Analysis of instrument self-noise and microseismic event detection using power spectral density estimates
Bibliographic record
Abstract
Apart from the event magnitude, hypocentral distance, and background noise level, the instrument self-noise can also act as a major constraint for the detection of weak microseismic events in particular for deployments in quiet environments such as below 1.5-2km depths. Instrument self-noise levels that are comparable or above background noise levels may not only complicate detection of weak events at larger distances but also challenge methods such as seismic interferometry which aim at analysis of coherent features in the noise wavefields to reveal subsurface structure. In this article we use power spectral densities to estimate the instrument self-noise for a sample dataset and show how it could affect further analysis. We also suggest that the variations of the spectral powers in a timefrequency representation can be used as a new criterion for event detection and therefore propose a new event time picking algorithm. Compared to the common Short-time average and Long-time average (STA/LTA) method, our suggested technique requires easier parameter settings and detects small events with anomalous spectral powers with respect to an estimated background noise spectrum.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".