{"id":"https://openalex.org/W3089372845","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206661","title":"Efficient 3D Semantic Segmentation of Seismic Images using Orthogonal Planes 2D Convolutional Neural Networks","display_name":"Efficient 3D Semantic Segmentation of Seismic Images using Orthogonal Planes 2D Convolutional Neural Networks","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089372845","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206661","mag":"3089372845"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206661","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","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/A5007508521","display_name":"Arthur Bridi Guazzelli","orcid":null},"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":true,"raw_author_name":"Arthur Bridi Guazzelli","raw_affiliation_strings":["Statistics and Computer Science Dept., Federal University of Santa Catarina, Florian\u00f3polis, Brazil"],"affiliations":[{"raw_affiliation_string":"Statistics and Computer Science Dept., Federal University of Santa Catarina, Florian\u00f3polis, Brazil","institution_ids":["https://openalex.org/I4104125"]}]},{"author_position":"middle","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":["Statistics and Computer Science Dept., Federal University of Santa Catarina, Florian\u00f3polis, Brazil"],"affiliations":[{"raw_affiliation_string":"Statistics and Computer Science Dept., Federal University of Santa Catarina, Florian\u00f3polis, Brazil","institution_ids":["https://openalex.org/I4104125"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061923106","display_name":"Bruno Barbosa Rodrigues","orcid":"https://orcid.org/0000-0002-6127-8212"},"institutions":[{"id":"https://openalex.org/I32393484","display_name":"Petrobras (Brazil)","ror":"https://ror.org/0235kyq22","country_code":"BR","type":"company","lineage":["https://openalex.org/I32393484"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Bruno B. Rodrigues","raw_affiliation_strings":["Petrobras S.A., CENPES Research Center, Brazil"],"affiliations":[{"raw_affiliation_string":"Petrobras S.A., CENPES Research Center, Brazil","institution_ids":["https://openalex.org/I32393484"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5007508521"],"corresponding_institution_ids":["https://openalex.org/I4104125"],"apc_list":null,"apc_paid":null,"fwci":0.1765,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.51619265,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9969000220298767,"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"}},"topics":[{"id":"https://openalex.org/T11801","display_name":"Reservoir Engineering and Simulation Methods","score":0.9969000220298767,"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"}},{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9962999820709229,"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/T10635","display_name":"Hydraulic Fracturing and Reservoir Analysis","score":0.9930999875068665,"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/benchmark","display_name":"Benchmark (surveying)","score":0.7369827628135681},{"id":"https://openalex.org/keywords/reservoir-modeling","display_name":"Reservoir modeling","score":0.7047407031059265},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6732277274131775},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6616675853729248},{"id":"https://openalex.org/keywords/cube","display_name":"Cube (algebra)","score":0.6534258723258972},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5596867799758911},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5413326025009155},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5045794248580933},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47671857476234436},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4701267182826996},{"id":"https://openalex.org/keywords/data-cube","display_name":"Data cube","score":0.44454994797706604},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4328676462173462},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.43222910165786743},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39676642417907715},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.3961363732814789},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.19153127074241638},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09953415393829346},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09200215339660645},{"id":"https://openalex.org/keywords/petroleum-engineering","display_name":"Petroleum engineering","score":0.08136853575706482}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7369827628135681},{"id":"https://openalex.org/C14641988","wikidata":"https://www.wikidata.org/wiki/Q7315329","display_name":"Reservoir modeling","level":2,"score":0.7047407031059265},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6732277274131775},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6616675853729248},{"id":"https://openalex.org/C53051483","wikidata":"https://www.wikidata.org/wiki/Q861555","display_name":"Cube (algebra)","level":2,"score":0.6534258723258972},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5596867799758911},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5413326025009155},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5045794248580933},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47671857476234436},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4701267182826996},{"id":"https://openalex.org/C78168278","wikidata":"https://www.wikidata.org/wiki/Q5227269","display_name":"Data cube","level":2,"score":0.44454994797706604},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4328676462173462},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.43222910165786743},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39676642417907715},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3961363732814789},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.19153127074241638},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09953415393829346},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09200215339660645},{"id":"https://openalex.org/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.08136853575706482},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206661","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206661","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1910501430","https://openalex.org/W2002913742","https://openalex.org/W2084200478","https://openalex.org/W2118978333","https://openalex.org/W2139916508","https://openalex.org/W2301358467","https://openalex.org/W2567599812","https://openalex.org/W2765610253","https://openalex.org/W2792124446","https://openalex.org/W2805035545","https://openalex.org/W2891255706","https://openalex.org/W2891331019","https://openalex.org/W2891691398","https://openalex.org/W2896715165","https://openalex.org/W2906761419","https://openalex.org/W2909119972","https://openalex.org/W2911424749","https://openalex.org/W2913647591","https://openalex.org/W2963941635","https://openalex.org/W2967170982","https://openalex.org/W3101333263","https://openalex.org/W6926241944"],"related_works":["https://openalex.org/W2087724950","https://openalex.org/W2366586806","https://openalex.org/W2407777725","https://openalex.org/W1969639826","https://openalex.org/W1964212383","https://openalex.org/W1582972698","https://openalex.org/W2352476121","https://openalex.org/W2355987888","https://openalex.org/W2161001463","https://openalex.org/W3120311794"],"abstract_inverted_index":{"Technological":[0],"advances":[1],"in":[2,77,87,106,120,138],"oil":[3],"and":[4,12,86,123],"gas":[5],"reservoir":[6,59,85],"characterization":[7],"such":[8],"as":[9],"3D":[10,49],"seismics":[11],"seismic":[13,50,70],"attributes":[14],"enriched":[15],"the":[16,23,39,56,72,82,92,107,110],"subsurface's":[17],"description":[18],"made":[19],"by":[20],"specialists.":[21],"Nevertheless,":[22],"analysis":[24],"of":[25,30,41,55,58,69,81,98],"this":[26],"now":[27],"huge":[28],"volume":[29],"data":[31,80],"became":[32],"a":[33,62,88,99],"complex":[34],"task.":[35],"This":[36],"work":[37],"explores":[38],"use":[40,137],"2D":[42],"orthogonal":[43],"planes":[44],"convolutional":[45],"neural":[46],"networks":[47],"for":[48],"cube":[51],"facies":[52],"classification,":[53],"one":[54],"steps":[57],"characterization.":[60],"Through":[61],"sampling":[63,131],"method":[64,132],"that":[65],"captures":[66],"spatial":[67],"information":[68],"data,":[71],"proposed":[73],"model":[74],"were":[75],"applied":[76],"both":[78],"synthetic":[79],"Stanford":[83],"VI-E":[84],"benchmark":[89],"based":[90],"on":[91,127],"F3":[93],"block,":[94],"which":[95],"is":[96,133],"part":[97],"real":[100],"reservoir.":[101],"Compared":[102],"to":[103,136],"other":[104],"models":[105],"same":[108],"benchmark,":[109],"classifiers":[111],"produced":[112],"here":[113],"had":[114],"superior":[115],"results,":[116],"with":[117],"over":[118],"88%":[119],"pixel":[121],"accuracy":[122,126],"90%":[124],"class":[125],"some":[128],"instances.":[129],"The":[130],"also":[134],"flexible":[135],"practical":[139],"cases.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
