Tackling the challenges of tectonic tremor localization using differential traveltimes and Bayesian inversion
Bibliographic record
Abstract
SUMMARY Episodic tremor and slip events in Northern Cascadia produce low-frequency, emergent seismic signals, often referred to as tectonic tremor. Methods designed to locate tremor face two challenges that increase the likelihood of producing low quality catalogues: first, signal arrival and duration are often poorly defined; second, high rates of tremor activity during episodes can cause multiple signals to be indiscernible from each other. In this paper, we present a new method of locating tectonic tremor using differential traveltimes from a waveform-envelope cross-correlation in a grid-based Bayesian inversion. To address the aforementioned challenges, we use a recently developed three-dimensional shear wave velocity model to compute traveltimes, and include processes to remove data outliers, estimate data error statistics, and quantify uncertainties within the Bayesian framework. Although this method is designed for tremor, to test the approach we consider a set of 58 local earthquakes between magnitudes −0.07 and 2.6 in the Southern Vancouver Island region and obtain well-constrained relocations. Residuals between official catalogue values and our relocations are quantized with respect to the 1 km grid resolution of the inversion, and average 2.7 km in epicentre and 5.2 km in depth. Analysis shows that depths of relocations are sensitive to horizontal variations and simplifications in velocity models. We then present our catalogue of tremor events during the 2004 episodic tremor and slip event beneath Southern Vancouver Island, Canada. Median uncertainties of tremor events quantified by 95 per cent credibility interval widths in a 1 km grid are 5 km and 9.km in horizontal and depth directions, respectively (1.2 km and 2.3 km using traditional standard deviation-based uncertainties). Comparison of our catalogue with previously published work demonstrates that our new method yields a good detection rate, a greater degree of epicentral clustering, and better depth resolution of tremor events. Catalogues produced using this new method may help to provide insight into the spatial extent of tremor, especially in depth, by yielding enhanced constraints on source locations on a regional scale.
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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) | 0.000 | 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 teacher head, 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".