{
  "annotation_id": "2fd61d1b-cfd5-4628-8910-c2f6a74d0a51",
  "date_created": "2026-03-02T18:00:59.872000Z",
  "date_modified": "2026-03-02T18:00:59.872000Z",
  "file_hash": "5d9fb186e710f30b3b7421c28dc6019d4a37b4fa59fb5d236816cf1af11d4cb4",
  "private": false,
  "record": {
    "abstract": "Recent studies in the literature have introduced a new approach to earthquake\nforecasting based on representing the space-time patterns of localized\nseismicity by a time-dependent system state vector in a real-valued Hilbert\nspace and deducing information about future space-time fluctuations from the\nphase angle of the state vector. While the success rate of this Pattern\nInformatics (PI) method has been encouraging, the method is still in its\ninfancy. Procedural analysis, statistical testing, parameter sensitivity\ninvestigation and optimization all still need to be performed. In this paper,\nwe attempt to optimize the PI approach by developing quantitative values for\n\"predictive goodness\" and analyzing possible variations in the proposed\nprocedure. In addition, we attempt to quantify the systematic dependence on the\nquality of the input catalog of historic data and develop methods for combining\ncatalogs from regions of different seismic rates.",
    "arxiv_id": "physics/0508147",
    "authors": [
      "James R. Holliday",
      "John B. Rundle",
      "Kristy F. Tiampo",
      "Bill Klein",
      "Andrea Donnellan"
    ],
    "categories": [
      "physics.geo-ph",
      "physics.data-an"
    ],
    "doi": "10.1007/s00024-006-0131-1",
    "title": "Systematic procedural and sensitivity analysis of the pattern   informatics method for forecasting large (M \u003e 5) earthquake events in   southern California",
    "url": "https://arxiv.org/abs/physics/0508147"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "d6ac34ef-e65e-4d89-a592-6f556764e717",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}