{"id":"https://openalex.org/W4402968169","doi":"https://doi.org/10.1109/tgrs.2024.3462935","title":"First-Arrival Picking for Out-of-Distribution Noisy Data: A Cost-Effective Transfer Learning Method With Tens of Samples","display_name":"First-Arrival Picking for Out-of-Distribution Noisy Data: A Cost-Effective Transfer Learning Method With Tens of Samples","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4402968169","doi":"https://doi.org/10.1109/tgrs.2024.3462935"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3462935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3462935","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/A5101420384","display_name":"Hanyang Li","orcid":"https://orcid.org/0000-0001-7779-3772"},"institutions":[{"id":"https://openalex.org/I921716337","display_name":"Northeast Petroleum University","ror":"https://ror.org/03net5943","country_code":"CN","type":"education","lineage":["https://openalex.org/I921716337"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hanyang Li","raw_affiliation_strings":["Sanya Offshore Oil and Gas Research Institute, Northeast Petroleum University, Sanya, China","SanYa Offshore Oil and Gas Research Institute, Northeast petroleum university, Sanya, China"],"affiliations":[{"raw_affiliation_string":"Sanya Offshore Oil and Gas Research Institute, Northeast Petroleum University, Sanya, China","institution_ids":["https://openalex.org/I921716337"]},{"raw_affiliation_string":"SanYa Offshore Oil and Gas Research Institute, Northeast petroleum university, Sanya, China","institution_ids":["https://openalex.org/I921716337"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082506668","display_name":"Xuegui Li","orcid":"https://orcid.org/0000-0001-9249-7509"},"institutions":[{"id":"https://openalex.org/I4210094970","display_name":"Energy Research Institute","ror":"https://ror.org/00ndnb620","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094970","https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I921716337","display_name":"Northeast Petroleum University","ror":"https://ror.org/03net5943","country_code":"CN","type":"education","lineage":["https://openalex.org/I921716337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuegui Li","raw_affiliation_strings":["Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China","institution_ids":["https://openalex.org/I921716337","https://openalex.org/I4210094970"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047787508","display_name":"Yuhang Sun","orcid":"https://orcid.org/0000-0002-8616-9901"},"institutions":[{"id":"https://openalex.org/I4210094970","display_name":"Energy Research Institute","ror":"https://ror.org/00ndnb620","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094970","https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I921716337","display_name":"Northeast Petroleum University","ror":"https://ror.org/03net5943","country_code":"CN","type":"education","lineage":["https://openalex.org/I921716337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhang Sun","raw_affiliation_strings":["Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China","institution_ids":["https://openalex.org/I921716337","https://openalex.org/I4210094970"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064660687","display_name":"Hongli Dong","orcid":"https://orcid.org/0000-0001-8531-6757"},"institutions":[{"id":"https://openalex.org/I4210094970","display_name":"Energy Research Institute","ror":"https://ror.org/00ndnb620","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210094970","https://openalex.org/I4210142748"]},{"id":"https://openalex.org/I921716337","display_name":"Northeast Petroleum University","ror":"https://ror.org/03net5943","country_code":"CN","type":"education","lineage":["https://openalex.org/I921716337"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongli Dong","raw_affiliation_strings":["National Key Laboratory of Continental Shale Oil, the Artificial Intelligence Energy Research Institute, and Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing, China","Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Continental Shale Oil, the Artificial Intelligence Energy Research Institute, and Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing, China","institution_ids":["https://openalex.org/I921716337"]},{"raw_affiliation_string":"Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, China","institution_ids":["https://openalex.org/I921716337","https://openalex.org/I4210094970"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080289760","display_name":"Gang Xu","orcid":"https://orcid.org/0000-0003-2329-9458"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gang Xu","raw_affiliation_strings":["Emerging Geophysical Explorapment Division, Bureau of Geophysical Prospecting, Zhuozhou, China"],"affiliations":[{"raw_affiliation_string":"Emerging Geophysical Explorapment Division, Bureau of Geophysical Prospecting, Zhuozhou, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101420384"],"corresponding_institution_ids":["https://openalex.org/I921716337"],"apc_list":null,"apc_paid":null,"fwci":2.1264,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.89256602,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"13"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9889000058174133,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9889000058174133,"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/T10860","display_name":"Speech and Audio Processing","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9805999994277954,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7147400379180908},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.520821213722229},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.44422852993011475},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4244108200073242},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3445504903793335},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.17274674773216248}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7147400379180908},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.520821213722229},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.44422852993011475},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4244108200073242},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3445504903793335},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.17274674773216248},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2024.3462935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3462935","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/G3066835647","display_name":null,"funder_award_id":"U21A2019","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3827579604","display_name":null,"funder_award_id":"LH2023D012","funder_id":"https://openalex.org/F4320323085","funder_display_name":"Natural Science Foundation of Heilongjiang Province"},{"id":"https://openalex.org/G6364725116","display_name":null,"funder_award_id":"LH2022F008","funder_id":"https://openalex.org/F4320323085","funder_display_name":"Natural Science Foundation of Heilongjiang Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323085","display_name":"Natural Science Foundation of Heilongjiang Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W1980785284","https://openalex.org/W2025583962","https://openalex.org/W2067014477","https://openalex.org/W2110309672","https://openalex.org/W2145747609","https://openalex.org/W2317140119","https://openalex.org/W2412205031","https://openalex.org/W2500646743","https://openalex.org/W2781854221","https://openalex.org/W2823482656","https://openalex.org/W2884436604","https://openalex.org/W2921520820","https://openalex.org/W2937917695","https://openalex.org/W2989717941","https://openalex.org/W2995504702","https://openalex.org/W3022504219","https://openalex.org/W3026481210","https://openalex.org/W3033131897","https://openalex.org/W3034090122","https://openalex.org/W3036653428","https://openalex.org/W3036939155","https://openalex.org/W3038017363","https://openalex.org/W3046859104","https://openalex.org/W3047855151","https://openalex.org/W3135519184","https://openalex.org/W3137248351","https://openalex.org/W3137839003","https://openalex.org/W3155564877","https://openalex.org/W3170600903","https://openalex.org/W3189132700","https://openalex.org/W4206204902","https://openalex.org/W4225775382","https://openalex.org/W4225931533","https://openalex.org/W4255625052","https://openalex.org/W4293298413","https://openalex.org/W4307168624","https://openalex.org/W4309675323","https://openalex.org/W4310856925","https://openalex.org/W4313909761","https://openalex.org/W4323896981","https://openalex.org/W4362636945","https://openalex.org/W4366090357","https://openalex.org/W4386241849","https://openalex.org/W4386264086","https://openalex.org/W4387169346","https://openalex.org/W4387503046","https://openalex.org/W4390985966","https://openalex.org/W4392316883","https://openalex.org/W4399768597","https://openalex.org/W6843689347"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W3123837699"],"abstract_inverted_index":{"Data-driven":[0],"methods":[1],"for":[2,33,143,203],"picking":[3],"the":[4,35,50,108,153,158,163,178],"first-arrival":[5,144],"of":[6,45,93,111,140,162],"seismic":[7],"waves":[8],"can":[9],"encounter":[10],"challenges":[11,161],"with":[12,18,168,194],"generalization":[13,84],"when":[14],"they":[15],"are":[16],"faced":[17],"out-of-distribution":[19,88],"data":[20,170],"that":[21],"falls":[22],"outside":[23],"their":[24],"training":[25],"set.":[26],"Transfer":[27],"learning":[28,40,79,104,191],"is":[29,147],"a":[30,42,76,122,127,131,138],"promising":[31],"technique":[32],"boosting":[34],"method\u2019s":[36],"generalization.":[37],"However,":[38],"transfer":[39,78,103,190],"necessitates":[41],"considerable":[43],"number":[44],"labeled":[46],"samples":[47],"to":[48,70,82,99,156,188],"fit":[49],"target":[51,115,164],"domain":[52,116],"data,":[53],"which":[54,96,146],"restricts":[55],"its":[56],"application":[57],"in":[58,107],"tasks":[59],"characterized":[60],"by":[61,87,152],"small":[62],"sample":[63],"sizes":[64],"and":[65,130,150,174],"real-time":[66],"constraints.":[67],"In":[68],"response":[69],"these":[71],"challenges,":[72],"this":[73],"article":[74],"introduces":[75],"cost-effective":[77],"approach":[80],"designed":[81],"mitigate":[83],"issues":[85],"caused":[86],"noise.":[89,117],"The":[90,118,134],"central":[91],"innovation":[92],"our":[94],"method,":[95],"we":[97],"refer":[98],"as":[100],"prior":[101,112],"knowledge-guided":[102],"(PG-TL),":[105],"lies":[106],"efficient":[109],"utilization":[110],"knowledge":[113,142],"concerning":[114],"PG-TL":[119,179],"method":[120,180],"adopts":[121],"parallel":[123],"network":[124,129,136,155],"architecture,":[125],"comprising":[126],"backbone":[128,135],"branch":[132,154],"network.":[133],"provides":[137],"foundation":[139],"universal":[141],"picking,":[145],"then":[148],"refined":[149],"adapted":[151],"address":[157],"specific":[159],"noise":[160,176],"domain.":[165],"Through":[166],"validation":[167],"field":[169],"from":[171],"different":[172,175],"regions":[173],"levels,":[177],"exhibits":[181],"robust":[182],"applicability,":[183],"achieving":[184],"performance":[185],"levels":[186],"comparable":[187],"traditional":[189],"approaches":[192],"but":[193],"significantly":[195],"reduced":[196],"reliance":[197],"on":[198],"samples\u2019":[199],"only":[200],"requiring":[201],"dozens":[202],"effective":[204],"implementation.":[205]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
