{"id":"https://openalex.org/W3205940985","doi":"https://doi.org/10.1109/igarss47720.2021.9554771","title":"Kerogen Type Classification in Hydrocarbon Source Rocks Using Hyperspectral Data and Machine Learning","display_name":"Kerogen Type Classification in Hydrocarbon Source Rocks Using Hyperspectral Data and Machine Learning","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3205940985","doi":"https://doi.org/10.1109/igarss47720.2021.9554771","mag":"3205940985"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","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/A5023399686","display_name":"Tain\u00e1 Thomassim Guimar\u00e3es","orcid":"https://orcid.org/0000-0002-6362-6591"},"institutions":[{"id":"https://openalex.org/I61722147","display_name":"Universidade do Vale do Rio dos Sinos","ror":"https://ror.org/05ctmmy43","country_code":"BR","type":"education","lineage":["https://openalex.org/I61722147"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Taina Thomassim Guimaraes","raw_affiliation_strings":["Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil","institution_ids":["https://openalex.org/I61722147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046415913","display_name":"Lucas Silveira Kupssinsk\u00fc","orcid":"https://orcid.org/0000-0003-2580-3996"},"institutions":[{"id":"https://openalex.org/I61722147","display_name":"Universidade do Vale do Rio dos Sinos","ror":"https://ror.org/05ctmmy43","country_code":"BR","type":"education","lineage":["https://openalex.org/I61722147"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Lucas Silveira Kupssinsku","raw_affiliation_strings":["Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil","institution_ids":["https://openalex.org/I61722147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063445713","display_name":"Daniel Capella Zanotta","orcid":"https://orcid.org/0000-0003-2959-6525"},"institutions":[{"id":"https://openalex.org/I61722147","display_name":"Universidade do Vale do Rio dos Sinos","ror":"https://ror.org/05ctmmy43","country_code":"BR","type":"education","lineage":["https://openalex.org/I61722147"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Daniel Capella Zanotta","raw_affiliation_strings":["Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil","institution_ids":["https://openalex.org/I61722147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022860868","display_name":"Jo\u00e3o Gabriel Motta","orcid":"https://orcid.org/0000-0002-5056-4053"},"institutions":[{"id":"https://openalex.org/I61722147","display_name":"Universidade do Vale do Rio dos Sinos","ror":"https://ror.org/05ctmmy43","country_code":"BR","type":"education","lineage":["https://openalex.org/I61722147"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Joao Gabriel Motta","raw_affiliation_strings":["Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil","institution_ids":["https://openalex.org/I61722147"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021858326","display_name":"Andr\u00e9 Luiz Durante Spigolon","orcid":"https://orcid.org/0000-0002-0545-1244"},"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":"Andre Luiz Durante Spigolon","raw_affiliation_strings":["Petrobras Research and Development Center (CENPES), RJ, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Petrobras Research and Development Center (CENPES), RJ, Brazil","institution_ids":["https://openalex.org/I32393484"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020661409","display_name":"Luiz Gonzaga","orcid":"https://orcid.org/0000-0002-7661-2447"},"institutions":[{"id":"https://openalex.org/I61722147","display_name":"Universidade do Vale do Rio dos Sinos","ror":"https://ror.org/05ctmmy43","country_code":"BR","type":"education","lineage":["https://openalex.org/I61722147"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Luiz Gonzaga","raw_affiliation_strings":["Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil","institution_ids":["https://openalex.org/I61722147"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027303526","display_name":"Maur\u00edcio Roberto Veronez","orcid":"https://orcid.org/0000-0002-5914-3546"},"institutions":[{"id":"https://openalex.org/I61722147","display_name":"Universidade do Vale do Rio dos Sinos","ror":"https://ror.org/05ctmmy43","country_code":"BR","type":"education","lineage":["https://openalex.org/I61722147"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Mauricio Roberto Veronez","raw_affiliation_strings":["Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vizlab | X-Reality and Geoinformatics Lab - Graduate Program in Applied Computing, Unisinos University, S\u00e3o Leopoldo, RS, Brazil","institution_ids":["https://openalex.org/I61722147"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4198,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70177289,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3633","last_page":"3636"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9997000098228455,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9997000098228455,"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":0.9993000030517578,"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/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.9707000255584717,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kerogen","display_name":"Kerogen","score":0.8599317073822021},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8477323055267334},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6178115010261536},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5814785361289978},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.5181378126144409},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49138346314430237},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4455797076225281},{"id":"https://openalex.org/keywords/source-rock","display_name":"Source rock","score":0.4361845850944519},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3862481117248535},{"id":"https://openalex.org/keywords/mineralogy","display_name":"Mineralogy","score":0.37561991810798645},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.2834537625312805},{"id":"https://openalex.org/keywords/structural-basin","display_name":"Structural basin","score":0.13625243306159973}],"concepts":[{"id":"https://openalex.org/C2779196632","wikidata":"https://www.wikidata.org/wiki/Q938398","display_name":"Kerogen","level":4,"score":0.8599317073822021},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8477323055267334},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6178115010261536},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5814785361289978},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.5181378126144409},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49138346314430237},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4455797076225281},{"id":"https://openalex.org/C126559015","wikidata":"https://www.wikidata.org/wiki/Q1988844","display_name":"Source rock","level":3,"score":0.4361845850944519},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3862481117248535},{"id":"https://openalex.org/C199289684","wikidata":"https://www.wikidata.org/wiki/Q83353","display_name":"Mineralogy","level":1,"score":0.37561991810798645},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.2834537625312805},{"id":"https://openalex.org/C109007969","wikidata":"https://www.wikidata.org/wiki/Q749565","display_name":"Structural basin","level":2,"score":0.13625243306159973},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554771","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554771","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land","score":0.6600000262260437}],"awards":[{"id":"https://openalex.org/G2314765024","display_name":null,"funder_award_id":"4600583791","funder_id":"https://openalex.org/F4320323909","funder_display_name":"Ag\u00eancia Nacional do Petr\u00f3leo, G\u00e1s Natural e Biocombust\u00edveis"},{"id":"https://openalex.org/G4039110280","display_name":null,"funder_award_id":"4600556376","funder_id":"https://openalex.org/F4320322468","funder_display_name":"Petrobras"}],"funders":[{"id":"https://openalex.org/F4320322468","display_name":"Petrobras","ror":"https://ror.org/0235kyq22"},{"id":"https://openalex.org/F4320323909","display_name":"Ag\u00eancia Nacional do Petr\u00f3leo, G\u00e1s Natural e Biocombust\u00edveis","ror":"https://ror.org/00phthq42"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1568706965","https://openalex.org/W1606653878","https://openalex.org/W2019038438","https://openalex.org/W2218707633","https://openalex.org/W2334587834","https://openalex.org/W2750454890","https://openalex.org/W2785470087","https://openalex.org/W2953949196","https://openalex.org/W3138090701","https://openalex.org/W6636178699"],"related_works":["https://openalex.org/W998619573","https://openalex.org/W2810613610","https://openalex.org/W649782683","https://openalex.org/W2074794239","https://openalex.org/W2865456334","https://openalex.org/W2345316453","https://openalex.org/W2349413286","https://openalex.org/W2005248029","https://openalex.org/W2893315629","https://openalex.org/W2386844414"],"abstract_inverted_index":{"Kerogen":[0],"type":[1,34],"in":[2,64],"source":[3],"rocks":[4],"is":[5,15],"directly":[6],"related":[7],"to":[8,31],"its":[9],"hydrocarbon":[10,77],"generation":[11,78],"potential.":[12,79],"Its":[13],"determination":[14],"often":[16],"carried":[17],"out":[18],"with":[19,75,90,113],"destructive":[20],"methods.":[21],"This":[22],"study":[23],"presents":[24],"a":[25,91],"non-destructive":[26],"technique":[27],"as":[28],"an":[29,73],"alternative":[30],"determine":[32],"kerogen":[33,124],"using":[35,49,86],"hyperspectral":[36,100],"data":[37,62],"and":[38,56,84,93,115,119],"machine":[39],"learning":[40],"techniques.":[41],"To":[42],"present":[43],"the":[44,94,103,127,130],"technique,":[45],"models":[46,81],"were":[47,82,96],"training":[48],"Support":[50],"Vector":[51],"Machines,":[52],"K":[53],"Nearest":[54],"Neighbors,":[55],"Random":[57],"Forest":[58],"classifiers":[59],"on":[60,99],"spectral":[61,87],"collected":[63],"rock":[65],"samples":[66],"acquired":[67],"from":[68],"Taubat\u00e9":[69],"Basin,":[70],"Brazil,":[71],"of":[72,102,129],"outcrop":[74],"high":[76],"The":[80,105],"trained":[83],"evaluated":[85],"signatures":[88],"measured":[89],"spectroradiometer":[92],"results":[95],"also":[97],"tested":[98],"images":[101],"samples.":[104],"experiments":[106],"described":[107],"here":[108],"achieved":[109],"accuracy":[110],"above":[111,117],"0.8":[112],"precision":[114],"recall":[116],"0.62":[118],"0.8,":[120],"respectively,":[121],"for":[122],"every":[123],"type,":[125],"indicating":[126],"soundness":[128],"classification.":[131]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
