{"id":"https://openalex.org/W4226466012","doi":"https://doi.org/10.1109/lgrs.2022.3167999","title":"Dropout-Based Robust Self-Supervised Deep Learning for Seismic Data Denoising","display_name":"Dropout-Based Robust Self-Supervised Deep Learning for Seismic Data Denoising","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4226466012","doi":"https://doi.org/10.1109/lgrs.2022.3167999"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2022.3167999","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3167999","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5060657166","display_name":"Gui Chen","orcid":"https://orcid.org/0000-0002-6617-7365"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Gui Chen","raw_affiliation_strings":["State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6617-7365","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014653096","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0001-9786-2093"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Liu","raw_affiliation_strings":["State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing, China","School of Petroleum, China University of Petroleum-Beijing, Karamay, Xinjiang, China"],"raw_orcid":"https://orcid.org/0000-0001-9786-2093","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293"]},{"raw_affiliation_string":"School of Petroleum, China University of Petroleum-Beijing, Karamay, Xinjiang, China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100675021","display_name":"Mi Zhang","orcid":"https://orcid.org/0000-0003-1584-2799"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mi Zhang","raw_affiliation_strings":["State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing, China","School of Petroleum, China University of Petroleum-Beijing, Karamay, Xinjiang, China"],"raw_orcid":"https://orcid.org/0000-0003-1584-2799","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293"]},{"raw_affiliation_string":"School of Petroleum, China University of Petroleum-Beijing, Karamay, Xinjiang, China","institution_ids":["https://openalex.org/I204553293"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101882574","display_name":"Haoran Zhang","orcid":"https://orcid.org/0000-0002-2039-7901"},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoran Zhang","raw_affiliation_strings":["State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2039-7901","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum-Beijing, Beijing, China","institution_ids":["https://openalex.org/I204553293"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5060657166"],"corresponding_institution_ids":["https://openalex.org/I204553293"],"apc_list":null,"apc_paid":null,"fwci":4.3579,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.95332808,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10892","display_name":"Drilling and Well Engineering","score":0.9962000250816345,"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/noise-reduction","display_name":"Noise reduction","score":0.7590156197547913},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.7376440167427063},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7140856981277466},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.656054675579071},{"id":"https://openalex.org/keywords/deconvolution","display_name":"Deconvolution","score":0.5518829226493835},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.531478226184845},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5221403241157532},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5206472277641296},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5117614269256592},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"Signal-to-noise ratio (imaging)","score":0.4603455066680908},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4253469407558441},{"id":"https://openalex.org/keywords/signal","display_name":"SIGNAL (programming language)","score":0.4220905005931854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.31409531831741333},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20962747931480408},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1828276515007019}],"concepts":[{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.7590156197547913},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.7376440167427063},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7140856981277466},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.656054675579071},{"id":"https://openalex.org/C174576160","wikidata":"https://www.wikidata.org/wiki/Q1183700","display_name":"Deconvolution","level":2,"score":0.5518829226493835},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.531478226184845},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5221403241157532},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5206472277641296},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5117614269256592},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.4603455066680908},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4253469407558441},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.4220905005931854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31409531831741333},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20962747931480408},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1828276515007019},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2022.3167999","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3167999","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1996098514","https://openalex.org/W2007221293","https://openalex.org/W2016572604","https://openalex.org/W2020205028","https://openalex.org/W2076393573","https://openalex.org/W2100495367","https://openalex.org/W2108473160","https://openalex.org/W2115528090","https://openalex.org/W2145167443","https://openalex.org/W2604772668","https://openalex.org/W2621603956","https://openalex.org/W2798365772","https://openalex.org/W2802726551","https://openalex.org/W2964013315","https://openalex.org/W2965060898","https://openalex.org/W2965510524","https://openalex.org/W2967662400","https://openalex.org/W2997574889","https://openalex.org/W3004375581","https://openalex.org/W3024236808","https://openalex.org/W3035542568","https://openalex.org/W3126514463","https://openalex.org/W3198159016","https://openalex.org/W3212113934","https://openalex.org/W6640963894","https://openalex.org/W6681096077","https://openalex.org/W6759001153"],"related_works":["https://openalex.org/W2159052453","https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2752972570","https://openalex.org/W4297051394","https://openalex.org/W2734887215","https://openalex.org/W2803255133","https://openalex.org/W1983978385","https://openalex.org/W3096304136"],"abstract_inverted_index":{"Incoherent":[0],"noise":[1],"suppression":[2],"is":[3,27,47],"an":[4],"indispensable":[5],"step":[6],"in":[7,20,88],"seismic":[8,21],"data":[9,22,35,46,68,120],"processing.":[10],"Recently,":[11],"deep":[12,107],"learning":[13],"(DL)":[14],"methods":[15,115,127],"have":[16],"gained":[17],"commendable":[18],"success":[19],"denoising,":[23],"one":[24],"of":[25,43,64,94],"which":[26],"the":[28,37,41,65,71,78,91,95,99,103,122,130,136],"supervised":[29],"DL":[30,54],"denoising":[31,55,92],"method":[32,56,124],"using":[33,58],"clean":[34,45,59],"as":[36],"training":[38,62],"label,":[39],"whereas":[40],"cost":[42],"obtaining":[44],"high.":[48],"We":[49],"investigate":[50],"a":[51,81],"robust":[52],"self-supervised":[53],"without":[57],"data.":[60],"Bernoulli-sampled":[61],"pairs":[63],"raw":[66],"noisy":[67],"produced":[69],"by":[70],"dropout":[72],"layer":[73],"are":[74],"served":[75],"to":[76],"train":[77],"NN,":[79],"and":[80,111,118,134],"Monte":[82],"Carlo":[83],"(MC)":[84],"self-integrated":[85],"technique":[86],"results":[87],"further":[89],"improving":[90],"quality":[93],"trained":[96],"NN":[97],"during":[98],"testing.":[100],"Compared":[101],"with":[102],"f-x":[104],"deconvolution":[105],"(FXDECON),":[106],"image":[108],"prior":[109],"(DIP),":[110],"sparse":[112],"autoencoder":[113],"(SAE)":[114],"via":[116],"synthetic":[117],"real":[119],"examples,":[121],"proposed":[123],"outperforms":[125],"these":[126],"for":[128],"enhancing":[129],"signal-to-noise":[131],"ratio":[132],"(SNR)":[133],"reducing":[135],"signal":[137],"loss.":[138]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}