{"id":"https://openalex.org/W4289950753","doi":"https://doi.org/10.1109/lgrs.2022.3187020","title":"A Surface-Wave Inversion Method Based on FHLV Loss Function in LSTM","display_name":"A Surface-Wave Inversion Method Based on FHLV Loss Function in LSTM","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4289950753","doi":"https://doi.org/10.1109/lgrs.2022.3187020"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2022.3187020","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3187020","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/A5049370516","display_name":"Yinghe Wu","orcid":"https://orcid.org/0000-0002-8350-2190"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinghe Wu","raw_affiliation_strings":["School of Geoscience and Technology, Southwest Petroleum University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-8350-2190","affiliations":[{"raw_affiliation_string":"School of Geoscience and Technology, Southwest Petroleum University, Chengdu, China","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003004095","display_name":"Shulin Pan","orcid":"https://orcid.org/0000-0003-0514-7274"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]},{"id":"https://openalex.org/I4210098205","display_name":"State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation","ror":"https://ror.org/00ftbmy59","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210098205"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shulin Pan","raw_affiliation_strings":["State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation and the School of Geoscience and Technology, Southwest Petroleum University, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0003-0514-7274","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation and the School of Geoscience and Technology, Southwest Petroleum University, Chengdu, China","institution_ids":["https://openalex.org/I165745306","https://openalex.org/I4210098205"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100350600","display_name":"Jingyi Chen","orcid":"https://orcid.org/0000-0002-4072-3567"},"institutions":[{"id":"https://openalex.org/I87208437","display_name":"University of Tulsa","ror":"https://ror.org/04wn28048","country_code":"US","type":"education","lineage":["https://openalex.org/I87208437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jingyi Chen","raw_affiliation_strings":["Department of Geosciences, Seismic Anisotropy Group, University of Tulsa, Tulsa, OK, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Geosciences, Seismic Anisotropy Group, University of Tulsa, Tulsa, OK, USA","institution_ids":["https://openalex.org/I87208437"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070612539","display_name":"Guojie Song","orcid":"https://orcid.org/0000-0001-7691-1933"},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guojie Song","raw_affiliation_strings":["School of Science, Southwest Petroleum University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science, Southwest Petroleum University, Chengdu, China","institution_ids":["https://openalex.org/I165745306"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005631492","display_name":"Qiyong Gou","orcid":null},"institutions":[{"id":"https://openalex.org/I4387156274","display_name":"PetroChina Southwest Oil and Gas Field Company (China)","ror":"https://ror.org/02j69wt57","country_code":null,"type":"company","lineage":["https://openalex.org/I4387156274","https://openalex.org/I98227222"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiyong Gou","raw_affiliation_strings":["Shale Gas Research Institute, Petro China Southwest Oil and Gasfield Company, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shale Gas Research Institute, Petro China Southwest Oil and Gasfield Company, Chengdu, China","institution_ids":["https://openalex.org/I4387156274"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.9075,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.83697642,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11757","display_name":"Seismic Waves and Analysis","score":1.0,"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/T11757","display_name":"Seismic Waves and Analysis","score":1.0,"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":0.9962999820709229,"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/T13885","display_name":"Geophysics and Sensor Technology","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/inversion","display_name":"Inversion (geology)","score":0.798679769039154},{"id":"https://openalex.org/keywords/surface-wave","display_name":"Surface wave","score":0.7572519779205322},{"id":"https://openalex.org/keywords/group-velocity","display_name":"Group velocity","score":0.4659724235534668},{"id":"https://openalex.org/keywords/inverse-transform-sampling","display_name":"Inverse transform sampling","score":0.4446752071380615},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39998626708984375},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.373573362827301},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.36522185802459717},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.21781319379806519},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.21735715866088867},{"id":"https://openalex.org/keywords/seismology","display_name":"Seismology","score":0.11878469586372375},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.09250408411026001}],"concepts":[{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.798679769039154},{"id":"https://openalex.org/C84174578","wikidata":"https://www.wikidata.org/wiki/Q889796","display_name":"Surface wave","level":2,"score":0.7572519779205322},{"id":"https://openalex.org/C181341577","wikidata":"https://www.wikidata.org/wiki/Q217361","display_name":"Group velocity","level":2,"score":0.4659724235534668},{"id":"https://openalex.org/C143606050","wikidata":"https://www.wikidata.org/wiki/Q1377019","display_name":"Inverse transform sampling","level":3,"score":0.4446752071380615},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39998626708984375},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.373573362827301},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.36522185802459717},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.21781319379806519},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.21735715866088867},{"id":"https://openalex.org/C165205528","wikidata":"https://www.wikidata.org/wiki/Q83371","display_name":"Seismology","level":1,"score":0.11878469586372375},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.09250408411026001},{"id":"https://openalex.org/C77928131","wikidata":"https://www.wikidata.org/wiki/Q193343","display_name":"Tectonics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2022.3187020","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3187020","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2008732286","https://openalex.org/W2048712940","https://openalex.org/W2064675550","https://openalex.org/W2091091226","https://openalex.org/W2102070657","https://openalex.org/W2133526188","https://openalex.org/W2143657160","https://openalex.org/W2505927162","https://openalex.org/W2753010592","https://openalex.org/W2768009915","https://openalex.org/W2781854221","https://openalex.org/W2986164432","https://openalex.org/W3004464931","https://openalex.org/W3008444343","https://openalex.org/W3029017923","https://openalex.org/W3083145699","https://openalex.org/W3090646150","https://openalex.org/W3159991720","https://openalex.org/W3164050102","https://openalex.org/W3201954468"],"related_works":["https://openalex.org/W2044527509","https://openalex.org/W3151319332","https://openalex.org/W3093557447","https://openalex.org/W2377084220","https://openalex.org/W4293147659","https://openalex.org/W2378498423","https://openalex.org/W1981223537","https://openalex.org/W4308342250","https://openalex.org/W2390674751","https://openalex.org/W4285327398"],"abstract_inverted_index":{"Surface-wave":[0],"analysis":[1],"methods":[2,59],"have":[3,54],"been":[4],"widely":[5],"applied":[6],"to":[7,37],"construct":[8],"near-surface":[9,23,167],"shear-wave":[10,24,168],"velocity":[11,25,84,169],"structures.":[12],"Whether":[13],"it":[14],"is":[15,27,94],"an":[16],"active":[17],"source":[18],"or":[19],"passive":[20],"source,":[21],"the":[22,31,39,56,80,95,112,118,122,126,130,133,141,166],"structure":[26],"obtained":[28],"by":[29,125],"inverting":[30],"surface-wave":[32,51,57,75,136],"dispersion":[33],"curve.":[34],"In":[35,65],"order":[36],"solve":[38],"problems":[40],"of":[41,90,100,121,132],"low":[42],"inversion":[43,47,58,76,137],"efficiency":[44],"and":[45,106,151,163],"poor":[46],"results":[48,155],"in":[49],"traditional":[50],"exploration,":[52],"we":[53,68],"studied":[55],"based":[60,78,139],"on":[61,79,140,148],"deep":[62],"learning":[63,119],"technology.":[64],"this":[66],"study,":[67],"propose":[69],"a":[70,103,107],"long":[71],"short-term":[72],"memory":[73],"(LSTM)":[74],"method":[77,93,138,160],"first":[81],"height":[82],"last":[83],"(FHLV)":[85],"loss":[86,97,105,143],"function.":[87],"The":[88,154],"core":[89],"our":[91,158],"proposed":[92,134,159],"FHLV":[96,142],"function":[98],"consisting":[99],"two":[101],"parts:":[102],"speed":[104],"thickness":[108,123],"loss,":[109],"which":[110],"improves":[111],"overall":[113],"prediction":[114],"accuracy":[115,131],"through":[116],"optimizing":[117],"process":[120],"parameter":[124],"network.":[127],"To":[128],"verify":[129],"LSTM":[135],"function,":[144],"experiments":[145],"are":[146],"conducted":[147],"both":[149],"synthetic":[150],"real":[152],"datasets.":[153],"show":[156],"that":[157],"can":[161],"efficiently":[162],"accurately":[164],"invert":[165],"structure.":[170]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}