{"id":"https://openalex.org/W3159152913","doi":"https://doi.org/10.1145/3412841.3441976","title":"Multi-view 3D seismic facies classifier","display_name":"Multi-view 3D seismic facies classifier","publication_year":2021,"publication_date":"2021-03-22","ids":{"openalex":"https://openalex.org/W3159152913","doi":"https://doi.org/10.1145/3412841.3441976","mag":"3159152913"},"language":"en","primary_location":{"id":"doi:10.1145/3412841.3441976","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3441976","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-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/A5005657660","display_name":"Elton Alves Trindade","orcid":"https://orcid.org/0000-0002-8596-7143"},"institutions":[{"id":"https://openalex.org/I4104125","display_name":"Universidade Federal de Santa Catarina","ror":"https://ror.org/041akq887","country_code":"BR","type":"education","lineage":["https://openalex.org/I4104125"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Elton Alves Trindade","raw_affiliation_strings":["Federal University of Santa Catarina"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Santa Catarina","institution_ids":["https://openalex.org/I4104125"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090517396","display_name":"Mauro Roisenberg","orcid":"https://orcid.org/0000-0001-9707-0360"},"institutions":[{"id":"https://openalex.org/I4104125","display_name":"Universidade Federal de Santa Catarina","ror":"https://ror.org/041akq887","country_code":"BR","type":"education","lineage":["https://openalex.org/I4104125"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Mauro Roisenberg","raw_affiliation_strings":["Federal University of Santa Catarina"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Federal University of Santa Catarina","institution_ids":["https://openalex.org/I4104125"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.932,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.69878764,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1003","last_page":"1011"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T10399","display_name":"Hydrocarbon exploration and reservoir analysis","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10635","display_name":"Hydraulic Fracturing and Reservoir Analysis","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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":0.6484569907188416},{"id":"https://openalex.org/keywords/reservoir-modeling","display_name":"Reservoir modeling","score":0.5687477588653564},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5479211807250977},{"id":"https://openalex.org/keywords/seismic-exploration","display_name":"Seismic exploration","score":0.5069811344146729},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.506852924823761},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4719371497631073},{"id":"https://openalex.org/keywords/seismic-inversion","display_name":"Seismic inversion","score":0.47136226296424866},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44829535484313965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4065239429473877},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.40233319997787476},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37622445821762085},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.18120983242988586},{"id":"https://openalex.org/keywords/petroleum-engineering","display_name":"Petroleum engineering","score":0.09091651439666748}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6484569907188416},{"id":"https://openalex.org/C14641988","wikidata":"https://www.wikidata.org/wiki/Q7315329","display_name":"Reservoir modeling","level":2,"score":0.5687477588653564},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5479211807250977},{"id":"https://openalex.org/C2985650181","wikidata":"https://www.wikidata.org/wiki/Q1500431","display_name":"Seismic exploration","level":2,"score":0.5069811344146729},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.506852924823761},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4719371497631073},{"id":"https://openalex.org/C39267094","wikidata":"https://www.wikidata.org/wiki/Q7446968","display_name":"Seismic inversion","level":3,"score":0.47136226296424866},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44829535484313965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4065239429473877},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.40233319997787476},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37622445821762085},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.18120983242988586},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.09091651439666748},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C24552861","wikidata":"https://www.wikidata.org/wiki/Q2670177","display_name":"Data assimilation","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3412841.3441976","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3441976","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/14","display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1487746374","https://openalex.org/W1575767825","https://openalex.org/W1644641054","https://openalex.org/W2058777943","https://openalex.org/W2110104817","https://openalex.org/W2137983211","https://openalex.org/W2165698076","https://openalex.org/W2187059244","https://openalex.org/W2489836108","https://openalex.org/W2592517375","https://openalex.org/W2734349601","https://openalex.org/W2747502838","https://openalex.org/W2769910914","https://openalex.org/W2782980412","https://openalex.org/W2787564202","https://openalex.org/W2808760859","https://openalex.org/W2889867094","https://openalex.org/W2891255706","https://openalex.org/W2910329480","https://openalex.org/W2919115771","https://openalex.org/W2935135048","https://openalex.org/W2939587785","https://openalex.org/W2952234052","https://openalex.org/W2963941635","https://openalex.org/W2981548405","https://openalex.org/W2991532523","https://openalex.org/W2997209697","https://openalex.org/W3034362861","https://openalex.org/W3103261259","https://openalex.org/W4247142410"],"related_works":["https://openalex.org/W2767948278","https://openalex.org/W2078476627","https://openalex.org/W2332422762","https://openalex.org/W2899726959","https://openalex.org/W2999536221","https://openalex.org/W3215723174","https://openalex.org/W2766448086","https://openalex.org/W2056913788","https://openalex.org/W2811064406","https://openalex.org/W3106695078"],"abstract_inverted_index":{"Technological":[0],"advances":[1],"in":[2,101,161,182,209],"oil":[3,89],"and":[4,12,68,88,90,116,170,221],"gas":[5,91],"reservoir":[6,86,169],"characterization,":[7],"such":[8,215],"as":[9,112],"3D":[10,63,98,110,123,139],"seismic":[11,13,43,64,154,173],"attributes,":[14],"enriched":[15],"the":[16,23,53,83,105,118,126,132,156,162,166,171,183,186],"subsurface's":[17],"description":[18],"made":[19],"by":[20,72],"specialists.":[21],"Nevertheless,":[22],"analysis":[24],"of":[25,30,55,82,85,108,125,131,153,165,175,201,222],"this":[26,45],"now":[27],"huge":[28],"volume":[29],"data":[31,111,164,174],"became":[32],"a":[33,48,95,122,138,146,192,204,218],"complex":[34],"task.":[35],"In":[36],"order":[37],"to":[38,97,121,137,179,231],"more":[39],"efficiently":[40],"manage":[41],"big":[42],"data,":[44,155],"work":[46],"explores":[47],"computationally":[49],"cheaper":[50],"network":[51,233],"with":[52,128,191,203],"use":[54],"2D":[56,96],"orthogonal":[57,114],"planes":[58,134],"convolutional":[59],"neural":[60],"networks":[61],"for":[62],"cube":[65],"facies":[66],"classification":[67],"lithostratigrafic":[69],"groups,":[70],"supported":[71],"an":[73],"heuristic":[74,194],"based":[75,141],"on":[76,142],"geological":[77,143,193],"principles,":[78],"which":[79,102,212],"is":[80,217],"one":[81,220],"steps":[84],"characterization":[87],"exploration.":[92],"We":[93],"proposed":[94,157,187],"transfer":[99],"learning":[100],"we":[103],"split":[104],"training":[106,133],"samples":[107],"our":[109],"3":[113],"slices":[115],"convert":[117],"trained":[119],"parameters":[120],"counterpart":[124],"network,":[127],"each":[129],"direction":[130],"conveniently":[135],"converted":[136],"convolution,":[140],"insights.":[144],"Through":[145],"sampling":[147],"method":[148],"that":[149,214],"captures":[150],"spacial":[151],"information":[152],"model":[158],"was":[159],"applied":[160,230],"synthetic":[163],"Stanford":[167],"VI-E":[168],"real":[172],"F3-block":[176],"dataset.":[177],"Compared":[178],"other":[180,199],"models":[181],"same":[184],"benchmark,":[185],"AH-Net":[188],"Ensemble":[189],"classifier,":[190],"obtained":[195],"better":[196],"results":[197],"than":[198],"architectures":[200],"literature":[202],"very":[205],"feasible":[206],"computational":[207],"cost":[208],"both":[210],"datasets,":[211],"suggests":[213],"approach":[216],"promising":[219],"easy":[223],"replication,":[224],"since":[225],"it":[226],"can":[227],"be":[228],"seamlessly":[229],"any":[232],"architecture.":[234]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
