{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PVAUHAHWXUFRX2JZEXNTSHANIF","short_pith_number":"pith:PVAUHAHW","schema_version":"1.0","canonical_sha256":"7d414380f6bd0b1be93925db391c0d417a229305f58d3275ea37bfaaaf3c369a","source":{"kind":"arxiv","id":"1805.00291","version":1},"attestation_state":"computed","paper":{"title":"Deep Autoassociative Neural Networks for Noise Reduction in Seismic data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Debjani Bhowmick, Deepak K. Gupta, Saumen Maiti, Uma Shankar","submitted_at":"2018-05-01T12:40:16Z","abstract_excerpt":"Machine learning is currently a trending topic in various science and engineering disciplines, and the field of geophysics is no exception. With the advent of powerful computers, it is now possible to train the machine to learn complex patterns in the data, which may not be easily realized using the traditional methods. Among the various machine learning methods, the artificial neural networks (ANNs) have received enormous attention. A variant of ANNs, autoassociative neural network (autoNN) tries to learn the reconstruction of input itself using backpropagation. In an autoNN, the input and ou"},"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":"1805.00291","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2018-05-01T12:40:16Z","cross_cats_sorted":[],"title_canon_sha256":"3c694889a2b99672fae0c63251da329e583a293800ac7059c8b641592b123942","abstract_canon_sha256":"97848085066aa7e1706b4257d924943724513afd3dde22f5d2db85d4e00d6edb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:10.035902Z","signature_b64":"S280eRIQZSNprU9vOdK7tI+QDrypOVlYGSwz6nTyaKsn+IR4yMeJYaw0wIQwNCP7AYIbWVcl0dmX1VFTXOxsCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d414380f6bd0b1be93925db391c0d417a229305f58d3275ea37bfaaaf3c369a","last_reissued_at":"2026-05-18T00:17:10.035252Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:10.035252Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Autoassociative Neural Networks for Noise Reduction in Seismic data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CE","authors_text":"Debjani Bhowmick, Deepak K. Gupta, Saumen Maiti, Uma Shankar","submitted_at":"2018-05-01T12:40:16Z","abstract_excerpt":"Machine learning is currently a trending topic in various science and engineering disciplines, and the field of geophysics is no exception. With the advent of powerful computers, it is now possible to train the machine to learn complex patterns in the data, which may not be easily realized using the traditional methods. Among the various machine learning methods, the artificial neural networks (ANNs) have received enormous attention. A variant of ANNs, autoassociative neural network (autoNN) tries to learn the reconstruction of input itself using backpropagation. In an autoNN, the input and ou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.00291","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":"1805.00291","created_at":"2026-05-18T00:17:10.035369+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.00291v1","created_at":"2026-05-18T00:17:10.035369+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.00291","created_at":"2026-05-18T00:17:10.035369+00:00"},{"alias_kind":"pith_short_12","alias_value":"PVAUHAHWXUFR","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"PVAUHAHWXUFRX2JZ","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"PVAUHAHW","created_at":"2026-05-18T12:32:46.962924+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.03278","citing_title":"Stacked autoencoders based machine learning for noise reduction and signal reconstruction in geophysical data","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PVAUHAHWXUFRX2JZEXNTSHANIF","json":"https://pith.science/pith/PVAUHAHWXUFRX2JZEXNTSHANIF.json","graph_json":"https://pith.science/api/pith-number/PVAUHAHWXUFRX2JZEXNTSHANIF/graph.json","events_json":"https://pith.science/api/pith-number/PVAUHAHWXUFRX2JZEXNTSHANIF/events.json","paper":"https://pith.science/paper/PVAUHAHW"},"agent_actions":{"view_html":"https://pith.science/pith/PVAUHAHWXUFRX2JZEXNTSHANIF","download_json":"https://pith.science/pith/PVAUHAHWXUFRX2JZEXNTSHANIF.json","view_paper":"https://pith.science/paper/PVAUHAHW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.00291&json=true","fetch_graph":"https://pith.science/api/pith-number/PVAUHAHWXUFRX2JZEXNTSHANIF/graph.json","fetch_events":"https://pith.science/api/pith-number/PVAUHAHWXUFRX2JZEXNTSHANIF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PVAUHAHWXUFRX2JZEXNTSHANIF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PVAUHAHWXUFRX2JZEXNTSHANIF/action/storage_attestation","attest_author":"https://pith.science/pith/PVAUHAHWXUFRX2JZEXNTSHANIF/action/author_attestation","sign_citation":"https://pith.science/pith/PVAUHAHWXUFRX2JZEXNTSHANIF/action/citation_signature","submit_replication":"https://pith.science/pith/PVAUHAHWXUFRX2JZEXNTSHANIF/action/replication_record"}},"created_at":"2026-05-18T00:17:10.035369+00:00","updated_at":"2026-05-18T00:17:10.035369+00:00"}