{"id":"https://openalex.org/W4226218162","doi":"https://doi.org/10.1109/tgrs.2022.3165037","title":"Learning From Noisy Data: An Unsupervised Random Denoising Method for Seismic Data Using Model-Based Deep Learning","display_name":"Learning From Noisy Data: An Unsupervised Random Denoising Method for Seismic Data Using Model-Based Deep Learning","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4226218162","doi":"https://doi.org/10.1109/tgrs.2022.3165037"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3165037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3165037","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/A5100422382","display_name":"Feng Wang","orcid":"https://orcid.org/0000-0002-8717-8893"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Wang","raw_affiliation_strings":["Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023261296","display_name":"Bo Yang","orcid":"https://orcid.org/0000-0001-6790-9882"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Yang","raw_affiliation_strings":["Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100461336","display_name":"Yuqing Wang","orcid":"https://orcid.org/0000-0002-9789-4295"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuqing Wang","raw_affiliation_strings":["State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Institute for Artificial Intelligence (THUAI), Tsinghua University, Beijing, China","Department of Automation, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Intelligent Technology and Systems, Beijing National Research Center for Information Science and Technology (BNRist), Institute for Artificial Intelligence (THUAI), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100432583","display_name":"Ming Wang","orcid":"https://orcid.org/0000-0003-0976-9871"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming Wang","raw_affiliation_strings":["Office of Water Survey and Design, Poyang County Bureau of Water Resources, Poyang, China"],"affiliations":[{"raw_affiliation_string":"Office of Water Survey and Design, Poyang County Bureau of Water Resources, Poyang, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100422382"],"corresponding_institution_ids":["https://openalex.org/I76130692"],"apc_list":null,"apc_paid":null,"fwci":7.0736,"has_fulltext":false,"cited_by_count":40,"citation_normalized_percentile":{"value":0.98430168,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"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":1.0,"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":1.0,"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/T11757","display_name":"Seismic Waves and Analysis","score":0.9993000030517578,"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":0.9957000017166138,"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":0.7156386971473694},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6575431227684021},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5849969387054443},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5815855264663696},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.5587161779403687},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5223765969276428},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.49224773049354553},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46036234498023987},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4434359073638916},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.43690192699432373},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41155102849006653},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16536948084831238},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09158197045326233}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7156386971473694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6575431227684021},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5849969387054443},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5815855264663696},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.5587161779403687},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5223765969276428},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.49224773049354553},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46036234498023987},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4434359073638916},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.43690192699432373},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41155102849006653},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16536948084831238},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09158197045326233},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3165037","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3165037","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":[],"awards":[{"id":"https://openalex.org/G4771007548","display_name":null,"funder_award_id":"2018YFC0603604","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G8895115085","display_name":null,"funder_award_id":"41774079","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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":74,"referenced_works":["https://openalex.org/W1507554030","https://openalex.org/W1522301498","https://openalex.org/W1523302144","https://openalex.org/W1829774568","https://openalex.org/W1901129140","https://openalex.org/W1982163941","https://openalex.org/W1994281301","https://openalex.org/W1996098514","https://openalex.org/W1999829977","https://openalex.org/W2010236565","https://openalex.org/W2016572604","https://openalex.org/W2020205028","https://openalex.org/W2051305545","https://openalex.org/W2064675550","https://openalex.org/W2093762864","https://openalex.org/W2118103795","https://openalex.org/W2122470519","https://openalex.org/W2133665775","https://openalex.org/W2141953966","https://openalex.org/W2153663612","https://openalex.org/W2160547390","https://openalex.org/W2160815625","https://openalex.org/W2257979135","https://openalex.org/W2318528603","https://openalex.org/W2403089413","https://openalex.org/W2417420177","https://openalex.org/W2508457857","https://openalex.org/W2604772668","https://openalex.org/W2618530766","https://openalex.org/W2766284769","https://openalex.org/W2798365772","https://openalex.org/W2899651173","https://openalex.org/W2904005001","https://openalex.org/W2919115771","https://openalex.org/W2937350470","https://openalex.org/W2945294778","https://openalex.org/W2947343518","https://openalex.org/W2951236102","https://openalex.org/W2955869164","https://openalex.org/W2970715218","https://openalex.org/W2972675027","https://openalex.org/W2993573967","https://openalex.org/W2995302838","https://openalex.org/W3035162526","https://openalex.org/W3042090478","https://openalex.org/W3094255354","https://openalex.org/W3096831136","https://openalex.org/W3102348318","https://openalex.org/W3102755745","https://openalex.org/W3104564825","https://openalex.org/W3121941308","https://openalex.org/W3126514463","https://openalex.org/W3133902371","https://openalex.org/W3143265780","https://openalex.org/W4231109964","https://openalex.org/W4231254085","https://openalex.org/W4242059867","https://openalex.org/W4250955649","https://openalex.org/W4287990713","https://openalex.org/W4295677899","https://openalex.org/W4297801963","https://openalex.org/W4298289240","https://openalex.org/W4301028776","https://openalex.org/W4385245566","https://openalex.org/W4386037851","https://openalex.org/W6631190155","https://openalex.org/W6638670064","https://openalex.org/W6735307612","https://openalex.org/W6739495466","https://openalex.org/W6739901393","https://openalex.org/W6756963044","https://openalex.org/W6763184335","https://openalex.org/W6769191023","https://openalex.org/W6788677503"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W2806259446","https://openalex.org/W2963326959","https://openalex.org/W4247136043","https://openalex.org/W4220926404","https://openalex.org/W3123344745"],"abstract_inverted_index":{"For":[0],"seismic":[1,216],"random":[2],"noise":[3],"attenuation,":[4],"deep":[5,45,93],"learning":[6],"has":[7],"attracted":[8],"much":[9],"attention":[10],"and":[11,77,99,136,186,214,230,248,254,256],"achieved":[12],"promising":[13],"performance.":[14],"However,":[15],"compared":[16,225],"with":[17,33,144],"conventional":[18,52],"methods,":[19,54,232],"the":[20,51,72,81,109,118,134,153,164,169,181,187,198,208,227,242,249],"denoising":[21,53,82,88],"performance":[22,56,224],"of":[23,197,251],"supervised":[24],"learning-based":[25],"methods":[26],"heavily":[27],"depends":[28],"on":[29,74,91,108,212],"massive":[30],"training":[31,155],"samples":[32],"high-quality":[34],"labeled":[35,75],"data,":[36],"which":[37,95,116,200],"makes":[38],"their":[39,55],"generalization":[40],"capabilities":[41],"limited.":[42],"Even":[43],"though":[44],"neural":[46,61,252],"networks":[47,62],"(DNNs)":[48],"usually":[49],"outperform":[50],"is":[57],"not":[58,206],"guaranteed":[59],"since":[60],"still":[63],"lack":[64],"good":[65],"mathematical":[66],"interpretability":[67],"at":[68],"present.":[69],"To":[70],"alleviate":[71,122],"dependency":[73],"data":[76,190,217],"explore":[78],"insights":[79],"into":[80],"system,":[83],"we":[84,132,167],"proposed":[85,165],"an":[86,160],"unsupervised":[87,161,231],"method":[89,204,221,259],"based":[90,107],"model-based":[92],"learning,":[94],"combined":[96],"domain":[97],"knowledge":[98],"a":[100,105,145],"data-driven":[101],"method.":[102],"We":[103],"designed":[104,149],"network":[106,154,253],"modified":[110],"iterative":[111],"soft":[112,119],"threshold":[113,120],"algorithm":[114],"(ISTA),":[115],"omitted":[117],"to":[121,150,226],"uncertainties":[123],"introduced":[124],"by":[125,172],"empirically":[126],"selected":[127],"thresholds.":[128],"In":[129,163],"this":[130],"network,":[131,185],"set":[133,168],"dictionary":[135],"code":[137],"as":[138,180],"trainable":[139],"parameters.":[140],"A":[141],"loss":[142],"function":[143],"smooth":[146],"penalty":[147],"was":[148],"ensure":[151],"that":[152,202,219],"can":[156,191],"be":[157,192],"implemented":[158],"in":[159],"manner.":[162],"method,":[166],"denoised":[170,189],"result":[171],"<inline-formula":[173,234],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[174,235],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[175,236],"<tex-math":[176,237],"notation=\"LaTeX\">$f-x$":[177,238],"</tex-math></inline-formula>":[178,239],"deconvolution":[179],"input":[182],"for":[183],"our":[184,203,220],"further":[188],"obtained":[193],"after":[194],"each":[195],"epoch":[196],"training,":[199],"means":[201],"does":[205],"need":[207],"testing":[209],"procedure.":[210],"Experiments":[211],"synthetic":[213],"field":[215],"demonstrate":[218],"exhibits":[222],"competitive":[223],"conventional,":[228],"supervised,":[229],"including":[233],"deconvolution,":[240],"curvelet,":[241],"Denoising":[243],"Convolutional":[244],"Neural":[245],"Network":[246],"(DnCNN),":[247],"integration":[250],"Block-matching":[255],"3-D":[257],"filtering":[258],"(NN":[260],"+":[261],"BM3D).":[262]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":17},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":4}],"updated_date":"2026-03-03T08:47:05.690250","created_date":"2025-10-10T00:00:00"}