Earthquake precursors: activation or quiescence?
Bibliographic record
Abstract
We discuss the long-standing question of whether the probability for large earthquake occurrence (magnitudes m > 6.0) is highest during time periods of smaller event activation, or highest during time periods of smaller event quiescence. The physics of the activation model are based on an idea from the theory of nucleation, that a small magnitude earthquake has a finite probability of growing into a large earthquake. The physics of the quiescence model is based on the idea that the occurrence of smaller earthquakes (here considered as magnitudes m > 3.5) may be due to a mechanism such as critical slowing down, in which fluctuations in systems with long-range interactions tend to be suppressed prior to large nucleation events. To illuminate this question, we construct two end-member forecast models illustrating, respectively, activation and quiescence. The activation model assumes only that activation can occur, either via aftershock nucleation or triggering, but expresses no choice as to which mechanism is preferred. Both of these models are in fact a means of filtering the seismicity time-series to compute probabilities. Using 25 yr of data from the California–Nevada catalogue of earthquakes, we show that of the two models, activation and quiescence, the latter appears to be the better model, as judged by backtesting (by a slight but not significant margin). We then examine simulation data from a topologically realistic earthquake model for California seismicity, Virtual California. This model includes not only earthquakes produced from increases in stress on the fault system, but also background and off-fault seismicity produced by a BASS–ETAS driving mechanism. Applying the activation and quiescence forecast models to the simulated data, we come to the opposite conclusion. Here, the activation forecast model is preferred to the quiescence model, presumably due to the fact that the BASS component of the model is essentially a model for activated seismicity. These results lead to the (weak) conclusion that California seismicity may be characterized more by quiescence than by activation, and that BASS–ETAS models may not be robustly applicable to the real data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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; both teacher heads agree on what is shown here.
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".