{"id":"https://openalex.org/W4287888658","doi":"https://doi.org/10.1109/tgrs.2022.3193563","title":"Seismic Impedance Inversion Based on Residual Attention Network","display_name":"Seismic Impedance Inversion Based on Residual Attention Network","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4287888658","doi":"https://doi.org/10.1109/tgrs.2022.3193563"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3193563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3193563","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":["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/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":true,"raw_author_name":"Bangyu Wu","raw_affiliation_strings":["CGG GeoSoftware (Beijing), Beijing, China","School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China","an Jiaotong University, Xi&#x2019","an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0001-9998-9071","affiliations":[{"raw_affiliation_string":"CGG GeoSoftware (Beijing), Beijing, China","institution_ids":[]},{"raw_affiliation_string":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"an Jiaotong University, Xi&#x2019","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"an, Shaanxi, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089838523","display_name":"Qiao Xie","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":"Qiao Xie","raw_affiliation_strings":["School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China","an, Shaanxi, China","an Jiaotong University, Xi&#x2019"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"an, Shaanxi, China","institution_ids":[]},{"raw_affiliation_string":"an Jiaotong University, Xi&#x2019","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031697060","display_name":"Baohai Wu","orcid":"https://orcid.org/0000-0002-9884-6164"},"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":"Baohai Wu","raw_affiliation_strings":["CGG GeoSoftware (Beijing), Beijing, China","School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0002-9884-6164","affiliations":[{"raw_affiliation_string":"CGG GeoSoftware (Beijing), Beijing, China","institution_ids":[]},{"raw_affiliation_string":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, Shaanxi, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005327204"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":5.8058,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.97525703,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"17"},"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":0.9987000226974487,"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":0.9973000288009644,"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/computer-science","display_name":"Computer science","score":0.7628107666969299},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7284559607505798},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5495190620422363},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5087223649024963},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5057792663574219},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.46697425842285156},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44064390659332275},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4365580081939697},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3507370948791504},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3317607045173645},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.18515712022781372},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.10423919558525085}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7628107666969299},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7284559607505798},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5495190620422363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5087223649024963},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5057792663574219},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.46697425842285156},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44064390659332275},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4365580081939697},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3507370948791504},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3317607045173645},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.18515712022781372},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.10423919558525085},{"id":"https://openalex.org/C77928131","wikidata":"https://www.wikidata.org/wiki/Q193343","display_name":"Tectonics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3193563","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3193563","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"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4099999964237213,"id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G69488122","display_name":null,"funder_award_id":"11971377","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":61,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1902237438","https://openalex.org/W1999567494","https://openalex.org/W2013894622","https://openalex.org/W2050847261","https://openalex.org/W2133564696","https://openalex.org/W2194775991","https://openalex.org/W2302086703","https://openalex.org/W2328625672","https://openalex.org/W2333091651","https://openalex.org/W2395579298","https://openalex.org/W2400429454","https://openalex.org/W2406726851","https://openalex.org/W2549139847","https://openalex.org/W2618530766","https://openalex.org/W2752782242","https://openalex.org/W2894410771","https://openalex.org/W2899699175","https://openalex.org/W2922509574","https://openalex.org/W2935849356","https://openalex.org/W2947704004","https://openalex.org/W2952634764","https://openalex.org/W2962729168","https://openalex.org/W2963504571","https://openalex.org/W2963787510","https://openalex.org/W2963840672","https://openalex.org/W2964350391","https://openalex.org/W2968094316","https://openalex.org/W2999581854","https://openalex.org/W3042090478","https://openalex.org/W3081502560","https://openalex.org/W3131639001","https://openalex.org/W3134256030","https://openalex.org/W3135339134","https://openalex.org/W3140751667","https://openalex.org/W3157386224","https://openalex.org/W3161175789","https://openalex.org/W3164783667","https://openalex.org/W3175147126","https://openalex.org/W3180658890","https://openalex.org/W3180982107","https://openalex.org/W3187004614","https://openalex.org/W3196434791","https://openalex.org/W3203094079","https://openalex.org/W3203590351","https://openalex.org/W3205843994","https://openalex.org/W3207293609","https://openalex.org/W3213964222","https://openalex.org/W4210969899","https://openalex.org/W4246193833","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6638444622","https://openalex.org/W6638667902","https://openalex.org/W6679434410","https://openalex.org/W6682132143","https://openalex.org/W6682137061","https://openalex.org/W6696085341","https://openalex.org/W6754484989"],"related_works":["https://openalex.org/W2560215812","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3167935049","https://openalex.org/W2964954556","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Deep":[0],"learning":[1,79,199],"has":[2,80],"achieved":[3],"promising":[4],"results":[5,26,169],"for":[6,68,119,188],"impedance":[7,121,191,212],"inversion":[8],"via":[9],"seismic":[10,46,120,189],"data.":[11],"Generally,":[12],"these":[13],"networks,":[14],"composed":[15],"of":[16,37,45,90,228,237],"convolution":[17,151],"layers":[18],"and":[19,39,76,92,110,116,131,152,160,165,182,216],"residual":[20,108],"blocks,":[21],"tend":[22],"to":[23,59,155,203,210,234],"deliver":[24],"good":[25],"with":[27,107,213,221,250],"deep":[28,31,78],"architectures.":[29],"Nevertheless,":[30],"networks":[32,179],"accompany":[33],"a":[34,104],"large":[35],"number":[36],"parameters":[38],"longer":[40],"training":[41],"time.":[42],"The":[43,123,226],"volume":[44],"data,":[47],"especially":[48],"3D":[49],"scenarios,":[50],"is":[51,56,200,230],"very":[52],"large.":[53],"Therefore,":[54],"it":[55],"particularly":[57],"important":[58],"improve":[60,156,205],"the":[61,65,72,82,143,157,172,194,206,240],"accuracy":[62,181,227],"while":[63,185],"ensuring":[64,186],"model":[66,164],"efficiency":[67,159,187],"practical":[69],"implementation.":[70],"With":[71],"flourishing":[73],"new":[74],"modules":[75,109],"techniques,":[77],"set":[81],"state-of-the-art":[83],"in":[84,180,245,256],"many":[85],"applications":[86],"across":[87],"wide":[88],"range":[89],"scientific":[91],"engineering":[93],"disciplines.":[94],"In":[95],"this":[96],"paper,":[97],"we":[98],"present":[99],"Residual":[100],"Attention":[101],"Network":[102],"(ResANet),":[103],"CNN":[105],"incorporating":[106],"two":[111],"attention":[112,115],"mechanisms:":[113],"channel-wise":[114,133],"feature-map":[117],"attention,":[118],"inversion.":[122,192],"proposed":[124,173],"network":[125,174],"can":[126],"fuse":[127],"multi-scale":[128],"channel":[129],"information":[130],"recalibrate":[132],"feature":[134],"responses":[135],"as":[136,138],"well":[137,242],"receptive":[139],"fields":[140],"adaptively.":[141],"At":[142],"same":[144],"time,":[145],"ResANet":[146,208,229],"adopts":[147],"grouped":[148],"convolution,":[149],"dilated":[150],"dropout":[153],"techniques":[154],"computation":[158],"stability.":[161],"Marmousi2":[162],"synthetic":[163],"field":[166,195,246],"data":[167,190,196],"test":[168],"show":[170],"that":[171],"outperforms":[175],"several":[176],"comparable":[177],"neural":[178],"generalization":[183],"ability":[184],"For":[193],"test,":[197],"transfer":[198],"also":[201],"evoked":[202],"further":[204],"performance.":[207],"tends":[209],"predict":[211],"high":[214],"resolution":[215],"strong":[217],"lateral":[218],"continuity":[219],"compare":[220,249],"three":[222],"closely":[223],"related":[224],"networks.":[225],"improved":[231],"by":[232],"1":[233],"2":[235],"orders":[236],"magnitude":[238],"on":[239],"6":[241],"logs":[243],"provided":[244],"dataset":[247],"tests":[248],"commercial":[251],"software":[252],"(InverTrace":[253],"Plus":[254],"module":[255],"Jason)":[257],"using":[258],"Constrained":[259],"Sparse":[260],"Spike":[261],"Inversion":[262],"(CSSI)":[263],"method.":[264]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-18T08:16:58.900851","created_date":"2025-10-10T00:00:00"}
