{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:3ZYPM5C5ZFJCMJXXU3IQS3553F","short_pith_number":"pith:3ZYPM5C5","canonical_record":{"source":{"id":"1708.04975","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-08-16T17:04:52Z","cross_cats_sorted":["cs.CV","physics.geo-ph"],"title_canon_sha256":"2ab3521120eb64524d2ca13a148830a565a359b9660b56b9f9d46ce8c37d1d5e","abstract_canon_sha256":"af5cddfd1f9911793ae3f3478dc948b6df441a33e721ca30f288d5066525898b"},"schema_version":"1.0"},"canonical_sha256":"de70f6745dc9522626f7a6d1096fbdd963ec14b3582d0ef1ff402858432703d8","source":{"kind":"arxiv","id":"1708.04975","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.04975","created_at":"2026-05-17T23:56:48Z"},{"alias_kind":"arxiv_version","alias_value":"1708.04975v2","created_at":"2026-05-17T23:56:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04975","created_at":"2026-05-17T23:56:48Z"},{"alias_kind":"pith_short_12","alias_value":"3ZYPM5C5ZFJC","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3ZYPM5C5ZFJCMJXX","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3ZYPM5C5","created_at":"2026-05-18T12:30:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:3ZYPM5C5ZFJCMJXXU3IQS3553F","target":"record","payload":{"canonical_record":{"source":{"id":"1708.04975","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-08-16T17:04:52Z","cross_cats_sorted":["cs.CV","physics.geo-ph"],"title_canon_sha256":"2ab3521120eb64524d2ca13a148830a565a359b9660b56b9f9d46ce8c37d1d5e","abstract_canon_sha256":"af5cddfd1f9911793ae3f3478dc948b6df441a33e721ca30f288d5066525898b"},"schema_version":"1.0"},"canonical_sha256":"de70f6745dc9522626f7a6d1096fbdd963ec14b3582d0ef1ff402858432703d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:48.588622Z","signature_b64":"sWeDKw3pYkFUoh0oO71ijkH/Q+1I6vptgZGcNcnIVGfoFxQWfQnHL9Ld6q6qSo42RfGYRabvU/LFbD39pufECA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de70f6745dc9522626f7a6d1096fbdd963ec14b3582d0ef1ff402858432703d8","last_reissued_at":"2026-05-17T23:56:48.588208Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:48.588208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.04975","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:56:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+wCVexq4MQtrWt4Ce61IjCfAR7Jz1gdN6mTa3cA02tnzVSG4OFW+cjhQPyNLZlW0JtR1jPuLH8BTO/K/F0yiDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T03:11:36.960051Z"},"content_sha256":"7be0b2418946539532decf87f4ac2a98e6e8d57353461130491395adcdcf55b8","schema_version":"1.0","event_id":"sha256:7be0b2418946539532decf87f4ac2a98e6e8d57353461130491395adcdcf55b8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:3ZYPM5C5ZFJCMJXXU3IQS3553F","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Training-image based geostatistical inversion using a spatial generative adversarial neural network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","physics.geo-ph"],"primary_cat":"stat.ML","authors_text":"Diederik Jacques, Eric Laloy, Niklas Linde, Romain H\\'erault","submitted_at":"2017-08-16T17:04:52Z","abstract_excerpt":"Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2D and 3D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04975","kind":"arxiv","version":2},"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":"2026-05-17T23:56:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eNUzTAN7zWZC7sjpAX/5Z4DsJcs5oMfG3KmI8oV4yKQYIzKep9ponuEL2jrtzRXLLwttAptysxvXWcqB6oICBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T03:11:36.960428Z"},"content_sha256":"aed3d9440d8f22ead5869b9b09afb72eeac85729d1e773c68626714b43f0feab","schema_version":"1.0","event_id":"sha256:aed3d9440d8f22ead5869b9b09afb72eeac85729d1e773c68626714b43f0feab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3ZYPM5C5ZFJCMJXXU3IQS3553F/bundle.json","state_url":"https://pith.science/pith/3ZYPM5C5ZFJCMJXXU3IQS3553F/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3ZYPM5C5ZFJCMJXXU3IQS3553F/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":"2026-06-02T03:11:36Z","links":{"resolver":"https://pith.science/pith/3ZYPM5C5ZFJCMJXXU3IQS3553F","bundle":"https://pith.science/pith/3ZYPM5C5ZFJCMJXXU3IQS3553F/bundle.json","state":"https://pith.science/pith/3ZYPM5C5ZFJCMJXXU3IQS3553F/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3ZYPM5C5ZFJCMJXXU3IQS3553F/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:3ZYPM5C5ZFJCMJXXU3IQS3553F","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":"af5cddfd1f9911793ae3f3478dc948b6df441a33e721ca30f288d5066525898b","cross_cats_sorted":["cs.CV","physics.geo-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-08-16T17:04:52Z","title_canon_sha256":"2ab3521120eb64524d2ca13a148830a565a359b9660b56b9f9d46ce8c37d1d5e"},"schema_version":"1.0","source":{"id":"1708.04975","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.04975","created_at":"2026-05-17T23:56:48Z"},{"alias_kind":"arxiv_version","alias_value":"1708.04975v2","created_at":"2026-05-17T23:56:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.04975","created_at":"2026-05-17T23:56:48Z"},{"alias_kind":"pith_short_12","alias_value":"3ZYPM5C5ZFJC","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3ZYPM5C5ZFJCMJXX","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3ZYPM5C5","created_at":"2026-05-18T12:30:58Z"}],"graph_snapshots":[{"event_id":"sha256:aed3d9440d8f22ead5869b9b09afb72eeac85729d1e773c68626714b43f0feab","target":"graph","created_at":"2026-05-17T23:56:48Z","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":"Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2D and 3D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowi","authors_text":"Diederik Jacques, Eric Laloy, Niklas Linde, Romain H\\'erault","cross_cats":["cs.CV","physics.geo-ph"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-08-16T17:04:52Z","title":"Training-image based geostatistical inversion using a spatial generative adversarial neural network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.04975","kind":"arxiv","version":2},"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":"sha256:7be0b2418946539532decf87f4ac2a98e6e8d57353461130491395adcdcf55b8","target":"record","created_at":"2026-05-17T23:56:48Z","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":"af5cddfd1f9911793ae3f3478dc948b6df441a33e721ca30f288d5066525898b","cross_cats_sorted":["cs.CV","physics.geo-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-08-16T17:04:52Z","title_canon_sha256":"2ab3521120eb64524d2ca13a148830a565a359b9660b56b9f9d46ce8c37d1d5e"},"schema_version":"1.0","source":{"id":"1708.04975","kind":"arxiv","version":2}},"canonical_sha256":"de70f6745dc9522626f7a6d1096fbdd963ec14b3582d0ef1ff402858432703d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de70f6745dc9522626f7a6d1096fbdd963ec14b3582d0ef1ff402858432703d8","first_computed_at":"2026-05-17T23:56:48.588208Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:56:48.588208Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sWeDKw3pYkFUoh0oO71ijkH/Q+1I6vptgZGcNcnIVGfoFxQWfQnHL9Ld6q6qSo42RfGYRabvU/LFbD39pufECA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:56:48.588622Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.04975","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7be0b2418946539532decf87f4ac2a98e6e8d57353461130491395adcdcf55b8","sha256:aed3d9440d8f22ead5869b9b09afb72eeac85729d1e773c68626714b43f0feab"],"state_sha256":"6ef0696a81a91f219d9a91fafcdeb72949990b517cb7e0c65f861874901097c5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NylsqKGWJftsKKiKD3UBx1vtSso/0mFg/+lALPf0dOTlzuZSVrXI4IT6XV1y+rjCSeMtPSV9z3kFfW758xfzAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T03:11:36.962339Z","bundle_sha256":"e6e96c9328b34b8b2b0c1cc2c216feff8dc58f6b10bee47562c44902fe834e46"}}