{"id":"https://openalex.org/W4321607995","doi":"https://doi.org/10.1109/lgrs.2023.3248233","title":"Seismic First Break Picking Through Swin Transformer Feature Extraction","display_name":"Seismic First Break Picking Through Swin Transformer Feature Extraction","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4321607995","doi":"https://doi.org/10.1109/lgrs.2023.3248233"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2023.3248233","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3248233","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/A5023608126","display_name":"Peifan Jiang","orcid":"https://orcid.org/0000-0002-8101-0061"},"institutions":[{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peifan Jiang","raw_affiliation_strings":["College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China","institution_ids":["https://openalex.org/I31595395"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103187741","display_name":"Fei Deng","orcid":"https://orcid.org/0000-0002-2218-0853"},"institutions":[{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Deng","raw_affiliation_strings":["College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China","institution_ids":["https://openalex.org/I31595395"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048419223","display_name":"Xuben Wang","orcid":"https://orcid.org/0000-0003-1963-8293"},"institutions":[{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuben Wang","raw_affiliation_strings":["College of Geophysics, Chengdu University of Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Geophysics, Chengdu University of Technology, Chengdu, China","institution_ids":["https://openalex.org/I31595395"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035547021","display_name":"Pengfei Shuai","orcid":null},"institutions":[{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfei Shuai","raw_affiliation_strings":["College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China","institution_ids":["https://openalex.org/I31595395"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021405148","display_name":"Wen Luo","orcid":"https://orcid.org/0000-0002-3064-4195"},"institutions":[{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Luo","raw_affiliation_strings":["College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China","institution_ids":["https://openalex.org/I31595395"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033437280","display_name":"Yun Tang","orcid":"https://orcid.org/0000-0002-3122-5881"},"institutions":[{"id":"https://openalex.org/I31595395","display_name":"Chengdu University of Technology","ror":"https://ror.org/05pejbw21","country_code":"CN","type":"education","lineage":["https://openalex.org/I31595395"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Tang","raw_affiliation_strings":["College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Cyber Security, Chengdu University of Technology, Chengdu, China","institution_ids":["https://openalex.org/I31595395"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5023608126"],"corresponding_institution_ids":["https://openalex.org/I31595395"],"apc_list":null,"apc_paid":null,"fwci":3.3217,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.9354235,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"20","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9958000183105469,"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":0.9922000169754028,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6992273330688477},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.597038984298706},{"id":"https://openalex.org/keywords/flattening","display_name":"Flattening","score":0.5442562103271484},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.542654275894165},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5270845293998718},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5122467875480652},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5112473964691162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4624365568161011},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4499163329601288},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3914148211479187},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34631091356277466},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14929744601249695},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.1481441855430603}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6992273330688477},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.597038984298706},{"id":"https://openalex.org/C19444555","wikidata":"https://www.wikidata.org/wiki/Q212750","display_name":"Flattening","level":2,"score":0.5442562103271484},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.542654275894165},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5270845293998718},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5122467875480652},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5112473964691162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4624365568161011},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4499163329601288},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3914148211479187},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34631091356277466},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14929744601249695},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.1481441855430603},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2023.3248233","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3248233","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":[],"awards":[{"id":"https://openalex.org/G3384074292","display_name":null,"funder_award_id":"41930112","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1990943105","https://openalex.org/W2125471009","https://openalex.org/W2133164188","https://openalex.org/W2145747609","https://openalex.org/W2528961483","https://openalex.org/W2548661175","https://openalex.org/W2767292437","https://openalex.org/W2781854221","https://openalex.org/W2794417179","https://openalex.org/W2963840672","https://openalex.org/W2970419158","https://openalex.org/W2982278112","https://openalex.org/W3033131897","https://openalex.org/W3138516171","https://openalex.org/W3138839952","https://openalex.org/W3185921949","https://openalex.org/W3197955373","https://openalex.org/W3209396322","https://openalex.org/W3212186901","https://openalex.org/W4205355987","https://openalex.org/W4214852098","https://openalex.org/W4385245566","https://openalex.org/W4385763767"],"related_works":["https://openalex.org/W2049450268","https://openalex.org/W3163663844","https://openalex.org/W2076772710","https://openalex.org/W2627522194","https://openalex.org/W2046237991","https://openalex.org/W2504233681","https://openalex.org/W2946123365","https://openalex.org/W2015179278","https://openalex.org/W2390326991","https://openalex.org/W4390726246"],"abstract_inverted_index":{"The":[0,141,182,222],"application":[1],"of":[2,37,70,75,96,99,155,167,170,187,212],"neural":[3,26],"networks":[4,27,40,45,214],"to":[5,49,89,93,107,129,137,160,195,215],"seismic":[6,143],"first":[7],"break":[8],"(FB)":[9],"picking":[10,21,108,220,230],"research":[11],"has":[12,227],"been":[13,30],"developed":[14],"for":[15,77],"many":[16],"years.":[17],"Numerous":[18],"multitrace":[19,101],"FB":[20,113,139,219,229,245],"methods":[22],"based":[23,41],"on":[24,42,124],"convolutional":[25,44],"(CNNs)":[28],"have":[29,50],"proposed.":[31],"Among":[32],"them,":[33],"the":[34,57,63,71,81,83,97,100,125,152,156,161,164,168,171,178,188,197,206,210,244],"pickup":[35],"method":[36,74],"semantic":[38,180],"segmentation":[39],"fully":[43],"(FCNs)":[46],"is":[47,104,174,193,236,248],"proven":[48],"stronger":[51],"noise":[52],"immunity.":[53],"However,":[54],"when":[55],"in":[56,62,239],"data":[58,144],"with":[59,110],"drastic":[60,111],"variations":[61],"FBs":[64,98,169],"between":[65,132,184],"local":[66,240],"adjacent":[67,172,241],"traces,":[68],"because":[69],"feature":[72,86,199],"extraction":[73],"FCNs":[76,234],"convergence":[78],"information":[79,166],"around":[80],"data,":[82,136],"network":[84],"output":[85,135],"edge":[87],"tends":[88],"smooth,":[90],"which":[91,103],"leads":[92],"a":[94,119,148],"flattening":[95],"pickup,":[102],"not":[105],"conducive":[106],"traces":[109,173,242],"intertrace":[112],"variations.":[114],"Therefore,":[115],"we":[116],"use":[117,202],"Transformer,":[118],"weight-transfer":[120],"model":[121],"that":[122,225],"relies":[123],"self-attention":[126],"(SA)":[127],"mechanism":[128],"calculate":[130],"weights":[131],"input":[133,159],"and":[134,158,163,190,208,235],"extract":[138],"features.":[140],"2-D":[142],"are":[145],"flattened":[146],"into":[147],"1-D":[149],"sequence":[150,189,198],"along":[151],"time":[153,246],"dimension":[154],"trace":[157],"network,":[162],"spatial":[165],"considered":[175],"without":[176],"disrupting":[177],"time-series":[179],"information.":[181],"correlation":[183],"any":[185],"element":[186],"other":[191],"elements":[192],"calculated":[194],"obtain":[196],"weights.":[200],"We":[201],"Swin":[203],"Transformer":[204],"as":[205],"backbone":[207],"combine":[209],"features":[211],"U-shaped":[213],"design":[216],"an":[217],"end-to-end":[218],"network\u2014STUNet.":[221],"results":[223],"show":[224],"STUNet":[226],"higher":[228],"accuracy":[231],"than":[232],"current":[233],"more":[237],"effective":[238],"where":[243],"variation":[247],"drastic.":[249]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}