{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YFLYOYY43FX6OK5VRK3IFM42LZ","short_pith_number":"pith:YFLYOYY4","schema_version":"1.0","canonical_sha256":"c15787631cd96fe72bb58ab682b39a5e6a5fd96a3515fac74dfc62835a13cc7a","source":{"kind":"arxiv","id":"1807.02716","version":1},"attestation_state":"computed","paper":{"title":"A Deep-Learning-Based Geological Parameterization for History Matching Complex Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","physics.geo-ph"],"primary_cat":"stat.ML","authors_text":"Louis J. Durlofsky, Wenyue Sun, Yimin Liu","submitted_at":"2018-07-07T20:34:04Z","abstract_excerpt":"A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN-PCA method is inspired by recent developments in computer vision using deep learning. CNN-PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN-PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN-PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multi"},"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":"1807.02716","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-07-07T20:34:04Z","cross_cats_sorted":["cs.CV","cs.LG","physics.geo-ph"],"title_canon_sha256":"1596e9fb24d0b155cb53644cddccfdd031d9b4af586cc37456033c33fc99e79d","abstract_canon_sha256":"3d05eafa54353990bf9150dd0aa6d158e17863e3515ecbb32928be0805612aa2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:10.901774Z","signature_b64":"KCZzgiVVCBiDmxSjkb2/7Zz19l5DcSXyp/ClRX28ts9cBBm7d7Fr+Xrp+JkAsDlJnWHEl3IJHAqQ34stuKN0Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c15787631cd96fe72bb58ab682b39a5e6a5fd96a3515fac74dfc62835a13cc7a","last_reissued_at":"2026-05-18T00:11:10.901230Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:10.901230Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Deep-Learning-Based Geological Parameterization for History Matching Complex Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","physics.geo-ph"],"primary_cat":"stat.ML","authors_text":"Louis J. Durlofsky, Wenyue Sun, Yimin Liu","submitted_at":"2018-07-07T20:34:04Z","abstract_excerpt":"A new low-dimensional parameterization based on principal component analysis (PCA) and convolutional neural networks (CNN) is developed to represent complex geological models. The CNN-PCA method is inspired by recent developments in computer vision using deep learning. CNN-PCA can be viewed as a generalization of an existing optimization-based PCA (O-PCA) method. Both CNN-PCA and O-PCA entail post-processing a PCA model to better honor complex geological features. In CNN-PCA, rather than use a histogram-based regularization as in O-PCA, a new regularization involving a set of metrics for multi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02716","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":"1807.02716","created_at":"2026-05-18T00:11:10.901326+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.02716v1","created_at":"2026-05-18T00:11:10.901326+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02716","created_at":"2026-05-18T00:11:10.901326+00:00"},{"alias_kind":"pith_short_12","alias_value":"YFLYOYY43FX6","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"YFLYOYY43FX6OK5V","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"YFLYOYY4","created_at":"2026-05-18T12:33:04.347982+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/YFLYOYY43FX6OK5VRK3IFM42LZ","json":"https://pith.science/pith/YFLYOYY43FX6OK5VRK3IFM42LZ.json","graph_json":"https://pith.science/api/pith-number/YFLYOYY43FX6OK5VRK3IFM42LZ/graph.json","events_json":"https://pith.science/api/pith-number/YFLYOYY43FX6OK5VRK3IFM42LZ/events.json","paper":"https://pith.science/paper/YFLYOYY4"},"agent_actions":{"view_html":"https://pith.science/pith/YFLYOYY43FX6OK5VRK3IFM42LZ","download_json":"https://pith.science/pith/YFLYOYY43FX6OK5VRK3IFM42LZ.json","view_paper":"https://pith.science/paper/YFLYOYY4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.02716&json=true","fetch_graph":"https://pith.science/api/pith-number/YFLYOYY43FX6OK5VRK3IFM42LZ/graph.json","fetch_events":"https://pith.science/api/pith-number/YFLYOYY43FX6OK5VRK3IFM42LZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YFLYOYY43FX6OK5VRK3IFM42LZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YFLYOYY43FX6OK5VRK3IFM42LZ/action/storage_attestation","attest_author":"https://pith.science/pith/YFLYOYY43FX6OK5VRK3IFM42LZ/action/author_attestation","sign_citation":"https://pith.science/pith/YFLYOYY43FX6OK5VRK3IFM42LZ/action/citation_signature","submit_replication":"https://pith.science/pith/YFLYOYY43FX6OK5VRK3IFM42LZ/action/replication_record"}},"created_at":"2026-05-18T00:11:10.901326+00:00","updated_at":"2026-05-18T00:11:10.901326+00:00"}