{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:Z6OO5DIMILIC3DJPQZIKFMHRTQ","short_pith_number":"pith:Z6OO5DIM","schema_version":"1.0","canonical_sha256":"cf9cee8d0c42d02d8d2f8650a2b0f19c0693e5faa2ed38b9df82655574011bc7","source":{"kind":"arxiv","id":"2206.08158","version":1},"attestation_state":"computed","paper":{"title":"Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.geo-ph"],"primary_cat":"cs.CV","authors_text":"Ghassan AlRegib, Kiran Kokilepersaud, Mohit Prabhushankar","submitted_at":"2022-06-16T13:20:54Z","abstract_excerpt":"In seismic interpretation, pixel-level labels of various rock structures can be time-consuming and expensive to obtain. As a result, there oftentimes exists a non-trivial quantity of unlabeled data that is left unused simply because traditional deep learning methods rely on access to fully labeled volumes. To rectify this problem, contrastive learning approaches have been proposed that use a self-supervised methodology in order to learn useful representations from unlabeled data. However, traditional contrastive learning approaches are based on assumptions from the domain of natural images tha"},"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":"2206.08158","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-06-16T13:20:54Z","cross_cats_sorted":["physics.geo-ph"],"title_canon_sha256":"df55995113585d8e65df54fa64fd447d0d7173691740b00eb3382735887be8b9","abstract_canon_sha256":"ba851c456789d90785a3e01ed43d3d92d16851bf6efaf291ad8db33feacdc9c3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:32:22.694367Z","signature_b64":"rZTp+SaF2fjA7BMUojuxFoY6y2wJrP6UtRFBZ3gRZb7itVANBdjdrgiHFQ7kY2DpSi+0xCSksCRQTHSHJt2JAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf9cee8d0c42d02d8d2f8650a2b0f19c0693e5faa2ed38b9df82655574011bc7","last_reissued_at":"2026-07-05T04:32:22.693941Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:32:22.693941Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Volumetric Supervised Contrastive Learning for Seismic Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.geo-ph"],"primary_cat":"cs.CV","authors_text":"Ghassan AlRegib, Kiran Kokilepersaud, Mohit Prabhushankar","submitted_at":"2022-06-16T13:20:54Z","abstract_excerpt":"In seismic interpretation, pixel-level labels of various rock structures can be time-consuming and expensive to obtain. As a result, there oftentimes exists a non-trivial quantity of unlabeled data that is left unused simply because traditional deep learning methods rely on access to fully labeled volumes. To rectify this problem, contrastive learning approaches have been proposed that use a self-supervised methodology in order to learn useful representations from unlabeled data. However, traditional contrastive learning approaches are based on assumptions from the domain of natural images tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.08158","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/2206.08158/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":"2206.08158","created_at":"2026-07-05T04:32:22.694006+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.08158v1","created_at":"2026-07-05T04:32:22.694006+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.08158","created_at":"2026-07-05T04:32:22.694006+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z6OO5DIMILIC","created_at":"2026-07-05T04:32:22.694006+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z6OO5DIMILIC3DJP","created_at":"2026-07-05T04:32:22.694006+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z6OO5DIM","created_at":"2026-07-05T04:32:22.694006+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/Z6OO5DIMILIC3DJPQZIKFMHRTQ","json":"https://pith.science/pith/Z6OO5DIMILIC3DJPQZIKFMHRTQ.json","graph_json":"https://pith.science/api/pith-number/Z6OO5DIMILIC3DJPQZIKFMHRTQ/graph.json","events_json":"https://pith.science/api/pith-number/Z6OO5DIMILIC3DJPQZIKFMHRTQ/events.json","paper":"https://pith.science/paper/Z6OO5DIM"},"agent_actions":{"view_html":"https://pith.science/pith/Z6OO5DIMILIC3DJPQZIKFMHRTQ","download_json":"https://pith.science/pith/Z6OO5DIMILIC3DJPQZIKFMHRTQ.json","view_paper":"https://pith.science/paper/Z6OO5DIM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.08158&json=true","fetch_graph":"https://pith.science/api/pith-number/Z6OO5DIMILIC3DJPQZIKFMHRTQ/graph.json","fetch_events":"https://pith.science/api/pith-number/Z6OO5DIMILIC3DJPQZIKFMHRTQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z6OO5DIMILIC3DJPQZIKFMHRTQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z6OO5DIMILIC3DJPQZIKFMHRTQ/action/storage_attestation","attest_author":"https://pith.science/pith/Z6OO5DIMILIC3DJPQZIKFMHRTQ/action/author_attestation","sign_citation":"https://pith.science/pith/Z6OO5DIMILIC3DJPQZIKFMHRTQ/action/citation_signature","submit_replication":"https://pith.science/pith/Z6OO5DIMILIC3DJPQZIKFMHRTQ/action/replication_record"}},"created_at":"2026-07-05T04:32:22.694006+00:00","updated_at":"2026-07-05T04:32:22.694006+00:00"}