{"id":"https://openalex.org/W3207869134","doi":"https://doi.org/10.1109/lgrs.2021.3120315","title":"Accelerating Seismic Dip Estimation With Deep Learning","display_name":"Accelerating Seismic Dip Estimation With Deep Learning","publication_year":2021,"publication_date":"2021-10-15","ids":{"openalex":"https://openalex.org/W3207869134","doi":"https://doi.org/10.1109/lgrs.2021.3120315","mag":"3207869134"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2021.3120315","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3120315","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/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":true,"raw_author_name":"Xiaokai Wang","raw_affiliation_strings":["School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0001-5396-985X","affiliations":[{"raw_affiliation_string":"School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","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":false,"raw_author_name":"Dawei Liu","raw_affiliation_strings":["School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0001-5553-2379","affiliations":[{"raw_affiliation_string":"School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100651084","display_name":"Wenchao Chen","orcid":"https://orcid.org/0000-0003-0696-337X"},"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":"Wenchao Chen","raw_affiliation_strings":["School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0003-0696-337X","affiliations":[{"raw_affiliation_string":"School of Information and Communications Engineering, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I87445476"]},{"raw_affiliation_string":"School of Information and Communications Engineering, Xi\u2019an Jiaotong University, Xi\u2019an, China","institution_ids":["https://openalex.org/I87445476"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100666814"],"corresponding_institution_ids":["https://openalex.org/I87445476"],"apc_list":null,"apc_paid":null,"fwci":0.559,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.63789198,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":97},"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":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":0.9977999925613403,"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":0.9940999746322632,"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":0.6321544051170349},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5539266467094421},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5353036522865295},{"id":"https://openalex.org/keywords/seismic-attribute","display_name":"Seismic attribute","score":0.5237036943435669},{"id":"https://openalex.org/keywords/magnetic-dip","display_name":"Magnetic dip","score":0.4494558870792389},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44924798607826233},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41277238726615906},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.3534203767776489},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.338501513004303},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32665830850601196},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.2104010283946991},{"id":"https://openalex.org/keywords/geophysics","display_name":"Geophysics","score":0.17348966002464294}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6321544051170349},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5539266467094421},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5353036522865295},{"id":"https://openalex.org/C2781294565","wikidata":"https://www.wikidata.org/wiki/Q5711131","display_name":"Seismic attribute","level":2,"score":0.5237036943435669},{"id":"https://openalex.org/C60365795","wikidata":"https://www.wikidata.org/wiki/Q1180638","display_name":"Magnetic dip","level":2,"score":0.4494558870792389},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44924798607826233},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41277238726615906},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3534203767776489},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.338501513004303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32665830850601196},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.2104010283946991},{"id":"https://openalex.org/C8058405","wikidata":"https://www.wikidata.org/wiki/Q46255","display_name":"Geophysics","level":1,"score":0.17348966002464294}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2021.3120315","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3120315","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/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/G6997018651","display_name":null,"funder_award_id":"41774135","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/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W28988658","https://openalex.org/W2014758788","https://openalex.org/W2014825640","https://openalex.org/W2050666142","https://openalex.org/W2059073625","https://openalex.org/W2073460414","https://openalex.org/W2106921281","https://openalex.org/W2126097500","https://openalex.org/W2128497116","https://openalex.org/W2149813544","https://openalex.org/W2793189836","https://openalex.org/W2892287369","https://openalex.org/W2906820601","https://openalex.org/W2913340405","https://openalex.org/W2928781249","https://openalex.org/W2946726629","https://openalex.org/W2958903376","https://openalex.org/W2963787510","https://openalex.org/W3042090478","https://openalex.org/W3043911516","https://openalex.org/W3125476449","https://openalex.org/W4389076767","https://openalex.org/W6637373629"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2376760083","https://openalex.org/W2046435967","https://openalex.org/W4231775656","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W2318112981"],"abstract_inverted_index":{"The":[0,176,218],"seismic":[1,7,60,88,116,154,174,224],"volumetric":[2,225],"dip":[3,24,29,51,54,65,90,121,143,162,180],"is":[4,211],"a":[5,57,68,73,108],"crucial":[6],"geometric":[8],"attribute,":[9],"which":[10,112],"can":[11,158,221],"provide":[12],"useful":[13],"information":[14],"for":[15,142],"assisting":[16],"subsequent":[17],"processing":[18],"and":[19,52,71,103,118,136],"interpretation.":[20],"Waveform":[21],"similarity":[22],"scanning-based":[23],"estimation":[25,30,66,181],"(WSSB)":[26],"delivers":[27],"reliable":[28],"but":[31],"encounters":[32],"problems":[33],"of":[34,87,114,173,179,196,208,216],"expensive":[35],"computation.":[36],"To":[37,95],"improve":[38],"computing":[39],"efficiency,":[40],"we":[41,99],"use":[42],"multitask":[43],"deep":[44],"learning":[45],"to":[46,83,106,133,167,187],"simultaneously":[47],"estimate":[48,223],"the":[49,97,119,128,147,151,156,194,201,205],"inline":[50],"crossline":[53],"directly":[55],"from":[56,91],"3-D":[58,153,171],"field":[59,115],"dataset.":[61],"Our":[62],"method":[63,184,210,220],"considers":[64],"as":[67],"regression":[69],"problem":[70],"trains":[72],"multilayer":[74],"convolutional":[75],"neural":[76],"network":[77,129,157],"with":[78,227],"dual-channel":[79],"output.":[80],"It":[81],"aims":[82],"output":[84,159],"continuous":[85],"values":[86],"apparent":[89],"two":[92,169],"directions":[93],"simultaneously.":[94],"train":[96],"network,":[98],"propose":[100],"an":[101],"effective":[102],"efficient":[104],"workflow":[105],"create":[107],"training":[109],"sample":[110],"dataset,":[111],"consists":[113],"cubes":[117,163],"corresponding":[120],"labels":[122],"estimated":[123],"by":[124,182,189,199],"WSSB.":[125,190,217],"After":[126],"training,":[127],"automatically":[130],"learns":[131],"how":[132],"extract":[134],"rich":[135],"proper":[137],"features":[138],"that":[139,164,215],"are":[140,165,185],"important":[141],"estimation.":[144],"By":[145],"sliding":[146],"extraction":[148],"window":[149],"within":[150],"full":[152],"data,":[155],"many":[160],"overlapping":[161],"stacked":[166],"get":[168],"complete":[170],"volumes":[172],"dip.":[175],"final":[177],"results":[178],"our":[183,197,209],"similar":[186],"those":[188],"We":[191],"further":[192],"demonstrate":[193],"accuracy":[195],"approach":[198],"comparing":[200],"structural":[202],"curvature.":[203],"However,":[204],"computation":[206],"time":[207],"much":[212],"less":[213],"than":[214],"proposed":[219],"accurately":[222],"dips":[226],"high":[228],"computational":[229],"efficiency.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
