{"id":"https://openalex.org/W4312646908","doi":"https://doi.org/10.1109/igarss46834.2022.9884853","title":"Multi-Task Deep Learning Seismic Impedance Inversion Optimization Based on Homoscedastic Uncertainty","display_name":"Multi-Task Deep Learning Seismic Impedance Inversion Optimization Based on Homoscedastic Uncertainty","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312646908","doi":"https://doi.org/10.1109/igarss46834.2022.9884853"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884853","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884853","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","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/A5102636543","display_name":"Xiu Zheng","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":"Xiu Zheng","raw_affiliation_strings":["School of Mathematics and Statistics, Xi&#x2019; an Jiaotong University,Xi&#x0027;an,Shaanxi,China,710049"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi&#x2019; an Jiaotong University,Xi&#x0027;an,Shaanxi,China,710049","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005327204","display_name":"Bangyu Wu","orcid":"https://orcid.org/0000-0001-9998-9071"},"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":"Bangyu Wu","raw_affiliation_strings":["School of Mathematics and Statistics, Xi&#x2019; an Jiaotong University,Xi&#x0027;an,Shaanxi,China,710049"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi&#x2019; an Jiaotong University,Xi&#x0027;an,Shaanxi,China,710049","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064534354","display_name":"Xu Zhu","orcid":"https://orcid.org/0000-0003-1287-1623"},"institutions":[{"id":"https://openalex.org/I2802497816","display_name":"Chinese Academy of Geological Sciences","ror":"https://ror.org/02gp4e279","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802497816"]},{"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":"Xu Zhu","raw_affiliation_strings":["Institute of Geology, Chinese Academy of Geological Sciences,Beijing,China,100037","School of Mathematics and Statistics, Xi&#x2019; an Jiaotong University,Xi&#x0027;an,Shaanxi,China,710049"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geology, Chinese Academy of Geological Sciences,Beijing,China,100037","institution_ids":["https://openalex.org/I2802497816"]},{"raw_affiliation_string":"School of Mathematics and Statistics, Xi&#x2019; an Jiaotong University,Xi&#x0027;an,Shaanxi,China,710049","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071589516","display_name":"Xiaosan Zhu","orcid":"https://orcid.org/0000-0002-7372-0477"},"institutions":[{"id":"https://openalex.org/I2802497816","display_name":"Chinese Academy of Geological Sciences","ror":"https://ror.org/02gp4e279","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802497816"]},{"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":"Xiaosan Zhu","raw_affiliation_strings":["Institute of Geology, Chinese Academy of Geological Sciences,Beijing,China,100037","School of Mathematics and Statistics, Xi&#x2019; an Jiaotong University,Xi&#x0027;an,Shaanxi,China,710049"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geology, Chinese Academy of Geological Sciences,Beijing,China,100037","institution_ids":["https://openalex.org/I2802497816"]},{"raw_affiliation_string":"School of Mathematics and Statistics, Xi&#x2019; an Jiaotong University,Xi&#x0027;an,Shaanxi,China,710049","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9136,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.86378309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"22","issue":null,"first_page":"6149","last_page":"6152"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T10892","display_name":"Drilling and Well Engineering","score":0.9995999932289124,"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/T10635","display_name":"Hydraulic Fracturing and Reservoir Analysis","score":0.9984999895095825,"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/homoscedasticity","display_name":"Homoscedasticity","score":0.7609118819236755},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6938762068748474},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.6598948240280151},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5335870981216431},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49392157793045044},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4838813543319702},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.4644840359687805},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4575355350971222},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4193428158760071},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3995608687400818},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.34066253900527954},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.20626991987228394},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14072269201278687},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12875142693519592},{"id":"https://openalex.org/keywords/heteroscedasticity","display_name":"Heteroscedasticity","score":0.12464737892150879},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.12378087639808655}],"concepts":[{"id":"https://openalex.org/C104409967","wikidata":"https://www.wikidata.org/wiki/Q1054836","display_name":"Homoscedasticity","level":3,"score":0.7609118819236755},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6938762068748474},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.6598948240280151},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5335870981216431},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49392157793045044},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4838813543319702},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.4644840359687805},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4575355350971222},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4193428158760071},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3995608687400818},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.34066253900527954},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.20626991987228394},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14072269201278687},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12875142693519592},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.12464737892150879},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.12378087639808655},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9884853","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884853","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2015140204","https://openalex.org/W2136922672","https://openalex.org/W2251324968","https://openalex.org/W2624871570","https://openalex.org/W2742079690","https://openalex.org/W2891890374","https://openalex.org/W2947704004","https://openalex.org/W2966977996","https://openalex.org/W2968094316","https://openalex.org/W2999581854","https://openalex.org/W3033276719","https://openalex.org/W3091325523","https://openalex.org/W3124259473","https://openalex.org/W3131047970","https://openalex.org/W3134256030","https://openalex.org/W3180658890","https://openalex.org/W6739365718","https://openalex.org/W6742058293","https://openalex.org/W6790719756","https://openalex.org/W6798703447"],"related_works":["https://openalex.org/W2262753808","https://openalex.org/W2388528498","https://openalex.org/W956967588","https://openalex.org/W2119309329","https://openalex.org/W3094460295","https://openalex.org/W2894129938","https://openalex.org/W2380616114","https://openalex.org/W2322382546","https://openalex.org/W2109239032","https://openalex.org/W205123838"],"abstract_inverted_index":{"Seismic":[0],"inversion":[1,36,194],"is":[2,41,131,150,168,188],"a":[3,54,77,158,182],"process":[4],"to":[5,42,64,76,90,170,190],"obtain":[6],"the":[7,44,67,99,104,108,112,115,119,126,135,139,148,165,172,175,204,212,218,223],"spatial":[8],"structure":[9],"and":[10,29,49,144,153,181,195,226],"physical":[11],"properties":[12,51],"of":[13,34,69,107,114,122,128,141,147,164,174,207,222],"underground":[14],"rock":[15,50],"formations":[16],"by":[17,24,52],"using":[18,57],"surface":[19],"acquired":[20],"seismic":[21,35,47,192,196],"data,":[22],"constrained":[23],"known":[25],"geological":[26],"laws,":[27],"drilling":[28],"logging":[30,58,70],"data.":[31,124],"The":[32,200],"principle":[33],"based":[37,160],"on":[38,118,134,161,203],"deep":[39],"learning":[40,84,130],"learn":[43],"mapping":[45],"between":[46],"data":[48,59,197],"training":[53],"neural":[55],"network":[56,186],"as":[60],"labels.":[61],"However,":[62,125],"due":[63],"high":[65],"cost,":[66],"number":[68],"curves":[71],"are":[72],"often":[73,151],"limited,":[74],"leading":[75],"trained":[78],"model":[79,167,209],"with":[80,229],"poor":[81],"generalization.":[82],"Multi-task":[83],"(MTL)":[85],"provides":[86],"an":[87],"effective":[88],"way":[89],"mitigate":[91],"this":[92,156],"problem.":[93],"Learning":[94],"multiple":[95,179],"related":[96],"tasks":[97],"at":[98],"same":[100,120],"time":[101],"can":[102,215],"improve":[103],"generalization":[105],"ability":[106],"model,":[109],"thereby":[110],"improving":[111],"performance":[113,127],"main":[116],"task":[117],"amount":[121],"labeled":[123],"multi-task":[129],"highly":[132],"dependent":[133],"relative":[136],"weights":[137,149,173],"for":[138,178],"loss":[140,176],"each":[142],"task,":[143],"manual":[145],"tuning":[146],"time-consuming":[152],"laborious.":[154],"In":[155],"paper,":[157],"method":[159,214],"homoscedastic":[162],"uncertainty":[163],"Bayesian":[166],"used":[169,189],"balance":[171],"function":[177],"tasks,":[180,225],"Fully":[183],"convolutional":[184],"residual":[185],"(FCRN)":[187],"achieve":[191],"impedance":[193,228],"reconstruction":[198],"simultaneously.":[199],"test":[201],"results":[202],"synthetic":[205],"dataset":[206],"Marmousi2":[208],"show":[210],"that":[211],"proposed":[213],"automatically":[216],"determine":[217],"approximate":[219],"optimal":[220],"weight":[221],"two":[224],"predicts":[227],"higher":[230],"accuracy":[231],"than":[232],"single-task":[233],"FCRN":[234],"model.":[235]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
