Using a modified time-reverse imaging technique to locate low-frequency earthquakes on the San Andreas Fault near Cholame, California
Bibliographic record
Abstract
We present a new method to locate low-frequency earthquakes (LFEs) within tectonic tremor episodes based on time-reverse imaging techniques. The modified time-reverse imaging technique presented here is the first method that locates individual LFEs within tremor episodes within 5 km uncertainty without relying on high-amplitude P-wave arrivals and that produces similar hypocentral locations to methods that locate events by stacking hundreds of LFEs without having to assume event co-location. In contrast to classic time-reverse imaging algorithms, we implement a modification to the method that searches for phase coherence over a short time period rather than identifying the maximum amplitude of a superpositioned wavefield. The method is independent of amplitude and can help constrain event origin time. The method uses individual LFE origin times, but does not rely on a priori information on LFE templates and families. We apply the method to locate 34 individual LFEs within tremor episodes that occur between 2010 and 2011 on the San Andreas Fault, near Cholame, California. Individual LFE location accuracies range from 2.6 to 5 km horizontally and 4.8 km vertically. Other methods that have been able to locate individual LFEs with accuracy of less than 5 km have mainly used large-amplitude events where a P-phase arrival can be identified. The method described here has the potential to locate a larger number of individual low-amplitude events with only the S-phase arrival. Location accuracy is controlled by the velocity model resolution and the wavelength of the dominant energy of the signal. Location results are also dependent on the number of stations used and are negligibly correlated with other factors such as the maximum gap in azimuthal coverage, source–station distance and signal-to-noise ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".