{"id":"https://openalex.org/W4394711234","doi":"https://doi.org/10.1109/lgrs.2024.3387699","title":"Estimation of Fracture Properties From Azimuthal Seismic Data Using Convolution Neural Network","display_name":"Estimation of Fracture Properties From Azimuthal Seismic Data Using Convolution Neural Network","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4394711234","doi":"https://doi.org/10.1109/lgrs.2024.3387699"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2024.3387699","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3387699","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/A5067762439","display_name":"Xinpeng Pan","orcid":"https://orcid.org/0000-0002-5405-5993"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xinpeng Pan","raw_affiliation_strings":["State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development and State Energy Center for Shale Oil Research and Development, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5405-5993","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development and State Energy Center for Shale Oil Research and Development, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100439403","display_name":"Xinyan Li","orcid":"https://orcid.org/0000-0003-3541-7976"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyan Li","raw_affiliation_strings":["School of Geosciences and Info-Physics, the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, and the Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, Central South University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, and the Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061368736","display_name":"Lei Huang","orcid":"https://orcid.org/0000-0003-4457-827X"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Huang","raw_affiliation_strings":["School of Geosciences and Info-Physics, the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, and the Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-4457-827X","affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, and the Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100729648","display_name":"Lei Li","orcid":"https://orcid.org/0000-0002-3391-5608"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Li","raw_affiliation_strings":["School of Geosciences and Info-Physics, the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, and the Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-3391-5608","affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, and the Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100383519","display_name":"Pu Wang","orcid":"https://orcid.org/0000-0002-5291-0714"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pu Wang","raw_affiliation_strings":["School of Geosciences and Info-Physics, the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, and the Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-5291-0714","affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, and the Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015394916","display_name":"Jianxin Liu","orcid":"https://orcid.org/0000-0001-9395-4150"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianxin Liu","raw_affiliation_strings":["School of Geosciences and Info-Physics, the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, and the Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0001-9395-4150","affiliations":[{"raw_affiliation_string":"School of Geosciences and Info-Physics, the Hunan Key Laboratory of Nonferrous Resources and Geological Hazards Exploration, and the Key Laboratory of Metallogenic Prediction of Nonferrous Metals, Ministry of Education, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9192,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.89788639,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"21","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":0.9937999844551086,"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":0.9937999844551086,"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/T13018","display_name":"Seismology and Earthquake Studies","score":0.9883000254631042,"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/T10635","display_name":"Hydraulic Fracturing and Reservoir Analysis","score":0.9822999835014343,"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/convolution","display_name":"Convolution (computer science)","score":0.7166076302528381},{"id":"https://openalex.org/keywords/azimuth","display_name":"Azimuth","score":0.6818593740463257},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5695927143096924},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5614944696426392},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.5463332533836365},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.4442311227321625},{"id":"https://openalex.org/keywords/fracture","display_name":"Fracture (geology)","score":0.4284709095954895},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42820656299591064},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3567201495170593},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2951553165912628},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15888193249702454},{"id":"https://openalex.org/keywords/geotechnical-engineering","display_name":"Geotechnical engineering","score":0.1012926697731018},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.08166489005088806}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7166076302528381},{"id":"https://openalex.org/C159737794","wikidata":"https://www.wikidata.org/wiki/Q124274","display_name":"Azimuth","level":2,"score":0.6818593740463257},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5695927143096924},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5614944696426392},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.5463332533836365},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.4442311227321625},{"id":"https://openalex.org/C43369102","wikidata":"https://www.wikidata.org/wiki/Q2307625","display_name":"Fracture (geology)","level":2,"score":0.4284709095954895},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42820656299591064},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3567201495170593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2951553165912628},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15888193249702454},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.1012926697731018},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.08166489005088806},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2024.3387699","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3387699","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":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G2913966578","display_name":null,"funder_award_id":"2023ZZTS0740","funder_id":"https://openalex.org/F4320321514","funder_display_name":"Central South University"},{"id":"https://openalex.org/G4956067447","display_name":null,"funder_award_id":"42004107","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5563539678","display_name":null,"funder_award_id":"42374076","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/F4320321514","display_name":"Central South University","ror":"https://ror.org/00f1zfq44"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1905987197","https://openalex.org/W2074650791","https://openalex.org/W2086421081","https://openalex.org/W2107263769","https://openalex.org/W2147800946","https://openalex.org/W2148180822","https://openalex.org/W2163922914","https://openalex.org/W2167030304","https://openalex.org/W2781854221","https://openalex.org/W2794057883","https://openalex.org/W2848748066","https://openalex.org/W2913776725","https://openalex.org/W3000574968","https://openalex.org/W3027568991","https://openalex.org/W3032991056","https://openalex.org/W3081976524","https://openalex.org/W3147262955","https://openalex.org/W3183154679","https://openalex.org/W3201872403","https://openalex.org/W4282595837","https://openalex.org/W4312227733","https://openalex.org/W4366523284","https://openalex.org/W4385453013"],"related_works":["https://openalex.org/W2483195039","https://openalex.org/W2048211457","https://openalex.org/W2281797687","https://openalex.org/W2358411735","https://openalex.org/W4250650723","https://openalex.org/W2611761074","https://openalex.org/W2358598243","https://openalex.org/W2331534598","https://openalex.org/W2313632216","https://openalex.org/W2964954556"],"abstract_inverted_index":{"Fracture":[0],"weaknesses":[1,46],"represent":[2],"two":[3],"critical":[4],"elastic":[5],"parameters":[6],"utilized":[7],"for":[8],"characterizing":[9],"fracture":[10,45,122],"properties":[11,123],"in":[12,32,47,86],"naturally":[13],"fractured":[14,33],"reservoirs.":[15],"Given":[16],"the":[17,37,76,104,131,140,148,157,175,179,184,191,195,203],"intricate":[18],"seismic":[19,42,98,108,127],"attributes":[20],"associated":[21,133],"with":[22,25,91,134],"amplitude":[23],"variation":[24],"angles":[26],"of":[27,95,106,151,159,170,194,207],"incidence":[28],"and":[29,44,80,97,162,172,178,205],"azimuth":[30],"(AVAZ)":[31],"reservoir,":[34],"accurately":[35],"delineating":[36],"mapping":[38],"relationships":[39],"between":[40,75,174],"azimuthal":[41,77,125],"data":[43],"analytic":[48],"form":[49],"poses":[50],"a":[51,58,68,92,111,168],"significant":[52],"challenge.":[53],"Leveraging":[54],"neural":[55,113,135],"networks":[56],"offers":[57],"nonlinear":[59],"mechanism":[60],"to":[61,118,146,189],"bridge":[62],"this":[63],"gap.":[64],"Initially,":[65],"we":[66,138,155],"establish":[67],"forward":[69],"model":[70,152,177],"by":[71],"employing":[72],"convolution":[73,112],"operations":[74],"PP-wave":[78],"(incident":[79],"reflected":[81],"P-wave)":[82],"reflection":[83],"coefficient":[84],"equation":[85],"transversely":[87],"isotropic":[88],"(HTI)":[89],"media":[90],"horizontal":[93],"axis":[94],"symmetry":[96],"statistical":[99],"wavelets.":[100],"This":[101],"foundation":[102],"enables":[103],"synthesis":[105],"azimuth-dependent":[107],"data.":[109,128,164],"Subsequently,":[110],"network":[114,136],"(CNN)":[115],"is":[116],"constructed":[117],"predict":[119],"subsurface":[120],"rock":[121],"from":[124],"pre-stack":[126],"To":[129],"quantify":[130],"uncertainty":[132],"estimation,":[137],"employ":[139],"approximate":[141],"Bayesian":[142],"computation":[143],"(ABC)":[144],"method":[145],"determine":[147],"posterior":[149,186],"distribution":[150,187],"parameters.":[153],"Finally,":[154],"present":[156],"application":[158],"both":[160],"synthetic":[161,176],"filed":[163],"Our":[165],"results":[166],"indicate":[167],"correlation":[169],"90%":[171],"86.8%":[173],"blind":[180],"well,":[181],"respectively.":[182],"Furthermore,":[183],"estimated":[185],"serves":[188],"validate":[190],"constraint":[192],"capability":[193],"proposed":[196],"method,":[197],"thereby":[198],"furnishing":[199],"comprehensive":[200],"evidence":[201],"supporting":[202],"feasibility":[204],"robustness":[206],"our":[208],"approach.":[209]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}