Pattern informatics approach to earthquake forecasting in 3D
Bibliographic record
Abstract
Abstract Natural seismicity is correlated across multiple spatial and temporal scales, but correlations in seismicity prior to a large earthquake are locally subtle (e.g. seismic quiescence) and often prominent in broad scale (e.g. seismic activation), resulting in local and regional seismicity patterns, e.g. a Mogi's donut. Recognizing that patterns in seismicity rate are reflecting the regional dynamics of the directly unobservable crustal stresses, the Pattern Informatics (PI) approach was introduced by Tiampoet al. and Rundleet al. in 2002. In this study, we expand the PI approach to forecasting earthquakes into the third or vertical dimension, and illustrate its further improvement in the forecasting performance through case studies of both natural and synthetic data. The PI characterizes rapidly evolving spatio‐temporal seismicity patterns as angular drifts of a unit state vector in a high‐dimensional correlation space, and systematically identifies anomalous shifts in seismic activity with respect to the regional background. 3D PI analysis is particularly advantageous over 2D analysis in resolving vertically overlapped seismicity anomalies in a highly complex tectonic environment. Case studies will help to illustrate some important properties of the PI forecasting tool. Copyright © 2009 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".