{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:QZNBHKZVIUWZJMS4GRNTC6BTRV","short_pith_number":"pith:QZNBHKZV","schema_version":"1.0","canonical_sha256":"865a13ab35452d94b25c345b3178338d7407f2bbb8ccc8a1887da53dbd447d28","source":{"kind":"arxiv","id":"1912.01144","version":1},"attestation_state":"computed","paper":{"title":"Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"physics.geo-ph","authors_text":"Gregory C. Beroza, S.Mostafa Mousavi","submitted_at":"2019-12-03T01:20:28Z","abstract_excerpt":"We present a deep learning method for single-station earthquake location, which we approach as a regression problem using two separate Bayesian neural networks. We use a multi-task temporal-convolutional neural network to learn epicentral distance and P travel time from 1-minute seismograms. The network estimates epicentral distance and P travel time with absolute mean errors of 0.23 km and 0.03 s respectively, along with their epistemic and aleatory uncertainties. We design a separate multi-input network using standard convolutional layers to estimate the back-azimuth angle, and its epistemic"},"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":"1912.01144","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.geo-ph","submitted_at":"2019-12-03T01:20:28Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"f25e3173559aba58cb53348fb7f3dd0fec1e08d944b69010311a4a11959c4d58","abstract_canon_sha256":"2dedff6937912dc68366d4d75dc9fdfd485282ebe2d7bac022319127eb049db4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:55:44.435802Z","signature_b64":"72TdFso5l3s2iFh8CLAo6rpCKCpbvHL2V/T6OLX0Xv2nh9DEk7S4aFX53xPM4VaM4Hz+h8qWBqvHbmB5KAFBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"865a13ab35452d94b25c345b3178338d7407f2bbb8ccc8a1887da53dbd447d28","last_reissued_at":"2026-07-05T01:55:44.435468Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:55:44.435468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"physics.geo-ph","authors_text":"Gregory C. Beroza, S.Mostafa Mousavi","submitted_at":"2019-12-03T01:20:28Z","abstract_excerpt":"We present a deep learning method for single-station earthquake location, which we approach as a regression problem using two separate Bayesian neural networks. We use a multi-task temporal-convolutional neural network to learn epicentral distance and P travel time from 1-minute seismograms. The network estimates epicentral distance and P travel time with absolute mean errors of 0.23 km and 0.03 s respectively, along with their epistemic and aleatory uncertainties. We design a separate multi-input network using standard convolutional layers to estimate the back-azimuth angle, and its epistemic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.01144","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/1912.01144/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":"1912.01144","created_at":"2026-07-05T01:55:44.435522+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.01144v1","created_at":"2026-07-05T01:55:44.435522+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.01144","created_at":"2026-07-05T01:55:44.435522+00:00"},{"alias_kind":"pith_short_12","alias_value":"QZNBHKZVIUWZ","created_at":"2026-07-05T01:55:44.435522+00:00"},{"alias_kind":"pith_short_16","alias_value":"QZNBHKZVIUWZJMS4","created_at":"2026-07-05T01:55:44.435522+00:00"},{"alias_kind":"pith_short_8","alias_value":"QZNBHKZV","created_at":"2026-07-05T01:55:44.435522+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.18099","citing_title":"An Attention-based Framework with Multistation Information for Earthquake Early Warnings","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QZNBHKZVIUWZJMS4GRNTC6BTRV","json":"https://pith.science/pith/QZNBHKZVIUWZJMS4GRNTC6BTRV.json","graph_json":"https://pith.science/api/pith-number/QZNBHKZVIUWZJMS4GRNTC6BTRV/graph.json","events_json":"https://pith.science/api/pith-number/QZNBHKZVIUWZJMS4GRNTC6BTRV/events.json","paper":"https://pith.science/paper/QZNBHKZV"},"agent_actions":{"view_html":"https://pith.science/pith/QZNBHKZVIUWZJMS4GRNTC6BTRV","download_json":"https://pith.science/pith/QZNBHKZVIUWZJMS4GRNTC6BTRV.json","view_paper":"https://pith.science/paper/QZNBHKZV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.01144&json=true","fetch_graph":"https://pith.science/api/pith-number/QZNBHKZVIUWZJMS4GRNTC6BTRV/graph.json","fetch_events":"https://pith.science/api/pith-number/QZNBHKZVIUWZJMS4GRNTC6BTRV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QZNBHKZVIUWZJMS4GRNTC6BTRV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QZNBHKZVIUWZJMS4GRNTC6BTRV/action/storage_attestation","attest_author":"https://pith.science/pith/QZNBHKZVIUWZJMS4GRNTC6BTRV/action/author_attestation","sign_citation":"https://pith.science/pith/QZNBHKZVIUWZJMS4GRNTC6BTRV/action/citation_signature","submit_replication":"https://pith.science/pith/QZNBHKZVIUWZJMS4GRNTC6BTRV/action/replication_record"}},"created_at":"2026-07-05T01:55:44.435522+00:00","updated_at":"2026-07-05T01:55:44.435522+00:00"}