{"id":"https://openalex.org/W4400975130","doi":"https://doi.org/10.1109/access.2024.3433612","title":"A Comparative Study of Vision Transformer and Convolutional Neural Network Models in Geological Fault Detection","display_name":"A Comparative Study of Vision Transformer and Convolutional Neural Network Models in Geological Fault Detection","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4400975130","doi":"https://doi.org/10.1109/access.2024.3433612"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3433612","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3433612","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3433612","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5105064562","display_name":"Jing Wang","orcid":"https://orcid.org/0009-0003-3552-9633"},"institutions":[{"id":"https://openalex.org/I4210119087","display_name":"North China Institute of Aerospace Engineering","ror":"https://ror.org/02m7msy24","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210119087"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Wang","raw_affiliation_strings":["School of Computer Science and Engineering, North China Institute of Aerospace Engineering, Langfang, Hebei, China"],"raw_orcid":"https://orcid.org/0009-0003-3552-9633","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, North China Institute of Aerospace Engineering, Langfang, Hebei, China","institution_ids":["https://openalex.org/I4210119087"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114321241","display_name":"Siteng Ma","orcid":"https://orcid.org/0000-0002-0374-166X"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Siteng Ma","raw_affiliation_strings":["School of Computer Science, University College Dublin, Dublin 4, Ireland"],"raw_orcid":"https://orcid.org/0000-0002-0374-166X","affiliations":[{"raw_affiliation_string":"School of Computer Science, University College Dublin, Dublin 4, Ireland","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101620761","display_name":"Yu An","orcid":"https://orcid.org/0000-0003-2890-6405"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Yu An","raw_affiliation_strings":["School of Computer Science, University College Dublin, Dublin 4, Ireland"],"raw_orcid":"https://orcid.org/0000-0003-2890-6405","affiliations":[{"raw_affiliation_string":"School of Computer Science, University College Dublin, Dublin 4, Ireland","institution_ids":["https://openalex.org/I100930933"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009000289","display_name":"Ruihai Dong","orcid":"https://orcid.org/0000-0002-2509-1370"},"institutions":[{"id":"https://openalex.org/I100930933","display_name":"University College Dublin","ror":"https://ror.org/05m7pjf47","country_code":"IE","type":"education","lineage":["https://openalex.org/I100930933"]}],"countries":["IE"],"is_corresponding":false,"raw_author_name":"Ruihai Dong","raw_affiliation_strings":["School of Computer Science, University College Dublin, Dublin 4, Ireland"],"raw_orcid":"https://orcid.org/0000-0002-2509-1370","affiliations":[{"raw_affiliation_string":"School of Computer Science, University College Dublin, Dublin 4, Ireland","institution_ids":["https://openalex.org/I100930933"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":2.4237,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.88389134,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"136148","last_page":"136159"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9957000017166138,"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"}},"topics":[{"id":"https://openalex.org/T12282","display_name":"Mineral Processing and Grinding","score":0.9957000017166138,"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"}},{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9896000027656555,"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/T10572","display_name":"Geophysical and Geoelectrical Methods","score":0.9884999990463257,"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":0.83216392993927},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8130991458892822},{"id":"https://openalex.org/keywords/fault-detection-and-isolation","display_name":"Fault detection and isolation","score":0.6618579626083374},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5831949710845947},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5523169040679932},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5246286988258362},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.46613427996635437},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4657260775566101},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4586730897426605},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4547935128211975},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42794132232666016},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38930684328079224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.83216392993927},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8130991458892822},{"id":"https://openalex.org/C152745839","wikidata":"https://www.wikidata.org/wiki/Q5438153","display_name":"Fault detection and isolation","level":3,"score":0.6618579626083374},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5831949710845947},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5523169040679932},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5246286988258362},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.46613427996635437},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4657260775566101},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4586730897426605},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4547935128211975},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42794132232666016},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38930684328079224},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C172707124","wikidata":"https://www.wikidata.org/wiki/Q423488","display_name":"Actuator","level":2,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3433612","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3433612","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:affddfd8270f4b52aeb1050c1ca916e1","is_oa":true,"landing_page_url":"https://doaj.org/article/affddfd8270f4b52aeb1050c1ca916e1","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 136148-136159 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3433612","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3433612","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.6700000166893005,"display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G6473078213","display_name":null,"funder_award_id":"201908130258","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"}],"funders":[{"id":"https://openalex.org/F4320320847","display_name":"Science Foundation Ireland","ror":"https://ror.org/0271asj38"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2194775991","https://openalex.org/W2787091153","https://openalex.org/W2809254203","https://openalex.org/W2890687414","https://openalex.org/W2908510526","https://openalex.org/W2911424749","https://openalex.org/W2935135048","https://openalex.org/W3013253863","https://openalex.org/W3091680284","https://openalex.org/W3111290062","https://openalex.org/W3127751679","https://openalex.org/W3133517930","https://openalex.org/W3138516171","https://openalex.org/W3153344593","https://openalex.org/W3157386224","https://openalex.org/W3160284783","https://openalex.org/W3169279223","https://openalex.org/W3200379731","https://openalex.org/W3203480968","https://openalex.org/W3204166336","https://openalex.org/W3207293609","https://openalex.org/W3212933375","https://openalex.org/W4211091942","https://openalex.org/W4225871896","https://openalex.org/W4226171979","https://openalex.org/W4296425595","https://openalex.org/W4320920694","https://openalex.org/W4360869627","https://openalex.org/W4384937344","https://openalex.org/W4386561400","https://openalex.org/W4392509048","https://openalex.org/W6687483927","https://openalex.org/W6757817989","https://openalex.org/W6795435739","https://openalex.org/W6810653034"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4321487865","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Geological":[0],"fault":[1,16,31,80,156],"detection":[2,17,32,81],"is":[3,131,178],"a":[4,34],"critical":[5],"aspect":[6],"of":[7,15,76,119,137,163],"geological":[8],"exploitation":[9],"and":[10,82,102,112,167],"oil-gas":[11],"exploration.":[12],"The":[13],"automation":[14],"can":[18],"significantly":[19],"reduce":[20],"the":[21,39,74,117,135],"dependence":[22],"on":[23,47],"expert":[24,173],"labeling.":[25],"Current":[26],"prevailing":[27],"methods":[28],"often":[29],"treat":[30],"as":[33],"semantic":[35],"segmentation":[36],"task":[37],"using":[38],"Convolutional":[40],"Neural":[41],"Network":[42],"(CNN).":[43],"However,":[44],"CNNs":[45],"emphasize":[46],"local":[48],"feature":[49],"extraction,":[50,66],"making":[51],"them":[52],"susceptible":[53],"to":[54,123,133],"noise":[55,124],"interference.":[56],"In":[57],"contrast,":[58],"Vision":[59],"Transformer":[60],"(ViT)":[61],"models,":[62,92,97,101,106,149],"prioritizing":[63],"global":[64],"context":[65],"have":[67],"shown":[68],"competitive":[69],"performance.":[70],"This":[71],"paper":[72],"explores":[73],"application":[75],"ViT":[77,100,121],"models":[78,122,140],"for":[79],"compares":[83],"their":[84],"performance":[85],"against":[86],"CNN":[87,96],"models.":[88],"We":[89],"investigate":[90],"six":[91],"including":[93],"two":[94,98,103],"pure":[95,99,120],"hybrid":[104,139],"CNN&ViT":[105,138],"comparing":[107],"three":[108],"datasets":[109],"(Thebe,":[110],"FaultSeg3D,":[111],"Kerry3D).":[113],"Our":[114,175,176],"analysis":[115],"underscores":[116],"resilience":[118],"interference":[125],"in":[126,141,155],"real-world":[127],"data.":[128],"Additionally,":[129],"it":[130],"noteworthy":[132],"highlight":[134],"advantage":[136],"delineating":[142],"low-grade":[143],"faults.":[144],"Furthermore,":[145],"leveraging":[146],"pre-trained":[147],"ImageNet":[148],"SwinUnet":[150],"demonstrates":[151],"remarkable":[152],"data":[153],"efficiency":[154],"prediction,":[157],"requiring":[158],"only":[159],"about":[160],"100":[161],"pairs":[162],"2D":[164],"image":[165],"patches":[166],"yielding":[168],"results":[169],"closely":[170],"aligned":[171],"with":[172],"annotations.":[174],"code":[177],"publicly":[179],"available":[180],"at:":[181],"https://github.com/wangjing9999/Comparing-CNN-and-ViT-in-Geological-Fault-Detection.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
