{"id":"https://openalex.org/W7153118749","doi":"https://doi.org/10.48550/arxiv.2604.07421","title":"SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion","display_name":"SPAMoE: Spectrum-Aware Hybrid Operator Framework for Full-Waveform Inversion","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7153118749","doi":"https://doi.org/10.48550/arxiv.2604.07421"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.07421","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07421","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.07421","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133368739","display_name":"Zhenyu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhenyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100777626","display_name":"Peiyuan Li","orcid":"https://orcid.org/0000-0003-4488-0532"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Peiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133324889","display_name":"Yongxiang Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Yongxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133326160","display_name":"Ruoyu Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Ruoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133345111","display_name":"Chenfei Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Chenfei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133352206","display_name":"Lei Zhang","orcid":"https://orcid.org/0000-0003-2343-084X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9591000080108643,"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":0.9591000080108643,"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/T11757","display_name":"Seismic Waves and Analysis","score":0.019300000742077827,"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/T13067","display_name":"Geological Modeling and Analysis","score":0.00430000014603138,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.6682999730110168},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.45570001006126404},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.44369998574256897},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.4212999939918518},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.41940000653266907},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.38179999589920044},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.36660000681877136}],"concepts":[{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.6682999730110168},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5371999740600586},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5109000205993652},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.45570001006126404},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.44369998574256897},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.4212999939918518},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.41940000653266907},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38179999589920044},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.36329999566078186},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3465000092983246},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.3199999928474426},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2969000041484833},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2750000059604645},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26980000734329224},{"id":"https://openalex.org/C121040770","wikidata":"https://www.wikidata.org/wiki/Q215675","display_name":"Quantum entanglement","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2678999900817871}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.07421","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07421","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.07421","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07421","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Full-waveform":[0],"inversion":[1],"(FWI)":[2],"is":[3],"pivotal":[4],"for":[5,58,150],"reconstructing":[6],"high-resolution":[7],"subsurface":[8],"velocity":[9],"models":[10],"but":[11],"remains":[12],"computationally":[13],"intensive":[14],"and":[15,28,91,103,119,156],"ill-posed.":[16],"While":[17],"deep":[18],"learning":[19],"approaches":[20],"promise":[21],"efficiency,":[22],"existing":[23],"Convolutional":[24],"Neural":[25,30],"Networks":[26],"(CNNs)":[27],"single-paradigm":[29],"Operators":[31],"(NOs)":[32],"struggle":[33],"with":[34,62],"one":[35],"fundamental":[36],"issue:":[37],"frequency":[38,81,109],"entanglement":[39],"of":[40,84],"multi-scale":[41,64],"geological":[42],"features.":[43],"To":[44],"address":[45],"this":[46],"challenge,":[47],"we":[48,97],"propose":[49],"Spectral-Preserving":[50,70],"Adaptive":[51],"MoE":[52],"(SPAMoE),":[53],"a":[54,69,75,99,112,146],"novel":[55,100],"spectrum-aware":[56],"framework":[57,149],"solving":[59],"inverse":[60],"problems":[61],"complex":[63],"structures.":[65],"Our":[66,154],"approach":[67],"introduces":[68],"DINO":[71],"Encoder":[72],"that":[73,106,128],"enforces":[74],"lower":[76],"bound":[77],"on":[78],"the":[79,85,122,131,138],"high-to-low":[80],"energy":[82],"ratio":[83],"encoded":[86],"representation,":[87],"mitigating":[88],"high-frequency":[89],"collapse":[90],"stabilizing":[92],"subsequent":[93],"frequency-domain":[94],"modeling.":[95],"Furthermore,":[96],"design":[98],"Spectral":[101],"Decomposition":[102],"Routing":[104],"mechanism":[105],"dynamically":[107],"assigns":[108],"bands":[110],"to":[111,137],"Mixture-of-Experts":[113],"(MoE)":[114],"ensemble":[115],"comprising":[116],"FNO,":[117],"MNO,":[118],"LNO.":[120],"On":[121],"ten":[123],"OpenFWI":[124,142],"sub-datasets,":[125],"experiments":[126],"show":[127],"SPAMoE":[129],"reduces":[130],"average":[132],"MAE":[133],"by":[134],"44.4%":[135],"relative":[136],"best":[139],"officially":[140],"reported":[141],"baseline,":[143],"thereby":[144],"establishing":[145],"new":[147],"architectural":[148],"learning-based":[151],"full-waveform":[152],"inversion.":[153],"code":[155],"data":[157],"are":[158],"available":[159],"at":[160],"https://github.com/zhenyuwang12366/SPAMoE":[161]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-11T00:00:00"}
