{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7XY5Z6BUUZ3LERYLLXF74IHMU4","short_pith_number":"pith:7XY5Z6BU","schema_version":"1.0","canonical_sha256":"fdf1dcf834a676b2470b5dcbfe20eca70ef5be4f215aab1c701492b10a8ea0e8","source":{"kind":"arxiv","id":"2209.12340","version":3},"attestation_state":"computed","paper":{"title":"Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.geo-ph"],"primary_cat":"cs.LG","authors_text":"Bian Li, Hanchen Wang, Shihang Feng, Xiu Yang, Youzuo Lin","submitted_at":"2022-09-25T22:25:57Z","abstract_excerpt":"In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning enables solving partial differential equations, including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. This approach can be faster than traditional numerical methods when numerous instances are to be solved. Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted i"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2209.12340","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-25T22:25:57Z","cross_cats_sorted":["physics.geo-ph"],"title_canon_sha256":"32634532a5ba6710d2c3c08cc96f7cc053e24608cd0d8dcac93881c1183a1680","abstract_canon_sha256":"8ca4462107b247794d0c00ff758030f8ee73ea6724c84aa048e0cbd9235dd1c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:49:30.169213Z","signature_b64":"ldr72op2dpU2e/xMcoKT01BBjbVvE+47Pv4/FYbUns5sOdTXnqDq1R1+w3z+t1QqpD3ZBzZfB51TTOG+FyVJDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fdf1dcf834a676b2470b5dcbfe20eca70ef5be4f215aab1c701492b10a8ea0e8","last_reissued_at":"2026-07-05T05:49:30.168727Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:49:30.168727Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.geo-ph"],"primary_cat":"cs.LG","authors_text":"Bian Li, Hanchen Wang, Shihang Feng, Xiu Yang, Youzuo Lin","submitted_at":"2022-09-25T22:25:57Z","abstract_excerpt":"In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning enables solving partial differential equations, including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. This approach can be faster than traditional numerical methods when numerous instances are to be solved. Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.12340","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2209.12340/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2209.12340","created_at":"2026-07-05T05:49:30.168785+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.12340v3","created_at":"2026-07-05T05:49:30.168785+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.12340","created_at":"2026-07-05T05:49:30.168785+00:00"},{"alias_kind":"pith_short_12","alias_value":"7XY5Z6BUUZ3L","created_at":"2026-07-05T05:49:30.168785+00:00"},{"alias_kind":"pith_short_16","alias_value":"7XY5Z6BUUZ3LERYL","created_at":"2026-07-05T05:49:30.168785+00:00"},{"alias_kind":"pith_short_8","alias_value":"7XY5Z6BU","created_at":"2026-07-05T05:49:30.168785+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.07084","citing_title":"Fourier Neural Operators for Time-Periodic Quantum Systems: Learning Floquet Hamiltonians, Observable Dynamics, and Operator Growth","ref_index":68,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7XY5Z6BUUZ3LERYLLXF74IHMU4","json":"https://pith.science/pith/7XY5Z6BUUZ3LERYLLXF74IHMU4.json","graph_json":"https://pith.science/api/pith-number/7XY5Z6BUUZ3LERYLLXF74IHMU4/graph.json","events_json":"https://pith.science/api/pith-number/7XY5Z6BUUZ3LERYLLXF74IHMU4/events.json","paper":"https://pith.science/paper/7XY5Z6BU"},"agent_actions":{"view_html":"https://pith.science/pith/7XY5Z6BUUZ3LERYLLXF74IHMU4","download_json":"https://pith.science/pith/7XY5Z6BUUZ3LERYLLXF74IHMU4.json","view_paper":"https://pith.science/paper/7XY5Z6BU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.12340&json=true","fetch_graph":"https://pith.science/api/pith-number/7XY5Z6BUUZ3LERYLLXF74IHMU4/graph.json","fetch_events":"https://pith.science/api/pith-number/7XY5Z6BUUZ3LERYLLXF74IHMU4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7XY5Z6BUUZ3LERYLLXF74IHMU4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7XY5Z6BUUZ3LERYLLXF74IHMU4/action/storage_attestation","attest_author":"https://pith.science/pith/7XY5Z6BUUZ3LERYLLXF74IHMU4/action/author_attestation","sign_citation":"https://pith.science/pith/7XY5Z6BUUZ3LERYLLXF74IHMU4/action/citation_signature","submit_replication":"https://pith.science/pith/7XY5Z6BUUZ3LERYLLXF74IHMU4/action/replication_record"}},"created_at":"2026-07-05T05:49:30.168785+00:00","updated_at":"2026-07-05T05:49:30.168785+00:00"}