Bibliographic record
Abstract
Deconvolution of seismic data is an important component of signal processing that aims to remove the seismic source from seismograms, thereby isolating the Green’s function. By considering seismograms of multiple earthquakes from similar locations recorded at a given station and that therefore share the same Green’s function, we investigate a system of equations where the unknowns are the sources and source durations. Our solution is derived using direct linear inversion to recover the sources and Newton’s method to recover source durations. For the short seismogram durations considered, we are able to recover source time functions for noise levels at 1% of the direct P -wave amplitude. However, the nonlinearity of the problem renders the system expensive to solve and sensitive to noise; therefore consideration is limited to short seismograms with high signal-to-noise ratio (SNR). When SNR levels are low, but a large multiplicity of seismograms representing a common source-receiver path are available, we can apply a different deconvolution approach to recover the Green’s function. In an application to tectonic tremor in northern Cascadia, we implement an iterative blind deconvolution method that involves correlation, threshold detection and stacking of 1000’s of low frequency earthquakes (LFEs) that form part of tremor to generate templates that can be considered as empirical Green’s functions. We exploit this identification to compute hypocentres and moment tensors. LFE hypocentres follow the general epicentral distribution of tremor and occur along tightly defined surfaces in depth. The majority of mechanisms are consistent with shallow thrusting in the direction of plate motion. We analyze the influence of ocean tides on the triggering of LFEs and find a spatially variable sensitivity to tidally induced up-dip shear stress (UDSS), suggesting that tidal sensitivity must partially depend on laterally heterogeneous physical properties. The majority of LFEs fail during positive and increasing UDSS, consistent with combined contributions from background slow slip and from tides acting directly on LFEs. We identified rapid tremor reversals in southern Vancouver Island with higher sensitivity to UDSS than the main front and which at least partially explains an observed increase in LFE sensitivity to UDSS with time.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".