{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PF7DTRSHYLV7YAJ4I5JP4QHCDR","short_pith_number":"pith:PF7DTRSH","schema_version":"1.0","canonical_sha256":"797e39c647c2ebfc013c4752fe40e21c4a4f6d530eacdf8247f122de22e56534","source":{"kind":"arxiv","id":"2501.12992","version":1},"attestation_state":"computed","paper":{"title":"Deep learning enhanced initial model prediction in elastic FWI: application to marine streamer data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.geo-ph","authors_text":"(2) NVIDIA, (3) Aramco Americas, Daniel Peter (1), Houston, Oleg Ovcharenko (2), Pavel Plotnitskii (1), Tariq Alkhalifah (1) ((1) KAUST, USA), Vladimir Kazei (3)","submitted_at":"2025-01-22T16:28:15Z","abstract_excerpt":"Low-frequency data are essential to constrain the low-wavenumber model components in seismic full-waveform inversion (FWI). However, due to acquisition limitations and ambient noise it is often unavailable. Deep learning (DL) can learn to map from high frequency model updates of elastic FWI to a low-wavenumber model update, producing an initial model estimation as if it was available from low-frequency data. We train a FusionNET-based convolutional neural network (CNN) on a synthetic dataset to produce an initial low-wavenumber model from a set of model updates produced by FWI on the data with"},"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":"2501.12992","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.geo-ph","submitted_at":"2025-01-22T16:28:15Z","cross_cats_sorted":[],"title_canon_sha256":"cf0e0ab54d7203ccb9320b2e641c49a39802119b47ad5c232d41c1f3f4246c4b","abstract_canon_sha256":"6556d7f46aa11f3114b75312fd15c4144855802bb62b3b1fbc519c55be2552c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:01.434202Z","signature_b64":"QBNqkjCKjk1qMpZCxJyVcd6rNKACWGWG5+Wt6U5/eWHR0maR61TvZ6Leg1YkBWifxr42XodhOFf3mPBHmxqVAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"797e39c647c2ebfc013c4752fe40e21c4a4f6d530eacdf8247f122de22e56534","last_reissued_at":"2026-07-05T10:04:01.433763Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:01.433763Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep learning enhanced initial model prediction in elastic FWI: application to marine streamer data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.geo-ph","authors_text":"(2) NVIDIA, (3) Aramco Americas, Daniel Peter (1), Houston, Oleg Ovcharenko (2), Pavel Plotnitskii (1), Tariq Alkhalifah (1) ((1) KAUST, USA), Vladimir Kazei (3)","submitted_at":"2025-01-22T16:28:15Z","abstract_excerpt":"Low-frequency data are essential to constrain the low-wavenumber model components in seismic full-waveform inversion (FWI). However, due to acquisition limitations and ambient noise it is often unavailable. Deep learning (DL) can learn to map from high frequency model updates of elastic FWI to a low-wavenumber model update, producing an initial model estimation as if it was available from low-frequency data. We train a FusionNET-based convolutional neural network (CNN) on a synthetic dataset to produce an initial low-wavenumber model from a set of model updates produced by FWI on the data with"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.12992","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/2501.12992/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":"2501.12992","created_at":"2026-07-05T10:04:01.433812+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.12992v1","created_at":"2026-07-05T10:04:01.433812+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.12992","created_at":"2026-07-05T10:04:01.433812+00:00"},{"alias_kind":"pith_short_12","alias_value":"PF7DTRSHYLV7","created_at":"2026-07-05T10:04:01.433812+00:00"},{"alias_kind":"pith_short_16","alias_value":"PF7DTRSHYLV7YAJ4","created_at":"2026-07-05T10:04:01.433812+00:00"},{"alias_kind":"pith_short_8","alias_value":"PF7DTRSH","created_at":"2026-07-05T10:04:01.433812+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/PF7DTRSHYLV7YAJ4I5JP4QHCDR","json":"https://pith.science/pith/PF7DTRSHYLV7YAJ4I5JP4QHCDR.json","graph_json":"https://pith.science/api/pith-number/PF7DTRSHYLV7YAJ4I5JP4QHCDR/graph.json","events_json":"https://pith.science/api/pith-number/PF7DTRSHYLV7YAJ4I5JP4QHCDR/events.json","paper":"https://pith.science/paper/PF7DTRSH"},"agent_actions":{"view_html":"https://pith.science/pith/PF7DTRSHYLV7YAJ4I5JP4QHCDR","download_json":"https://pith.science/pith/PF7DTRSHYLV7YAJ4I5JP4QHCDR.json","view_paper":"https://pith.science/paper/PF7DTRSH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.12992&json=true","fetch_graph":"https://pith.science/api/pith-number/PF7DTRSHYLV7YAJ4I5JP4QHCDR/graph.json","fetch_events":"https://pith.science/api/pith-number/PF7DTRSHYLV7YAJ4I5JP4QHCDR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PF7DTRSHYLV7YAJ4I5JP4QHCDR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PF7DTRSHYLV7YAJ4I5JP4QHCDR/action/storage_attestation","attest_author":"https://pith.science/pith/PF7DTRSHYLV7YAJ4I5JP4QHCDR/action/author_attestation","sign_citation":"https://pith.science/pith/PF7DTRSHYLV7YAJ4I5JP4QHCDR/action/citation_signature","submit_replication":"https://pith.science/pith/PF7DTRSHYLV7YAJ4I5JP4QHCDR/action/replication_record"}},"created_at":"2026-07-05T10:04:01.433812+00:00","updated_at":"2026-07-05T10:04:01.433812+00:00"}