Multichannel Alignment of <i>S</i> Waves
Bibliographic record
Abstract
ABSTRACT High-resolution earthquake locations and structural inversions using body waves rely on precise delay-time measurements. Subsample accuracy can be realized for P waves using multichannel cross correlation (MCCC), as developed by VanDecar and Crosson (1990), which exploits redundancy in pairwise cross correlations to determine delays between similar waveforms in studies of mantle structure using teleseismic sources (common source and multiple stations) and regional studies of structure and seismicity (multiple sources and common station). For regional S waves, alignment is complicated by the additional degree of freedom in waveform polarity that is expressed for sources with different moment tensors. Here, we recast MCCC within a principal component framework and demonstrate the equivalence between maximizing waveform correlation and minimization of various singular value–based objective functions for P waves. The singular-value framework is more general and leads naturally to an MCCC linear system for S waves that possesses an order of magnitude greater redundancy than that for P waves. Robust L1 solution of the system provides an effective means of mitigating outliers at the expense of subsample precision. Residual time shifts associated with higher-order singular vectors are employed in an iterative adaptive alignment that achieves subsample resolution. We demonstrate application of the approach on a seismicity cluster within the northern Cascadia crustal fore-arc.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".