{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:OLZEJRCJEAJRSPJOBN3LJLKJXD","short_pith_number":"pith:OLZEJRCJ","schema_version":"1.0","canonical_sha256":"72f244c4492013193d2e0b76b4ad49b8f46a67627c50825917a1ede648fa724f","source":{"kind":"arxiv","id":"1604.00441","version":1},"attestation_state":"computed","paper":{"title":"A novel workflow for seismic net pay estimation with uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.geo-ph","authors_text":"Dale Baptiste, Michael E. Glinsky, Muhlis Unaldi, Vishal Nagassar","submitted_at":"2016-04-02T00:05:50Z","abstract_excerpt":"This paper presents a novel workflow for seismic net pay estimation with uncertainty. It is demonstrated on the Cassra/Iris Field. The theory for the stochastic wavelet derivation (which estimates the seismic noise level along with the wavelet, time-to-depth mapping, and their uncertainties), the stochastic sparse spike inversion, and the net pay estimation (using secant areas) along with its uncertainty; will be outlined. This includes benchmarking of this methodology on a synthetic model. A critical part of this process is the calibration of the secant areas. This is done in a two step proce"},"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":"1604.00441","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.geo-ph","submitted_at":"2016-04-02T00:05:50Z","cross_cats_sorted":[],"title_canon_sha256":"a5d5ea188a0a118841694501685c2c573dde86d4b78be044f2cfeccc037762d9","abstract_canon_sha256":"a194b820519d1d94b5b0ad5f66d4bc5cc863393cd5b540abc1cfa4d0417d150d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:17:49.615347Z","signature_b64":"U59TFc3voZpVe+re2yVGa4KiOP2SOSF+eFFycYAcXgP+5uytIRba9PZt2WJdfc6F2iOBF4J14nPqcMlOjYeWCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72f244c4492013193d2e0b76b4ad49b8f46a67627c50825917a1ede648fa724f","last_reissued_at":"2026-05-18T01:17:49.614611Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:17:49.614611Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A novel workflow for seismic net pay estimation with uncertainty","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.geo-ph","authors_text":"Dale Baptiste, Michael E. Glinsky, Muhlis Unaldi, Vishal Nagassar","submitted_at":"2016-04-02T00:05:50Z","abstract_excerpt":"This paper presents a novel workflow for seismic net pay estimation with uncertainty. It is demonstrated on the Cassra/Iris Field. The theory for the stochastic wavelet derivation (which estimates the seismic noise level along with the wavelet, time-to-depth mapping, and their uncertainties), the stochastic sparse spike inversion, and the net pay estimation (using secant areas) along with its uncertainty; will be outlined. This includes benchmarking of this methodology on a synthetic model. A critical part of this process is the calibration of the secant areas. This is done in a two step proce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.00441","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":""},"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":"1604.00441","created_at":"2026-05-18T01:17:49.614730+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.00441v1","created_at":"2026-05-18T01:17:49.614730+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.00441","created_at":"2026-05-18T01:17:49.614730+00:00"},{"alias_kind":"pith_short_12","alias_value":"OLZEJRCJEAJR","created_at":"2026-05-18T12:30:36.002864+00:00"},{"alias_kind":"pith_short_16","alias_value":"OLZEJRCJEAJRSPJO","created_at":"2026-05-18T12:30:36.002864+00:00"},{"alias_kind":"pith_short_8","alias_value":"OLZEJRCJ","created_at":"2026-05-18T12:30:36.002864+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/OLZEJRCJEAJRSPJOBN3LJLKJXD","json":"https://pith.science/pith/OLZEJRCJEAJRSPJOBN3LJLKJXD.json","graph_json":"https://pith.science/api/pith-number/OLZEJRCJEAJRSPJOBN3LJLKJXD/graph.json","events_json":"https://pith.science/api/pith-number/OLZEJRCJEAJRSPJOBN3LJLKJXD/events.json","paper":"https://pith.science/paper/OLZEJRCJ"},"agent_actions":{"view_html":"https://pith.science/pith/OLZEJRCJEAJRSPJOBN3LJLKJXD","download_json":"https://pith.science/pith/OLZEJRCJEAJRSPJOBN3LJLKJXD.json","view_paper":"https://pith.science/paper/OLZEJRCJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.00441&json=true","fetch_graph":"https://pith.science/api/pith-number/OLZEJRCJEAJRSPJOBN3LJLKJXD/graph.json","fetch_events":"https://pith.science/api/pith-number/OLZEJRCJEAJRSPJOBN3LJLKJXD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OLZEJRCJEAJRSPJOBN3LJLKJXD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OLZEJRCJEAJRSPJOBN3LJLKJXD/action/storage_attestation","attest_author":"https://pith.science/pith/OLZEJRCJEAJRSPJOBN3LJLKJXD/action/author_attestation","sign_citation":"https://pith.science/pith/OLZEJRCJEAJRSPJOBN3LJLKJXD/action/citation_signature","submit_replication":"https://pith.science/pith/OLZEJRCJEAJRSPJOBN3LJLKJXD/action/replication_record"}},"created_at":"2026-05-18T01:17:49.614730+00:00","updated_at":"2026-05-18T01:17:49.614730+00:00"}