Automated processing strategies for ambient seismic data
Bibliographic record
Abstract
ABSTRACT Extracting body‐wave arrivals from ambient seismic recordings remains a challenging task, largely because ambient records are usually dominated by surface‐wave energy. Most ambient seismic data‐processing strategies aimed at enhancing body‐wave energy focus on a cross‐correlation plus stack methodology. While this approach is useful for interferometric investigations, it effectively squares the magnitude of unwanted coherent noise events (e.g. surface waves, burst‐like or strong monochromatic energy) that commonly overpower ambient body‐wave signal. Accordingly, coherent noise events are usually treated with a binary accept‐or‐reject decision of individual data windows based on root‐mean‐squared energy considerations. Applying a data‐processing workflow to uncorrelated ambient seismic data represents an alternate strategy for mitigating coherent noise. However, this pre‐stack methodology requires significant computational effort due to the often terabyte‐sized data volumes. To make this approach feasible, we outline an automated processing workflow to automatically identify and mitigate coherent noise events that otherwise does not severely degrade the remaining waveforms. After each processing step, we apply a number of quality control measures to monitor the convergence rate of cross‐correlation plus stack waveforms and for evidence of emerging body‐wave reflection events. We apply the processing flow to an ambient seismic data set acquired on a large‐N array at a mine site near Lalor Lake, Manitoba, Canada. Our quality control analyses suggest that automated preprocessing of uncorrelated ambient seismic recordings successfully mitigates unwanted impulsive and monochromatic coherent noise events. Accordingly, we assert that applying an automated data‐processing approach would be beneficial for body‐wave and other imaging and inversion analyses applied to ambient seismic recordings.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".