{"id":"https://openalex.org/W4286906176","doi":"https://doi.org/10.1109/tgrs.2022.3196810","title":"MD Loss: Efficient Training of 3-D Seismic Fault Segmentation Network Under Sparse Labels by Weakening Anomaly Annotation","display_name":"MD Loss: Efficient Training of 3-D Seismic Fault Segmentation Network Under Sparse Labels by Weakening Anomaly Annotation","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4286906176","doi":"https://doi.org/10.1109/tgrs.2022.3196810"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3196810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3196810","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2110.05319","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007336490","display_name":"Yimin Dou","orcid":"https://orcid.org/0000-0001-9247-0832"},"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":"Yimin Dou","raw_affiliation_strings":["College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"],"raw_orcid":"https://orcid.org/0000-0001-9247-0832","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101966487","display_name":"Kewen Li","orcid":"https://orcid.org/0009-0001-7992-7233"},"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":"Kewen Li","raw_affiliation_strings":["College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China","institution_ids":["https://openalex.org/I4210162190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103345983","display_name":"Jianbing Zhu","orcid":null},"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":"Jianbing Zhu","raw_affiliation_strings":["Shengli Oilfield Company, SINOPEC, Dongying, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shengli Oilfield Company, SINOPEC, Dongying, China","institution_ids":["https://openalex.org/I106994412"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019385946","display_name":"Timing Li","orcid":"https://orcid.org/0000-0003-4928-895X"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Timing Li","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0003-4928-895X","affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062868387","display_name":"Shaoquan Tan","orcid":null},"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":"Shaoquan Tan","raw_affiliation_strings":["Shengli Oilfield Company, SINOPEC, Dongying, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shengli Oilfield Company, SINOPEC, Dongying, China","institution_ids":["https://openalex.org/I106994412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038508654","display_name":"Zongchao Huang","orcid":null},"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":"Zongchao Huang","raw_affiliation_strings":["College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, 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":5.2632,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.96860879,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"14"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9987999796867371,"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/T10892","display_name":"Drilling and Well Engineering","score":0.9952999949455261,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7742682695388794},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6154411435127258},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5571738481521606},{"id":"https://openalex.org/keywords/fault","display_name":"Fault (geology)","score":0.5281120538711548},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5153988599777222},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4909556210041046},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.47752252221107483},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41046571731567383}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7742682695388794},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6154411435127258},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5571738481521606},{"id":"https://openalex.org/C175551986","wikidata":"https://www.wikidata.org/wiki/Q47089","display_name":"Fault (geology)","level":2,"score":0.5281120538711548},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5153988599777222},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4909556210041046},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.47752252221107483},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41046571731567383},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2022.3196810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3196810","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2110.05319","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.05319","pdf_url":"https://arxiv.org/pdf/2110.05319","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2110.05319","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.05319","pdf_url":"https://arxiv.org/pdf/2110.05319","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7300000190734863,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G4074231672","display_name":null,"funder_award_id":"ZR2021MF082","funder_id":"https://openalex.org/F4320324174","funder_display_name":"Natural Science Foundation of Shandong Province"},{"id":"https://openalex.org/G6900237327","display_name":null,"funder_award_id":"51991365","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/F4320324174","display_name":"Natural Science Foundation of Shandong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2014758788","https://openalex.org/W2063660399","https://openalex.org/W2068840375","https://openalex.org/W2074419585","https://openalex.org/W2095867946","https://openalex.org/W2106921281","https://openalex.org/W2125362802","https://openalex.org/W2152225778","https://openalex.org/W2185926984","https://openalex.org/W2314961947","https://openalex.org/W2395611524","https://openalex.org/W2464708700","https://openalex.org/W2560311620","https://openalex.org/W2592421213","https://openalex.org/W2808760859","https://openalex.org/W2884436604","https://openalex.org/W2889638205","https://openalex.org/W2891111066","https://openalex.org/W2911424749","https://openalex.org/W2935135048","https://openalex.org/W2962914239","https://openalex.org/W2963787510","https://openalex.org/W2988868065","https://openalex.org/W3012353860","https://openalex.org/W3014641072","https://openalex.org/W3030907680","https://openalex.org/W3089369763","https://openalex.org/W3090580099","https://openalex.org/W3092382944","https://openalex.org/W3107905215","https://openalex.org/W3124259473","https://openalex.org/W3153344593","https://openalex.org/W3157386224","https://openalex.org/W3175100278","https://openalex.org/W3180982107","https://openalex.org/W3203094079","https://openalex.org/W3207293609","https://openalex.org/W3211037489","https://openalex.org/W4240104933","https://openalex.org/W4248461486","https://openalex.org/W4249013231","https://openalex.org/W4256579789","https://openalex.org/W6778679693","https://openalex.org/W6784784916"],"related_works":["https://openalex.org/W2055243143","https://openalex.org/W4321636575","https://openalex.org/W4379231730","https://openalex.org/W1986418932","https://openalex.org/W2357796999","https://openalex.org/W2045526782","https://openalex.org/W2741131631","https://openalex.org/W4389858081","https://openalex.org/W2156919374","https://openalex.org/W1984019423"],"abstract_inverted_index":{"Data-driven":[0],"fault":[1,28,65,92,104],"detection":[2,59,105],"has":[3,44],"been":[4],"regarded":[5],"as":[6],"a":[7,197],"3D":[8,27,64],"image":[9],"segmentation":[10,29,66],"task.":[11],"The":[12],"models":[13,183],"trained":[14],"from":[15],"synthetic":[16],"data":[17],"are":[18,109],"difficult":[19],"to":[20,37,54,58,62,112,137,146,184,189,195],"generalize":[21,188],"in":[22,173,224],"some":[23],"surveys.":[24,191],"Recently,":[25],"training":[26,55,80,174],"using":[30],"sparse":[31,69],"manual":[32,42],"2D":[33,70],"slices":[34],"is":[35,52,93,117,120,193,213],"thought":[36],"yield":[38],"promising":[39],"results,":[40],"but":[41],"labeling":[43],"many":[45],"false":[46,74,177],"negative":[47,75,178],"labels":[48,71,179],"(abnormal":[49],"annotations),":[50],"which":[51,119,141],"detrimental":[53],"and":[56,83,97,131,152,175,187,200,211],"consequently":[57],"performance.":[60],"Motivated":[61],"train":[63],"networks":[67],"under":[68],"while":[72],"suppressing":[73],"labels,":[76],"we":[77],"analyze":[78],"the":[79,85,91,123,143,168],"process":[81],"gradient":[82],"propose":[84],"Mask":[86],"Dice":[87],"(MD)":[88],"loss.":[89],"Moreover,":[90],"an":[94],"edge":[95,113,144],"feature,":[96],"current":[98],"encoder-decoder":[99],"architectures":[100],"widely":[101],"used":[102],"for":[103,122],"(e.g.,":[106],"U-shape":[107],"network)":[108],"not":[110],"conducive":[111],"representation.":[114],"Consequently,":[115],"Fault-Net":[116,192],"proposed,":[118],"designed":[121],"characteristics":[124],"of":[125,170,203],"faults,":[126,204],"employs":[127],"high-resolution":[128],"propagation":[129,151],"features,":[130],"embeds":[132],"Multi-Scale":[133],"Compression":[134],"Fusion":[135],"block":[136],"fuse":[138],"multi-scale":[139],"information,":[140],"allows":[142],"information":[145],"be":[147],"fully":[148],"preserved":[149],"during":[150],"fusion,":[153],"thus":[154],"enabling":[155,181],"advanced":[156],"performance":[157,186,223],"via":[158],"few":[159],"computational":[160,209],"resources.":[161],"Experimental":[162],"demonstrates":[163],"that":[164],"MD":[165],"loss":[166],"supports":[167],"inclusion":[169],"human":[171],"experience":[172],"suppresses":[176],"therein,":[180],"baseline":[182],"improve":[185],"more":[190,198],"capable":[194],"provide":[196],"stable":[199],"reliable":[201],"interpretation":[202],"it":[205],"uses":[206],"extremely":[207],"low":[208],"resources":[210],"inference":[212],"significantly":[214],"faster":[215],"than":[216],"other":[217],"models.":[218],"Our":[219],"method":[220],"indicates":[221],"optimal":[222],"comparison":[225],"with":[226],"several":[227],"mainstream":[228],"methods.":[229]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":7}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}