{"id":"https://openalex.org/W7105652778","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228859","title":"IRGV: An Interpretable Reinforced Graph Contrastive Framework for Diagenetic Facies Identification","display_name":"IRGV: An Interpretable Reinforced Graph Contrastive Framework for Diagenetic Facies Identification","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W7105652778","doi":"https://doi.org/10.1109/ijcnn64981.2025.11228859"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11228859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 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":null,"display_name":"Liying Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Liying Zhang","raw_affiliation_strings":["China University of Petroleum (Beijing),College of Artificial Intelligence"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum (Beijing),College of Artificial Intelligence","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jingyu He","orcid":null},"institutions":[{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]},{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyu He","raw_affiliation_strings":["China University of Petroleum (Beijing),College of Artificial Intelligence"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum (Beijing),College of Artificial Intelligence","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I204553293"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Deshuai Mu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I204553293","display_name":"China University of Petroleum, Beijing","ror":"https://ror.org/041qf4r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I204553293"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deshuai Mu","raw_affiliation_strings":["China University of Petroleum (Beijing),College of Artificial Intelligence"],"affiliations":[{"raw_affiliation_string":"China University of Petroleum (Beijing),College of Artificial Intelligence","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I204553293"]}]},{"author_position":"last","author":{"id":null,"display_name":"Zhiguo Mao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210112595","display_name":"Research Institute of Petroleum Exploration and Development","ror":"https://ror.org/02awe6g05","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210112595","https://openalex.org/I98227222"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiguo Mao","raw_affiliation_strings":["Research Institute of Petroleum Exploration and Development,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Research Institute of Petroleum Exploration and Development,Beijing,China","institution_ids":["https://openalex.org/I4210112595"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I204553293","https://openalex.org/I4210100255"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.79489888,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.47519999742507935,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.47519999742507935,"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/T10399","display_name":"Hydrocarbon exploration and reservoir analysis","score":0.2678999900817871,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.04410000145435333,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8366000056266785},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5978999733924866},{"id":"https://openalex.org/keywords/diagenesis","display_name":"Diagenesis","score":0.5929999947547913},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5830000042915344},{"id":"https://openalex.org/keywords/facies","display_name":"Facies","score":0.5475999712944031},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5353000164031982},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4542999863624573},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44749999046325684}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8366000056266785},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5978999733924866},{"id":"https://openalex.org/C130452526","wikidata":"https://www.wikidata.org/wiki/Q180621","display_name":"Diagenesis","level":2,"score":0.5929999947547913},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5830000042915344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5669000148773193},{"id":"https://openalex.org/C146588470","wikidata":"https://www.wikidata.org/wiki/Q742139","display_name":"Facies","level":3,"score":0.5475999712944031},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.54339998960495},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5353000164031982},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4542999863624573},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44749999046325684},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.36489999294281006},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34599998593330383},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.3433000147342682},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.336899995803833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3343999981880188},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3215000033378601},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.27459999918937683},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C22047676","wikidata":"https://www.wikidata.org/wiki/Q898680","display_name":"Clustering coefficient","level":3,"score":0.2676999866962433}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11228859","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11228859","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5419967770576477,"display_name":"Peace, Justice and strong institutions"},{"id":"https://metadata.un.org/sdg/10","score":0.40460261702537537,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2963626623","https://openalex.org/W3004946360","https://openalex.org/W3017713704","https://openalex.org/W4205473129","https://openalex.org/W4225416936","https://openalex.org/W4226219101","https://openalex.org/W4288073539","https://openalex.org/W4296534231","https://openalex.org/W4308293107","https://openalex.org/W4382203265","https://openalex.org/W4388193048","https://openalex.org/W4391470255","https://openalex.org/W4399618125","https://openalex.org/W4400322756","https://openalex.org/W4410508943"],"related_works":[],"abstract_inverted_index":{"Diagenetic":[0],"facies":[1,67],"identification":[2,68],"plays":[3],"an":[4,13],"indispensable":[5],"role":[6],"in":[7,58,65],"geology":[8],"and":[9,15,61,115,141,150],"petroleum":[10],"exploration,":[11],"providing":[12],"objective":[14],"continuous":[16],"approach":[17],"for":[18,81,139],"observing":[19],"subsurface":[20],"diagenetic":[21,45,66],"facies.":[22,46],"Deep":[23],"graph":[24,95,105,158],"clustering":[25,140,159,167],"networks":[26],"can":[27],"extract":[28],"high-level":[29],"feature":[30,99],"representations":[31],"from":[32],"well-logging":[33,37,90,170],"data,":[34],"thereby":[35],"mapping":[36],"data":[38,91],"to":[39,92],"the":[40,73,79,86,98,111,123,128,147],"global":[41],"distribution":[42],"information":[43],"of":[44,63,89,127],"However,":[47],"existing":[48,166],"methods":[49],"face":[50],"limitations":[51],"such":[52],"as":[53,78,135],"poor":[54],"discrimination":[55],"capability,":[56],"difficulty":[57],"parameter":[59],"tuning,":[60],"lack":[62],"interpretability":[64],"tasks.":[69],"Therefore,":[70],"we":[71,102,131],"adopt":[72],"universal":[74],"gravitation":[75],"between":[76],"nodes":[77],"metric":[80],"determining":[82],"neighbors,":[83],"which":[84,120],"leverages":[85],"structural":[87],"features":[88],"construct":[93],"higher-quality":[94],"data.":[96,171],"In":[97],"extraction":[100],"process,":[101],"design":[103],"a":[104,116,136],"contrastive":[106],"learning":[107,125,134],"framework":[108],"based":[109],"on":[110,169],"KAN":[112],"neural":[113],"network":[114],"dual-information":[117],"loss":[118],"function,":[119],"significantly":[121],"enhances":[122],"representation":[124],"ability":[126],"model.":[129],"Additionally,":[130],"incorporate":[132],"reinforcement":[133],"guiding":[137],"module":[138],"decision":[142],"tree":[143],"generation,":[144],"synchronously":[145],"improving":[146],"model\u2019s":[148],"adaptability":[149],"interpretability.":[151],"Extensive":[152],"comparative":[153],"experiments":[154],"with":[155],"four":[156],"advanced":[157],"models":[160],"demonstrate":[161],"that":[162],"our":[163],"method":[164],"outperforms":[165],"approaches":[168]},"counts_by_year":[],"updated_date":"2025-11-15T23:13:30.683059","created_date":"2025-11-14T00:00:00"}