{"id":"https://openalex.org/W7151348521","doi":"https://doi.org/10.48550/arxiv.2604.04107","title":"Physical Sensitivity Kernels Can Emerge in Data-Driven Forward Models: Evidence From Surface-Wave Dispersion","display_name":"Physical Sensitivity Kernels Can Emerge in Data-Driven Forward Models: Evidence From Surface-Wave Dispersion","publication_year":2026,"publication_date":"2026-04-05","ids":{"openalex":"https://openalex.org/W7151348521","doi":"https://doi.org/10.48550/arxiv.2604.04107"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.04107","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04107","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.04107","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032644776","display_name":"Ziye Yu","orcid":"https://orcid.org/0000-0002-1720-3811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Ziye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121108630","display_name":"Yuqi Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Yuqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133075842","display_name":"XIN YI LIU","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T11757","display_name":"Seismic Waves and Analysis","score":0.6692000031471252,"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":0.6692000031471252,"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/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.11900000274181366,"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/T10572","display_name":"Geophysical and Geoelectrical Methods","score":0.0625,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.6725999712944031},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.6039000153541565},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5871000289916992},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.49070000648498535},{"id":"https://openalex.org/keywords/differential","display_name":"Differential (mechanical device)","score":0.42239999771118164},{"id":"https://openalex.org/keywords/inverse-problem","display_name":"Inverse problem","score":0.4075999855995178},{"id":"https://openalex.org/keywords/surrogate-data","display_name":"Surrogate data","score":0.39489999413490295},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.38029998540878296}],"concepts":[{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.6725999712944031},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.6039000153541565},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5871000289916992},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5080999732017517},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.49070000648498535},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43619999289512634},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.42239999771118164},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C142806159","wikidata":"https://www.wikidata.org/wiki/Q7646876","display_name":"Surrogate data","level":3,"score":0.39489999413490295},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.38029998540878296},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37389999628067017},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.366100013256073},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3346000015735626},{"id":"https://openalex.org/C116672817","wikidata":"https://www.wikidata.org/wiki/Q1454986","display_name":"Physical system","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32339999079704285},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.29910001158714294},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2870999872684479},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28630000352859497},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2630999982357025},{"id":"https://openalex.org/C177562468","wikidata":"https://www.wikidata.org/wiki/Q182893","display_name":"Dispersion (optics)","level":2,"score":0.25949999690055847}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.04107","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04107","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.04107","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04107","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Data-driven":[0],"neural":[1,75,114],"networks":[2],"are":[3],"increasingly":[4],"used":[5],"as":[6,87],"surrogate":[7,46,76],"forward":[8,115],"models":[9,77],"in":[10,98],"geophysics,":[11],"but":[12],"it":[13],"remains":[14,136],"unclear":[15],"whether":[16],"they":[17],"recover":[18,58,118],"only":[19],"the":[20,25,54,59,92,99,107,129],"data":[21],"mapping":[22],"or":[23],"also":[24],"underlying":[26],"physical":[27,64,120],"sensitivity":[28,49],"structure.":[29],"Here":[30],"we":[31,51],"test":[32],"this":[33,133],"question":[34],"using":[35],"surface-wave":[36],"dispersion.":[37],"By":[38],"comparing":[39],"automatically":[40],"differentiated":[41],"gradients":[42,56],"from":[43],"a":[44,67],"neural-network":[45],"with":[47],"theoretical":[48],"kernels,":[50],"show":[52,112],"that":[53,74,113],"learned":[55],"can":[57,78,102,117],"main":[60],"depth-dependent":[61],"structure":[62,135],"of":[63,70],"kernels":[65],"across":[66],"broad":[68],"range":[69],"periods.":[71],"This":[72],"indicates":[73],"learn":[79],"physically":[80,137],"meaningful":[81],"differential":[82,134],"information,":[83],"rather":[84],"than":[85],"acting":[86],"purely":[88],"black-box":[89],"predictors.":[90],"At":[91],"same":[93],"time,":[94],"strong":[95],"structural":[96],"priors":[97],"training":[100],"distribution":[101],"introduce":[103],"systematic":[104],"artifacts":[105],"into":[106],"inferred":[108],"sensitivities.":[109],"Our":[110],"results":[111],"surrogates":[116],"useful":[119],"information":[121],"for":[122],"inversion":[123],"and":[124],"uncertainty":[125],"analysis,":[126],"while":[127],"clarifying":[128],"conditions":[130],"under":[131],"which":[132],"consistent.":[138]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}
