{"id":"https://openalex.org/W7110412356","doi":"https://doi.org/10.1109/tgrs.2025.3641873","title":"Deep Learning-Driven Subsurface Characterization for Affordable CO <sub>2</sub> and Reservoir Monitoring","display_name":"Deep Learning-Driven Subsurface Characterization for Affordable CO <sub>2</sub> and Reservoir Monitoring","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7110412356","doi":"https://doi.org/10.1109/tgrs.2025.3641873"},"language":null,"primary_location":{"id":"doi:10.1109/tgrs.2025.3641873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3641873","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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":null,"display_name":"Son Phan","orcid":"https://orcid.org/0000-0001-5023-5228"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Son Phan","raw_affiliation_strings":["SLB, Houston, TX, USA"],"raw_orcid":"https://orcid.org/0000-0001-5023-5228","affiliations":[{"raw_affiliation_string":"SLB, Houston, TX, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wenyi Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenyi Hu","raw_affiliation_strings":["SLB, Houston, TX, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SLB, Houston, TX, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":null,"display_name":"Aria Abubakar","orcid":"https://orcid.org/0000-0003-4025-3443"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aria Abubakar","raw_affiliation_strings":["SLB, Houston, TX, USA"],"raw_orcid":"https://orcid.org/0000-0003-4025-3443","affiliations":[{"raw_affiliation_string":"SLB, Houston, TX, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.5893123,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.8615999817848206,"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":0.8615999817848206,"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/T11302","display_name":"CO2 Sequestration and Geologic Interactions","score":0.053599998354911804,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13018","display_name":"Seismology and Earthquake Studies","score":0.03020000085234642,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4569999873638153},{"id":"https://openalex.org/keywords/data-processing","display_name":"Data processing","score":0.435699999332428},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4259999990463257},{"id":"https://openalex.org/keywords/repeatability","display_name":"Repeatability","score":0.41819998621940613},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.36079999804496765},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.34850001335144043},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.3343999981880188},{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.33160001039505005}],"concepts":[{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4569999873638153},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4383000135421753},{"id":"https://openalex.org/C138827492","wikidata":"https://www.wikidata.org/wiki/Q6661985","display_name":"Data processing","level":2,"score":0.435699999332428},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C154020017","wikidata":"https://www.wikidata.org/wiki/Q520171","display_name":"Repeatability","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.4041999876499176},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3961000144481659},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.34940001368522644},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.34850001335144043},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.33160001039505005},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3091999888420105},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C39267094","wikidata":"https://www.wikidata.org/wiki/Q7446968","display_name":"Seismic inversion","level":3,"score":0.3009999990463257},{"id":"https://openalex.org/C14641988","wikidata":"https://www.wikidata.org/wiki/Q7315329","display_name":"Reservoir modeling","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C46293882","wikidata":"https://www.wikidata.org/wiki/Q2080707","display_name":"Petrophysics","level":3,"score":0.2964000105857849},{"id":"https://openalex.org/C161028810","wikidata":"https://www.wikidata.org/wiki/Q2138304","display_name":"Regional geology","level":4,"score":0.28679999709129333},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.25949999690055847}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3641873","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3641873","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2054304620","https://openalex.org/W2139393389","https://openalex.org/W2145811902","https://openalex.org/W2313580270","https://openalex.org/W2590039535","https://openalex.org/W2963775778","https://openalex.org/W2968735721","https://openalex.org/W3029050805","https://openalex.org/W3042713562","https://openalex.org/W3091507481","https://openalex.org/W3124257786","https://openalex.org/W3131408961","https://openalex.org/W3155993404","https://openalex.org/W4206379710","https://openalex.org/W4206602135","https://openalex.org/W4239149925","https://openalex.org/W4249934370","https://openalex.org/W4302010873","https://openalex.org/W4391560269","https://openalex.org/W4400865985","https://openalex.org/W4404058581","https://openalex.org/W4404843219"],"related_works":[],"abstract_inverted_index":{"Time-lapse":[0],"seismic":[1,40,64,75,80,135,171],"is":[2,147],"one":[3],"of":[4,38,52,238],"the":[5,33,58,72,96,134,151,160,170,181,224,229,235,244],"most":[6],"effective":[7],"tools":[8],"for":[9,49,126,164],"monitoring":[10,110,200],"subsurface":[11,53,67,97],"processes":[12],"or":[13],"property":[14,54,68,113,225],"changes":[15,55,99,217],"caused":[16],"by":[17,56,102,203],"hydrocarbon":[18],"production,":[19],"CO<sub":[20],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[21,205],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sub>":[22],"sequestration,":[23],"geothermal":[24],"development,":[25],"and":[26,35,83,106,199,215,247],"many":[27],"other":[28],"activities.":[29],"To":[30],"substantially":[31],"reduce":[32],"cost":[34],"turnaround":[36],"time":[37],"time-lapse":[39,161],"data":[41,65,81,162,201,246],"processing":[42,76],"projects,":[43],"we":[44,173,233],"developed":[45,174],"a":[46,118,139,144,188],"deep-learning":[47,89,177,240],"network":[48,90,115,178,186,227],"rapid":[50],"characterization":[51],"establishing":[57],"direct":[59],"nonlinear":[60],"mapping":[61],"from":[62,130,169],"premigration":[63],"to":[66,93,149,157,242],"models.":[69],"By":[70,222],"bypassing":[71],"time-consuming":[73],"conventional":[74],"procedures,":[77],"such":[78,208],"as":[79,180,209],"migration":[82],"full":[84],"waveform":[85],"inversion":[86],"(FWI),":[87],"this":[88,239],"enables":[91],"us":[92],"quantitatively":[94],"estimate":[95],"condition":[98],"almost":[100],"instantaneously":[101],"efficiently":[103],"scanning,":[104],"selecting,":[105],"analyzing":[107],"any":[108],"new":[109],"datasets.":[111],"This":[112,185],"estimation":[114,226],"architecture":[116],"features":[117,187],"multibranch":[119],"design":[120],"with":[121,143,228],"different":[122],"convolutional":[123],"filtering":[124],"sizes":[125],"better":[127],"feature":[128],"extraction":[129,168],"dipping":[131],"events":[132],"within":[133],"gathers.":[136],"In":[137],"addition,":[138],"customized":[140],"loss":[141],"function":[142],"weighted":[145],"term":[146],"used":[148],"address":[150],"imbalanced":[152],"training":[153],"label":[154],"issue.":[155],"Furthermore,":[156],"effectively":[158],"suppress":[159],"nonrepeatability":[163],"accurate":[165],"timelapse":[166],"response":[167],"data,":[172],"an":[175],"additional":[176],"known":[179],"repeatability":[182,230],"enforcement":[183,231],"network.":[184],"specially":[189],"designed":[190],"learning":[191],"strategy":[192],"aimed":[193],"at":[194],"eliminating":[195],"differences":[196],"between":[197],"baseline":[198],"induced":[202],"non-CO<sub":[204],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sub>-related":[206],"factors,":[207],"variations":[210],"in":[211,218],"bandwidth,":[212],"source":[213],"signature,":[214],"seasonal":[216],"near-surface":[219],"conditions,":[220],"etc.":[221],"combining":[223],"network,":[232],"demonstrate":[234],"successful":[236],"application":[237],"approach":[241],"both":[243],"Sleipner":[245],"Chimera":[248],"data.":[249]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-08T00:00:00"}