{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LGEM4Q72KUTU2OES5ME5LVRIBC","short_pith_number":"pith:LGEM4Q72","canonical_record":{"source":{"id":"1802.05622","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-15T15:34:23Z","cross_cats_sorted":["cs.CV","physics.geo-ph"],"title_canon_sha256":"2f0339e852b452033b143c657f374b77038569a75e2265f0a5b090a4eb2b4df4","abstract_canon_sha256":"9a39b298844d8c8036e4cf5ac0a0deea6a9bfeac1467408353434699976afc25"},"schema_version":"1.0"},"canonical_sha256":"5988ce43fa55274d3892eb09d5d62808a45ca2cfa03596ad9db6240675cc464e","source":{"kind":"arxiv","id":"1802.05622","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.05622","created_at":"2026-05-18T00:23:15Z"},{"alias_kind":"arxiv_version","alias_value":"1802.05622v1","created_at":"2026-05-18T00:23:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05622","created_at":"2026-05-18T00:23:15Z"},{"alias_kind":"pith_short_12","alias_value":"LGEM4Q72KUTU","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LGEM4Q72KUTU2OES","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LGEM4Q72","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LGEM4Q72KUTU2OES5ME5LVRIBC","target":"record","payload":{"canonical_record":{"source":{"id":"1802.05622","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-15T15:34:23Z","cross_cats_sorted":["cs.CV","physics.geo-ph"],"title_canon_sha256":"2f0339e852b452033b143c657f374b77038569a75e2265f0a5b090a4eb2b4df4","abstract_canon_sha256":"9a39b298844d8c8036e4cf5ac0a0deea6a9bfeac1467408353434699976afc25"},"schema_version":"1.0"},"canonical_sha256":"5988ce43fa55274d3892eb09d5d62808a45ca2cfa03596ad9db6240675cc464e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:15.036765Z","signature_b64":"HLcOf3CeytrRKU/IVap9gXWAv9pqR9dWNu46oSpkjzPrKsKcqXaMBGCpyy5EYX83LGzGcHrj6qnNxbEF1/7BAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5988ce43fa55274d3892eb09d5d62808a45ca2cfa03596ad9db6240675cc464e","last_reissued_at":"2026-05-18T00:23:15.036124Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:15.036124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.05622","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:23:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"woO18rO7dx8N8mX5zOSvcwZk+A9nXUIncM4U8dOhCpK2OipDArbNZNmHtqCgx/mGruLVXbHsJabxquxubXBbBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T12:39:57.389722Z"},"content_sha256":"579d1acf16baf36ac6ede5b839ab9c9e62f5e44ec5fe41ed47d8c7f07e6fc74b","schema_version":"1.0","event_id":"sha256:579d1acf16baf36ac6ede5b839ab9c9e62f5e44ec5fe41ed47d8c7f07e6fc74b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LGEM4Q72KUTU2OES5ME5LVRIBC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","physics.geo-ph"],"primary_cat":"stat.ML","authors_text":"Lukas Mosser, Martin J. Blunt, Olivier Dubrule","submitted_at":"2018-02-15T15:34:23Z","abstract_excerpt":"Geostatistical modeling of petrophysical properties is a key step in modern integrated oil and gas reservoir studies. Recently, generative adversarial networks (GAN) have been shown to be a successful method for generating unconditional simulations of pore- and reservoir-scale models. This contribution leverages the differentiable nature of neural networks to extend GANs to the conditional simulation of three-dimensional pore- and reservoir-scale models. Based on the previous work of Yeh et al. (2016), we use a content loss to constrain to the conditioning data and a perceptual loss obtained f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05622","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:23:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lTn2gOUpsvaBmWWMYldaak/WjqBzjwYSoByl5ct+CRJat+l4s7pkGvT8tXQhOCl0uI4cf6f7FUuwcO6LSBLqBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T12:39:57.390276Z"},"content_sha256":"75641aa79df4c53eddc00fce6079f4c7d081be127824ad6839c21be1e25d46b5","schema_version":"1.0","event_id":"sha256:75641aa79df4c53eddc00fce6079f4c7d081be127824ad6839c21be1e25d46b5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LGEM4Q72KUTU2OES5ME5LVRIBC/bundle.json","state_url":"https://pith.science/pith/LGEM4Q72KUTU2OES5ME5LVRIBC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LGEM4Q72KUTU2OES5ME5LVRIBC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T12:39:57Z","links":{"resolver":"https://pith.science/pith/LGEM4Q72KUTU2OES5ME5LVRIBC","bundle":"https://pith.science/pith/LGEM4Q72KUTU2OES5ME5LVRIBC/bundle.json","state":"https://pith.science/pith/LGEM4Q72KUTU2OES5ME5LVRIBC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LGEM4Q72KUTU2OES5ME5LVRIBC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LGEM4Q72KUTU2OES5ME5LVRIBC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9a39b298844d8c8036e4cf5ac0a0deea6a9bfeac1467408353434699976afc25","cross_cats_sorted":["cs.CV","physics.geo-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-15T15:34:23Z","title_canon_sha256":"2f0339e852b452033b143c657f374b77038569a75e2265f0a5b090a4eb2b4df4"},"schema_version":"1.0","source":{"id":"1802.05622","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.05622","created_at":"2026-05-18T00:23:15Z"},{"alias_kind":"arxiv_version","alias_value":"1802.05622v1","created_at":"2026-05-18T00:23:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.05622","created_at":"2026-05-18T00:23:15Z"},{"alias_kind":"pith_short_12","alias_value":"LGEM4Q72KUTU","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LGEM4Q72KUTU2OES","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LGEM4Q72","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:75641aa79df4c53eddc00fce6079f4c7d081be127824ad6839c21be1e25d46b5","target":"graph","created_at":"2026-05-18T00:23:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Geostatistical modeling of petrophysical properties is a key step in modern integrated oil and gas reservoir studies. Recently, generative adversarial networks (GAN) have been shown to be a successful method for generating unconditional simulations of pore- and reservoir-scale models. This contribution leverages the differentiable nature of neural networks to extend GANs to the conditional simulation of three-dimensional pore- and reservoir-scale models. Based on the previous work of Yeh et al. (2016), we use a content loss to constrain to the conditioning data and a perceptual loss obtained f","authors_text":"Lukas Mosser, Martin J. Blunt, Olivier Dubrule","cross_cats":["cs.CV","physics.geo-ph"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-15T15:34:23Z","title":"Conditioning of three-dimensional generative adversarial networks for pore and reservoir-scale models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.05622","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:579d1acf16baf36ac6ede5b839ab9c9e62f5e44ec5fe41ed47d8c7f07e6fc74b","target":"record","created_at":"2026-05-18T00:23:15Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9a39b298844d8c8036e4cf5ac0a0deea6a9bfeac1467408353434699976afc25","cross_cats_sorted":["cs.CV","physics.geo-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-15T15:34:23Z","title_canon_sha256":"2f0339e852b452033b143c657f374b77038569a75e2265f0a5b090a4eb2b4df4"},"schema_version":"1.0","source":{"id":"1802.05622","kind":"arxiv","version":1}},"canonical_sha256":"5988ce43fa55274d3892eb09d5d62808a45ca2cfa03596ad9db6240675cc464e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5988ce43fa55274d3892eb09d5d62808a45ca2cfa03596ad9db6240675cc464e","first_computed_at":"2026-05-18T00:23:15.036124Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:15.036124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HLcOf3CeytrRKU/IVap9gXWAv9pqR9dWNu46oSpkjzPrKsKcqXaMBGCpyy5EYX83LGzGcHrj6qnNxbEF1/7BAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:15.036765Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.05622","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:579d1acf16baf36ac6ede5b839ab9c9e62f5e44ec5fe41ed47d8c7f07e6fc74b","sha256:75641aa79df4c53eddc00fce6079f4c7d081be127824ad6839c21be1e25d46b5"],"state_sha256":"928c10105a5d073cd23f2a613e542119823b73399de7ae159e2cf43991f25923"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8mP7Y4DFLKHOTGENndgiuyijJc1ZEeDw67OGABeOjTwCy56Hc1p5mewkjvrmbPcVI1PqrdxsV//H/I6ciKDWAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T12:39:57.393409Z","bundle_sha256":"95777f2e67cb2baecd95d0b0c7d87dca42ed084ee6d2ec0341073298092e3486"}}