Seismic interferometry using non-volcanic tremor in Cascadia
Bibliographic record
Abstract
The Green’s function for a source and receiver located on the Earth’s surface over a heterogeneous medium can be recovered by cross-correlating and integrating the transmission response of noise fields recorded at the two surface locations. This assertion relies on the assumption that the noise field is generated by random, independent sources distributed over a surface surrounding the heterogeneity, with the dominant contributions to the integral over source location occurring at stationary points of the integrand for a given structure. The majority of seismological research on seismic interferometry to date has focussed on surface wave applications, due in part to the general absence of deep, high-frequency noise sources. In this study, I investigate the possibility of using the recently documented, deep-seated, non-volcanic tremor on the Cascadia subduction zone as a noise source for seismic interferometry to recover scattered body wave contributions to the surface Green’s function. Tremors in the vicinity of the POLARIS-BC array for 2004 and 2005 were documented, and data for stations TWKB, MGCB and LZB were filtered and cross-correlated for all components of stations pairs TWKB-MCGB and TWKB-LZB. TWKB and MGCB correlations are generally superior, generating a large highly reproducible arrival at 4.5 s in 2004 and 2005 for combinations of vertical with radial or transverse components. TWKB and LZB correlations are less reproducible, but still yield, for the same components, a strong arrival at 3.5 s for 2005. Upon consideration of source/receiver geometry, polarity and appearance at positive versus negative lags, I interpret these arrivals to represent contributions travelling as S-to-P-to-S en route from source to free-surface to scatterer to receiver. Arrival times suggest that these signals originate at depths between 9 and 12 km, coincident with an interval of strong reflectivity imaged in the 1987 Lithoprobe Vancouver Island transect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".