Detailed nucleation process and mechanism of the July 2019 Mw 6.4 Ridgecrest, California earthquake
Bibliographic record
Abstract
Foreshocks provide valuable information on the nucleation process and mechanism of impending earthquakes. In this study, we utilized the Match&Locate method to build a high-precision foreshock catalog for the July 2019 Mw 6.4 Ridgecrest, California earthquake. The Mw 6.4 mainshock was preceded by 40 foreshocks within ~2 hours (on July 4, 2017 from 15:35:29 to 17:32:52, UTC). Their spatiotemporal distribution reveals a complex seismogenic structure consisting of multiple fault strands, which were connected as a throughgoing fault by later foreshocks and eventually accommodated the 2019 Mw 6.4 mainshock. To better understand the nucleation mechanism, we conducted a series of analysis for the foreshocks including repeating earthquake identification, rupture directivity inversion, and Coulomb stress change estimation. We identified a pair of small earthquakes with close magnitude, high waveform similarity, and high cross-spectral coherence at the early nucleation stage. However, we cannot confirm if they are repeating earthquakes due to their low magnitude and insufficient sampling rate. Thus, the initial nucleartion mechanism is unclear to us. Following the largest ML 4.0 foreshock, we found the majority of its aftershocks and the Mw 6.4 mainshock occurred within regions of increasing Coulomb stress, indicating that they were triggered by stress transfer. Our study suggests that the nucleation of the Mw 6.4 mainshock can be prominently explained by cascade triggering even though we cannot exclude the possible existence of a minor aseismic slip process at the early stage.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".