Detection and Location of Low‐Frequency Earthquakes Using Cross‐Station Correlation
Bibliographic record
Abstract
Source geometry and simple propagation of S waves generated by low‐frequency earthquakes (LFEs) in northern Cascadia results in strong waveform coherence on horizontal channels for stations at smaller epicentral distances. As recognized by previous workers, this cross‐station similarity can be exploited for detection of LFEs in tremor. We develop a cross‐station correlation approach based on waveform coherence and travel‐time consistency that exploits a full complement of network stations, and we demonstrate its application to separate arrays on Vancouver Island and Washington state. Our approach yields thousands of impulsive LFE detections per slow‐slip episode and hundreds of events between episodes. LFE epicentral distributions reveal the presence of well‐defined regions of high asperity density with those regions farther downdip that are active during interepisodic tremor and slow‐slip periods. The LFE epicenters also display pronounced spatiotemporal clustering that compares favorably with independent tremor and LFE template catalogs and that defines rapid tremor reversals with complex propagation patterns. The LFE source depths can be estimated for some detections in which P waves are identified on the vertical channel through correlation with the horizontal‐component S waveforms. LFE hypocenters cluster between two recently developed plate interface models below southern Vancouver Island. Our approach enables the identification of high signal‐to‐noise‐ratio impulsive LFE detections from targeted regions on the plate boundary for generation of LFE templates to be used in studies of structure and seismogenesis in Cascadia. Online Material: Additional maps of station density for southern Vancouver Island, epicenter locations for northern Washington, and additional time–distance plots for southern Vancouver Island.
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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 | 0.001 |
| 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".