{"id":"https://openalex.org/W4401880340","doi":"https://doi.org/10.1109/tgrs.2024.3449279","title":"A Random Medium Modeling Method Based on Wasserstein Convolutional Generative Adversarial Networks and Velocity Mask","display_name":"A Random Medium Modeling Method Based on Wasserstein Convolutional Generative Adversarial Networks and Velocity Mask","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4401880340","doi":"https://doi.org/10.1109/tgrs.2024.3449279"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3449279","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tgrs.2024.3449279","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":"https://openalex.org/A5053686628","display_name":"Jiahao Liu","orcid":"https://orcid.org/0009-0007-4445-1195"},"institutions":[{"id":"https://openalex.org/I2802497816","display_name":"Chinese Academy of Geological Sciences","ror":"https://ror.org/02gp4e279","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802497816"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiahao Liu","raw_affiliation_strings":["Chinese Academy of Geological Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0007-4445-1195","affiliations":[{"raw_affiliation_string":"Chinese Academy of Geological Sciences, Beijing, China","institution_ids":["https://openalex.org/I2802497816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080795238","display_name":"Jiayong Yan","orcid":"https://orcid.org/0000-0002-7787-5572"},"institutions":[{"id":"https://openalex.org/I2802497816","display_name":"Chinese Academy of Geological Sciences","ror":"https://ror.org/02gp4e279","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802497816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiayong Yan","raw_affiliation_strings":["Chinese Academy of Geological Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7787-5572","affiliations":[{"raw_affiliation_string":"Chinese Academy of Geological Sciences, Beijing, China","institution_ids":["https://openalex.org/I2802497816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009938927","display_name":"Jianguang Han","orcid":"https://orcid.org/0000-0003-4235-3130"},"institutions":[{"id":"https://openalex.org/I2802497816","display_name":"Chinese Academy of Geological Sciences","ror":"https://ror.org/02gp4e279","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802497816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianguang Han","raw_affiliation_strings":["Chinese Academy of Geological Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4235-3130","affiliations":[{"raw_affiliation_string":"Chinese Academy of Geological Sciences, Beijing, China","institution_ids":["https://openalex.org/I2802497816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101917696","display_name":"Changxin Chen","orcid":"https://orcid.org/0000-0003-1468-5920"},"institutions":[{"id":"https://openalex.org/I2802497816","display_name":"Chinese Academy of Geological Sciences","ror":"https://ror.org/02gp4e279","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802497816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changxin Chen","raw_affiliation_strings":["Chinese Academy of Geological Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Geological Sciences, Beijing, China","institution_ids":["https://openalex.org/I2802497816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115604225","display_name":"Xu Wang","orcid":"https://orcid.org/0009-0007-3958-3900"},"institutions":[{"id":"https://openalex.org/I2802497816","display_name":"Chinese Academy of Geological Sciences","ror":"https://ror.org/02gp4e279","country_code":"CN","type":"facility","lineage":["https://openalex.org/I2802497816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Wang","raw_affiliation_strings":["Chinese Academy of Geological Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Geological Sciences, Beijing, China","institution_ids":["https://openalex.org/I2802497816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100772492","display_name":"Chen Miao","orcid":"https://orcid.org/0000-0002-2769-7034"},"institutions":[{"id":"https://openalex.org/I3125743391","display_name":"China University of Geosciences (Beijing)","ror":"https://ror.org/04q6c7p66","country_code":"CN","type":"education","lineage":["https://openalex.org/I3125743391"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Miao Chen","raw_affiliation_strings":["School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Resources, China University of Geosciences (Beijing), Beijing, China","institution_ids":["https://openalex.org/I3125743391"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5053686628"],"corresponding_institution_ids":["https://openalex.org/I2802497816"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12662354,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9970999956130981,"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"}},"topics":[{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9970999956130981,"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"}},{"id":"https://openalex.org/T11105","display_name":"Advanced Image Processing Techniques","score":0.9965999722480774,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9951000213623047,"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/computer-science","display_name":"Computer science","score":0.6610690951347351},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.6021445393562317},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5671018958091736},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4295181632041931},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.42495957016944885},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4206307530403137},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36633622646331787},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.25667837262153625}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6610690951347351},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.6021445393562317},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5671018958091736},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4295181632041931},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.42495957016944885},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4206307530403137},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36633622646331787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25667837262153625}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2024.3449279","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/tgrs.2024.3449279","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":[{"id":"https://openalex.org/G1570693489","display_name":null,"funder_award_id":"U2344220","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W224589444","https://openalex.org/W577824090","https://openalex.org/W1580389772","https://openalex.org/W1639961155","https://openalex.org/W1969696814","https://openalex.org/W2015620003","https://openalex.org/W2034477916","https://openalex.org/W2034927875","https://openalex.org/W2054367970","https://openalex.org/W2076304753","https://openalex.org/W2089443104","https://openalex.org/W2103504761","https://openalex.org/W2106794296","https://openalex.org/W2125389028","https://openalex.org/W2128852798","https://openalex.org/W2160556601","https://openalex.org/W2165894314","https://openalex.org/W2166260557","https://openalex.org/W2239896818","https://openalex.org/W2738087101","https://openalex.org/W2786668732","https://openalex.org/W2948893225","https://openalex.org/W2949265864","https://openalex.org/W2950776302","https://openalex.org/W2963185411","https://openalex.org/W2963684088","https://openalex.org/W2987357275","https://openalex.org/W3024352275","https://openalex.org/W3036800407","https://openalex.org/W3095036472","https://openalex.org/W3109916085","https://openalex.org/W3153319639","https://openalex.org/W3195250328","https://openalex.org/W3198590184","https://openalex.org/W4238805501","https://openalex.org/W4285093386","https://openalex.org/W4293298413","https://openalex.org/W4294643831","https://openalex.org/W4298289240","https://openalex.org/W4300426975","https://openalex.org/W4312407964","https://openalex.org/W4321103827","https://openalex.org/W4322576542","https://openalex.org/W4365800965","https://openalex.org/W4386241849","https://openalex.org/W4389104812","https://openalex.org/W4396974100","https://openalex.org/W6637568146","https://openalex.org/W6677995690","https://openalex.org/W6678815747","https://openalex.org/W6729482032","https://openalex.org/W6735913928","https://openalex.org/W6745560452","https://openalex.org/W6748143387","https://openalex.org/W6748294366","https://openalex.org/W6748690695","https://openalex.org/W6779669310"],"related_works":["https://openalex.org/W2502115930","https://openalex.org/W2888032422","https://openalex.org/W2996316059","https://openalex.org/W4385421777","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2845413374","https://openalex.org/W3005996785","https://openalex.org/W4297411772","https://openalex.org/W4235873501"],"abstract_inverted_index":{"Seismic":[0],"forward":[1,19],"modeling":[2,68,94,192,238],"is":[3,63,110,218],"crucial":[4],"for":[5],"exploration":[6,48],"geophysics,":[7],"especially":[8],"in":[9,204],"seismic":[10],"exploration.":[11],"As":[12],"research":[13],"has":[14],"progressed,":[15],"the":[16,34,40,66,116,136,141,144,170,180,199,205],"limitations":[17],"of":[18,36,43,72,182,201,236],"models":[20,29],"assuming":[21],"homogeneous":[22],"media":[23,85,118,226],"have":[24],"become":[25],"increasingly":[26],"evident.":[27],"These":[28],"no":[30],"longer":[31],"adequately":[32],"address":[33],"complexities":[35],"real-world":[37],"scenarios.":[38],"Furthermore,":[39],"multiscale":[41,165,224],"heterogeneity":[42,62],"subsurface":[44,232],"structures":[45],"significantly":[46],"influences":[47],"outcomes":[49],"and":[50,75,104,115,130,146,164,179,240],"geological":[51,209],"interpretations.":[52],"Therefore,":[53,87,211],"constructing":[54],"a":[55,91,123,222],"random":[56,84,92,117,184,225],"medium":[57,93],"model":[58],"that":[59,96,173],"accurately":[60],"reflects":[61],"necessary.":[64],"However,":[65],"current":[67],"methods":[69],"face":[70],"issues":[71,235],"low":[73],"efficiency":[74],"insufficient":[76],"flexibility,":[77],"making":[78],"it":[79,217],"difficult":[80],"to":[81,135,139,220],"construct":[82],"complex":[83,208],"models.":[86,186],"this":[88,174],"article":[89],"proposes":[90],"method":[95,175],"combines":[97],"Wasserstein":[98,128,167],"convolutional":[99,151],"generative":[100],"adversarial":[101],"networks":[102],"(WCGANs)":[103],"velocity":[105],"masking.":[106],"A":[107],"training":[108,177],"dataset":[109],"constructed":[111],"through":[112,157],"spectral":[113],"decomposition,":[114],"parameters":[119],"are":[120,133,148],"constrained":[121],"into":[122],"composite":[124],"conditional":[125],"vector.":[126],"The":[127],"distance":[129,168],"gradient":[131],"penalty":[132],"added":[134],"loss":[137],"function":[138],"train":[140],"networks.":[142],"Also,":[143],"generator":[145],"discriminator":[147],"improved":[149],"using":[150,188],"network":[152],"structures.":[153,210],"After":[154],"comprehensive":[155],"evaluation":[156],"visual":[158],"inspection,":[159],"principal":[160],"component":[161],"analysis":[162],"(PCA),":[163],"sliced":[166],"(MS-SWD),":[169],"results":[171],"show":[172],"improves":[176],"stability":[178],"diversity":[181],"generated":[183],"perturbation":[185],"Consequently,":[187],"WCGANs":[189],"can":[190,197],"improve":[191],"efficiency,":[193],"while":[194],"masking":[195],"techniques":[196],"control":[198],"characteristics":[200],"different":[202],"regions":[203],"model,":[206],"simulating":[207],"by":[212],"combining":[213],"these":[214],"two":[215],"methods,":[216],"possible":[219],"establish":[221],"regional":[223],"model.":[227],"This":[228],"approach":[229],"efficiently":[230],"simulates":[231],"heterogeneity,":[233],"addressing":[234],"long":[237],"times":[239],"inflexibility.":[241]},"counts_by_year":[],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
