Optimal Multichannel Stretch Factors for Estimating Changes in Seismic Velocity: Application to the 2012 Mw 7.8 Haida Gwaii Earthquake
Bibliographic record
Abstract
ABSTRACT We propose new methods for assessing temporal changes in seismic velocity using the S-wave coda for repeating earthquakes and cross-correlation functions of ambient noise. For a pair of seismic waveforms representing a common source–receiver path, the relative change in path-averaged velocity over the corresponding time interval is directly proportional to the factor by which one waveform needs to be stretched or compressed with respect to the other to achieve maximum coherence. For an arbitrary number of waveforms, initial pair-wise stretch factors determined through standard approaches can be improved through solution of an overdetermined system and further refined through an iterative approach exploiting the singular value decomposition to minimize rank of the stretched waveform section. We apply this combined approach to both repeating earthquakes and ambient noise correlations for Haida Gwaii in western Canada, the site of a Mw 7.8 thrust earthquake in 2012. Optimal stretch factors for repeating earthquake families indicate that path-averaged S velocities dropped by up to 0.16% after the earthquake. Ambient noise correlations indicate that velocities dropped by between 0.26% and 0.39%, which we interpret to be more pronounced in the uppermost levels of the crust. We explore these results in terms of changes in crustal porosity and hydrogeologic conditions by considering the observation that hot spring activity on Haida Gwaii ceased following the 2012 mainshock and recovered over the next several years.
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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) | 0.001 | 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) | 0.000 | 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".