Earthquake-earthquake triggering in natural swarms and fluid-induced seismicity
Bibliographic record
Abstract
Aftershock cascades and aftershock zones play an important role in forecasting seismic activity in both natural and human-made situations. While their behavior including the spatial aftershock zone scaling has been the focus of many studies in tectonic settings finding, for example, long-range earthquake-earthquake triggering in the near-field, this is not the case in situations where the seismic activity is primarily driven by fluids and the diffusion of excessive pore pressure. Here, we probe three different seismic settings that are believed to be influenced by fluid diffusion. The natural swarm in i) the Long Valley Caldera and the suspected swarms in ii) the Yuha Desert, both located in California, and associated earthquake-earthquake triggering behavior are compared against induced seismicity related to large scale wastewater disposal in iii) Oklahoma and southern Kansas. All settings exhibit a significant amount of event-event triggering highlighting the importance of secondary processes for the overall seismicity. We find an almost identical temporal event-event triggering behavior including the Omori-Utsu relation and the associated productivity relation. In terms of the spatial triggering density, both cases i) and iii) show a rapid decay beyond their rupture length. This proves that narrow spatial “aftershock” zones are not specific to induced seismicity but also occur in natural settings. Typical of most tectonic settings, a relatively long-range behavior is observed in case ii) suggesting that fluid migration might not be the dominant driving mechanism of the seismic activity and/or that the underlying structure of the fault network may control the secondary earthquake-earthquake triggering and its spatial evolution.
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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 | 0.000 | 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) | 0.001 | 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".