882","https://openalex.org/W2393030256"],"abstract_inverted_index":{"Microresistivity":[0],"imaging":[1,30],"logging":[2,31,57,128],"is":[3,41,61,94],"a":[4,117,144],"crucial":[5],"technique":[6],"for":[7,90,97],"evaluating":[8],"the":[9,21,24,28,36,56,65,70,73,101,110,123,137,160,164,172,178,183,196,199,209],"glutenite,":[10],"carbonate":[11],"and":[12,76,106,163,194],"other":[13],"complex":[14],"reservoirs":[15],"in":[16,27,55,85,112,126,177],"oil":[17],"exploration":[18],"field.":[19],"Because":[20],"size":[22,38],"of":[23,72,147,198],"electrode":[25],"pads":[26],"electric":[29],"instrument":[32],"can":[33,142],"not":[34],"match":[35],"borehole":[37,46],"well,":[39],"it":[40,60],"hard":[42],"to":[43,51,63,68,121,158,174,189],"get":[44],"full":[45],"covering":[47],"images,":[48],"which":[49,141,203],"leads":[50],"many":[52],"blank":[53,66,124],"strips":[54,67,125],"images.":[58],"Thus":[59],"necessary":[62],"fill":[64],"ensure":[69],"accuracy":[71],"subsequent":[74],"processing":[75],"interpretation.":[77],"The":[78,153,167],"existing":[79,210],"filling":[80,201,211],"methods":[81],"work":[82],"well":[83],"only":[84],"simple":[86],"formation":[87],"structures,":[88],"but":[89],"images":[91,129],"whose":[92],"structure":[93],"very":[95],"complex,":[96],"example,":[98],"glutenite":[99],"formation,":[100],"filled":[102],"regions":[103],"appear":[104],"blurred":[105],"indistinct.":[107],"To":[108],"remedy":[109],"drawbacks,":[111],"this":[113],"paper,":[114],"we":[115],"propose":[116],"machine":[118],"learning":[119],"method":[120],"inpaint":[122],"electrical":[127],"based":[130],"on":[131,171],"deep":[132],"convolutional":[133],"neural":[134],"network":[135],"with":[136],"attention":[138,154,162,169,185],"mechanisms":[139,155],"embedded,":[140],"capture":[143],"large":[145],"number":[146],"low-level":[148],"image":[149],"statistical":[150],"prior":[151],"information.":[152],"here":[156],"refer":[157],"both":[159],"channel":[161,168],"spatial":[165,184],"attention.":[166],"focuses":[170],"information":[173],"be":[175],"attended":[176],"intermediate":[179],"feature":[180],"maps":[181],"while":[182],"serves":[186],"determining":[187],"where":[188],"attend.":[190],"Extensive":[191],"experiments":[192],"validate":[193],"confirm":[195],"effectiveness":[197],"proposed":[200],"method,":[202],"produce":[204],"more":[205],"compelling":[206],"results":[207],"than":[208],"approaches.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}