{"id":"https://openalex.org/W3042090478","doi":"https://doi.org/10.1109/tgrs.2019.2947149","title":"Poststack Seismic Data Denoising Based on 3-D Convolutional Neural Network","display_name":"Poststack Seismic Data Denoising Based on 3-D Convolutional Neural Network","publication_year":2019,"publication_date":"2019-11-06","ids":{"openalex":"https://openalex.org/W3042090478","doi":"https://doi.org/10.1109/tgrs.2019.2947149","mag":"3042090478"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2019.2947149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2947149","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/A5100638482","display_name":"Dawei Liu","orcid":"https://orcid.org/0000-0001-5553-2379"},"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":"Dawei Liu","raw_affiliation_strings":["School of Information and Communication Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China","School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100664990","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-8770-3862"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["InvestBrain, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"InvestBrain, Shanghai, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100666814","display_name":"Xiaokai Wang","orcid":"https://orcid.org/0000-0001-5396-985X"},"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":"Xiaokai Wang","raw_affiliation_strings":["School of Information and Communication Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China","School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-5396-985X","affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100416961","display_name":"Cheng Wang","orcid":"https://orcid.org/0000-0001-6075-796X"},"institutions":[{"id":"https://openalex.org/I4210105529","display_name":"Daqing Oilfield General Hospital","ror":"https://ror.org/01ey7we33","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210105529"]},{"id":"https://openalex.org/I4387155657","display_name":"Daqing Oilfield of CNPC","ror":"https://ror.org/03shhrx95","country_code":null,"type":"company","lineage":["https://openalex.org/I4387155657","https://openalex.org/I98227222"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cheng Wang","raw_affiliation_strings":["Exploration and Development Research Institute, Daqing Oilfield Company, Ltd., Daqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Exploration and Development Research Institute, Daqing Oilfield Company, Ltd., Daqing, China","institution_ids":["https://openalex.org/I4210105529","https://openalex.org/I4387155657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076188702","display_name":"Jiang-yun Pei","orcid":null},"institutions":[{"id":"https://openalex.org/I4210105529","display_name":"Daqing Oilfield General Hospital","ror":"https://ror.org/01ey7we33","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210105529"]},{"id":"https://openalex.org/I4387155657","display_name":"Daqing Oilfield of CNPC","ror":"https://ror.org/03shhrx95","country_code":null,"type":"company","lineage":["https://openalex.org/I4387155657","https://openalex.org/I98227222"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiangyun Pei","raw_affiliation_strings":["Exploration and Development Research Institute, Daqing Oilfield Company, Ltd., Daqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Exploration and Development Research Institute, Daqing Oilfield Company, Ltd., Daqing, China","institution_ids":["https://openalex.org/I4210105529","https://openalex.org/I4387155657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100651083","display_name":"Wenchao Chen","orcid":"https://orcid.org/0000-0002-4948-3037"},"institutions":[{"id":"https://openalex.org/I4210105529","display_name":"Daqing Oilfield General Hospital","ror":"https://ror.org/01ey7we33","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210105529"]},{"id":"https://openalex.org/I4387155657","display_name":"Daqing Oilfield of CNPC","ror":"https://ror.org/03shhrx95","country_code":null,"type":"company","lineage":["https://openalex.org/I4387155657","https://openalex.org/I98227222"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenchao Chen","raw_affiliation_strings":["Exploration and Development Research Institute, Daqing Oilfield Company, Ltd., Daqing, China"],"raw_orcid":"https://orcid.org/0000-0002-4948-3037","affiliations":[{"raw_affiliation_string":"Exploration and Development Research Institute, Daqing Oilfield Company, Ltd., Daqing, China","institution_ids":["https://openalex.org/I4210105529","https://openalex.org/I4387155657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100638482"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":10.1128,"has_fulltext":false,"cited_by_count":139,"citation_normalized_percentile":{"value":0.99145325,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"58","issue":"3","first_page":"1598","last_page":"1629"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9955000281333923,"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":0.9943000078201294,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6886637210845947},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.6703653335571289},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6669356226921082},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.612378716468811},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5824467539787292},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5796794891357422},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.47891294956207275},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4688761234283447},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.35949942469596863},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3347036838531494},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12407007813453674}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6886637210845947},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.6703653335571289},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6669356226921082},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.612378716468811},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5824467539787292},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5796794891357422},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47891294956207275},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4688761234283447},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.35949942469596863},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3347036838531494},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12407007813453674}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2019.2947149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2019.2947149","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":[{"score":0.5099999904632568,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[{"id":"https://openalex.org/G2043061748","display_name":null,"funder_award_id":"41974131","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3058776780","display_name":null,"funder_award_id":"41504093","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6997018651","display_name":null,"funder_award_id":"41774135","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7790814990","display_name":null,"funder_award_id":"xjj2016065","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G8973800200","display_name":null,"funder_award_id":"U1663207","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/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":84,"referenced_works":["https://openalex.org/W140590254","https://openalex.org/W274818618","https://openalex.org/W1557106888","https://openalex.org/W1570134156","https://openalex.org/W1584393767","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1836465849","https://openalex.org/W1964141793","https://openalex.org/W1979116456","https://openalex.org/W1984719437","https://openalex.org/W1992129416","https://openalex.org/W1996070960","https://openalex.org/W1996098514","https://openalex.org/W1997837299","https://openalex.org/W2013913026","https://openalex.org/W2021117681","https://openalex.org/W2025886468","https://openalex.org/W2026967674","https://openalex.org/W2029344852","https://openalex.org/W2037642501","https://openalex.org/W2050321860","https://openalex.org/W2058632668","https://openalex.org/W2069156797","https://openalex.org/W2087013144","https://openalex.org/W2087282766","https://openalex.org/W2093114078","https://openalex.org/W2102372511","https://openalex.org/W2108473160","https://openalex.org/W2110817584","https://openalex.org/W2112525653","https://openalex.org/W2115791615","https://openalex.org/W2116892352","https://openalex.org/W2117481741","https://openalex.org/W2119112357","https://openalex.org/W2129657353","https://openalex.org/W2132454757","https://openalex.org/W2132984323","https://openalex.org/W2144733135","https://openalex.org/W2146337213","https://openalex.org/W2152582233","https://openalex.org/W2155263022","https://openalex.org/W2158824661","https://openalex.org/W2160547390","https://openalex.org/W2163605009","https://openalex.org/W2164647001","https://openalex.org/W2194775991","https://openalex.org/W2318387983","https://openalex.org/W2318528603","https://openalex.org/W2343172899","https://openalex.org/W2343348914","https://openalex.org/W2348436252","https://openalex.org/W2508457857","https://openalex.org/W2557967966","https://openalex.org/W2584622573","https://openalex.org/W2592421213","https://openalex.org/W2595350724","https://openalex.org/W2639586488","https://openalex.org/W2764276316","https://openalex.org/W2774014017","https://openalex.org/W2777741489","https://openalex.org/W2793146153","https://openalex.org/W2891111066","https://openalex.org/W2891739644","https://openalex.org/W2892074009","https://openalex.org/W2892108217","https://openalex.org/W2904005001","https://openalex.org/W2919115771","https://openalex.org/W2954863666","https://openalex.org/W2962934715","https://openalex.org/W2964204553","https://openalex.org/W3150688106","https://openalex.org/W4210381520","https://openalex.org/W4245991938","https://openalex.org/W6610144941","https://openalex.org/W6634053107","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6677704767","https://openalex.org/W6681686951","https://openalex.org/W6684191040","https://openalex.org/W6730638624","https://openalex.org/W6749271710","https://openalex.org/W6754956979"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973","https://openalex.org/W3034789145","https://openalex.org/W4367628250"],"abstract_inverted_index":{"Deep":[0],"learning":[1,20,93,174,249],"has":[2,141,216,236],"been":[3],"successfully":[4],"applied":[5],"to":[6,21,71,112,161,180,220,255],"image":[7],"denoising.":[8],"In":[9,100,132,244],"this":[10,169],"study,":[11],"we":[12,149,246],"take":[13],"one":[14,40],"step":[15],"forward":[16],"by":[17,115],"using":[18,116],"deep":[19,92,173],"suppress":[22,113,221],"random":[23],"noise":[24,114,224,227],"in":[25,69,137,168,253],"poststack":[26,42,82],"seismic":[27,43,50,83,104,127,139,183,206,210],"data":[28,44,105,128,140,184,207,211],"from":[29,185,228],"the":[30,39,74,77,86,101,178,189,192,201,214,218,222,233,257],"aspects":[31],"of":[32,76,81,103,126,191,204,275],"network":[33,60,235,261],"architecture":[34],"and":[35,208,225,250],"training":[36,135,152,165,193,230,258,262],"samples.":[37,166,187,231],"On":[38,85],"hand,":[41,88],"denoising":[45,57,89,238,277],"mainly":[46],"aims":[47],"at":[48],"3-D":[49,56,65,78],"data.":[51,84],"We":[52],"designed":[53],"an":[54,143],"end-to-end":[55],"convolutional":[58],"neural":[59],"(3-D-DnCNN)":[61],"that":[62,119,156,172,213,274],"takes":[63],"raw":[64],"cubes":[66],"as":[67],"input":[68],"order":[70,254],"better":[72,237],"extract":[73],"features":[75],"spatial":[79],"structure":[80],"other":[87],"images":[90],"with":[91],"require":[94],"noisy-clean":[95],"sample":[96,153],"pairs":[97],"for":[98],"training.":[99],"field":[102,138,182,209],"processing,":[106],"researchers":[107],"usually":[108],"try":[109],"their":[110],"best":[111],"complex":[117,159,199],"processes":[118],"combine":[120],"different":[121,229],"methods,":[122],"but":[123,145],"clean":[124],"labels":[125],"are":[129],"not":[130],"available.":[131],"addition,":[133,245],"building":[134,190],"samples":[136,194],"become":[142],"interesting":[144],"challenging":[146],"problem.":[147],"Therefore,":[148],"propose":[150],"a":[151,158,198],"selection":[154],"method":[155],"contains":[157],"workflow":[160],"produce":[162],"comparatively":[163],"ideal":[164],"Experiments":[167],"study":[170],"demonstrate":[171],"can":[175,269],"directly":[176],"learn":[177],"ability":[179,219],"denoise":[181],"selected":[186],"Although":[188],"may":[195],"occur":[196],"through":[197],"process,":[200],"experimental":[202],"results":[203],"synthetic":[205],"show":[212],"3-D-DnCNN":[215,234],"learned":[217],"Gaussian":[223],"super-Gaussian":[226],"Moreover,":[232],"performance":[239],"toward":[240],"arc-like":[241],"imaging":[242],"noise.":[243],"adopt":[247],"residual":[248],"batch":[251],"normalization":[252],"accelerate":[256],"speed.":[259],"After":[260],"is":[263],"satisfactorily":[264],"completed,":[265],"its":[266],"processing":[267],"efficiency":[268],"be":[270],"significantly":[271],"higher":[272],"than":[273],"conventional":[276],"methods.":[278]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":19},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":36},{"year":2022,"cited_by_count":34},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":3}],"updated_date":"2026-05-09T13:55:54.758798","created_date":"2025-10-10T00:00:00"}