Evaluation of Cross-Correlation Methods on a Massive Scale for Accurate Relocation of Seismic Events in East Asia
Bibliographic record
Abstract
A weakness of traditional methods of seismic event location is their susceptibility to errors in picking arrival times of regional phases such as Pn, Pg, and teleseismic phases. We evaluate the practical utility of waveform cross-correlation as a way to reduce or remove pick error, applied to large datasets in seismically active regions of East Asia, for purposes of obtaining significant improvements in event location. Key ideas in this project emerged during 2000-2003 from work of a Lamont-led consortium to calibrate stations in East Asia. Thus, we found for a number of regions that one of the best methods for obtaining ground truth events was to use waveform cross-correlations that enabled excellent (sub-kilometer) precision in relative locations. Additional (often, non-seismic) information then allowed absolute locations to be estimated. A preliminary study of China has shown that 1300 out of 14,000 earthquakes (approximately 9%) exhibit high cross-correlations with at least one other earthquake, and on this basis we have found 494 sets of cross correlated multiplets, ranging from doublets to one multiplet with 26 events. We apply waveform cross correlations to the problem of event location in four project areas, namely China, parts of Eastern Canada, the Central United States (New Madrid), and (for purposes of validation of our overall method) California. For each project area, we carry out the three steps of data acquisition (digital waveforms, traditional phase picks, catalog information), waveform cross-correlations (exploring the effects of different filters, spatial event separation, and differences in magnitude), and event location (double-difference). We produce data sets of waveforms, cross-correlation measurements of arrival differences, and sets of precise locations.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".