{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:V4F5WWLC4IEHEGSXHDPXOF6RER","short_pith_number":"pith:V4F5WWLC","schema_version":"1.0","canonical_sha256":"af0bdb5962e208721a5738df7717d1244a48ca355b1b384d3a2fbc3b15508aab","source":{"kind":"arxiv","id":"2305.05150","version":1},"attestation_state":"computed","paper":{"title":"Physics-informed neural network for seismic wave inversion in layered semi-infinite domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA","math.NA"],"primary_cat":"physics.geo-ph","authors_text":"Chengping Rao, Hao Sun, Pu Ren, Yang Liu","submitted_at":"2023-05-09T03:30:06Z","abstract_excerpt":"Estimating the material distribution of Earth's subsurface is a challenging task in seismology and earthquake engineering. The recent development of physics-informed neural network (PINN) has shed new light on seismic inversion. In this paper, we present a PINN framework for seismic wave inversion in layered (1D) semi-infinite domain. The absorbing boundary condition is incorporated into the network as a soft regularizer for avoiding excessive computation. In specific, we design a lightweight network to learn the unknown material distribution and a deep neural network to approximate solution v"},"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":"2305.05150","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.geo-ph","submitted_at":"2023-05-09T03:30:06Z","cross_cats_sorted":["cs.LG","cs.NA","math.NA"],"title_canon_sha256":"68744096fe220afdb69b3f2ce37e54255a716e7187a784045ce10975d74c58f3","abstract_canon_sha256":"3ede5fa7744f23dc4ee693e8d94a96200148f90411ba51313b5b8bd02fac5bcb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:08:31.335708Z","signature_b64":"uun/xyIfP+qIGEwGihwbmUm1bINMAe8qnH/NfloUtNGxqtQyw2Yh+LIIinihC35Lj5yyS0n/eVFad96edCmVCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af0bdb5962e208721a5738df7717d1244a48ca355b1b384d3a2fbc3b15508aab","last_reissued_at":"2026-07-05T06:08:31.335283Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:08:31.335283Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Physics-informed neural network for seismic wave inversion in layered semi-infinite domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NA","math.NA"],"primary_cat":"physics.geo-ph","authors_text":"Chengping Rao, Hao Sun, Pu Ren, Yang Liu","submitted_at":"2023-05-09T03:30:06Z","abstract_excerpt":"Estimating the material distribution of Earth's subsurface is a challenging task in seismology and earthquake engineering. The recent development of physics-informed neural network (PINN) has shed new light on seismic inversion. In this paper, we present a PINN framework for seismic wave inversion in layered (1D) semi-infinite domain. The absorbing boundary condition is incorporated into the network as a soft regularizer for avoiding excessive computation. In specific, we design a lightweight network to learn the unknown material distribution and a deep neural network to approximate solution v"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.05150","kind":"arxiv","version":1},"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/2305.05150/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":"2305.05150","created_at":"2026-07-05T06:08:31.335357+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.05150v1","created_at":"2026-07-05T06:08:31.335357+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.05150","created_at":"2026-07-05T06:08:31.335357+00:00"},{"alias_kind":"pith_short_12","alias_value":"V4F5WWLC4IEH","created_at":"2026-07-05T06:08:31.335357+00:00"},{"alias_kind":"pith_short_16","alias_value":"V4F5WWLC4IEHEGSX","created_at":"2026-07-05T06:08:31.335357+00:00"},{"alias_kind":"pith_short_8","alias_value":"V4F5WWLC","created_at":"2026-07-05T06:08:31.335357+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/V4F5WWLC4IEHEGSXHDPXOF6RER","json":"https://pith.science/pith/V4F5WWLC4IEHEGSXHDPXOF6RER.json","graph_json":"https://pith.science/api/pith-number/V4F5WWLC4IEHEGSXHDPXOF6RER/graph.json","events_json":"https://pith.science/api/pith-number/V4F5WWLC4IEHEGSXHDPXOF6RER/events.json","paper":"https://pith.science/paper/V4F5WWLC"},"agent_actions":{"view_html":"https://pith.science/pith/V4F5WWLC4IEHEGSXHDPXOF6RER","download_json":"https://pith.science/pith/V4F5WWLC4IEHEGSXHDPXOF6RER.json","view_paper":"https://pith.science/paper/V4F5WWLC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.05150&json=true","fetch_graph":"https://pith.science/api/pith-number/V4F5WWLC4IEHEGSXHDPXOF6RER/graph.json","fetch_events":"https://pith.science/api/pith-number/V4F5WWLC4IEHEGSXHDPXOF6RER/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/V4F5WWLC4IEHEGSXHDPXOF6RER/action/timestamp_anchor","attest_storage":"https://pith.science/pith/V4F5WWLC4IEHEGSXHDPXOF6RER/action/storage_attestation","attest_author":"https://pith.science/pith/V4F5WWLC4IEHEGSXHDPXOF6RER/action/author_attestation","sign_citation":"https://pith.science/pith/V4F5WWLC4IEHEGSXHDPXOF6RER/action/citation_signature","submit_replication":"https://pith.science/pith/V4F5WWLC4IEHEGSXHDPXOF6RER/action/replication_record"}},"created_at":"2026-07-05T06:08:31.335357+00:00","updated_at":"2026-07-05T06:08:31.335357+00:00"}