Comparison of sparse Gabor-based methods for detection of microseismic events
Bibliographic record
Abstract
ABSTRACT Event detection and selection is a challenging and time-consuming step in microseismic data processing because signals often are embedded in noisy recordings. Automated triggering-based algorithms can detect many potential events in the data. However, their precision rates often are low, thus requiring substantial manual labor to select desired events of interest. This study investigates two time-frequency methods which combine signal enhancement with automated event detection and selection, namely, (1) the sparse Gabor transform and (2) the neighboring block thresholding. Both methods use thresholding in the time-frequency domain to increase signal enhancement, followed by an energy detection criterion, leading to improved event detection with higher precision rates. However, the neighboring block thresholding causes amplitude fidelity issues and we observe changes in the relative and maximum amplitudes of the waveforms reconstructed from the thresholded coefficients. Conversely, the sparse Gabor transform attenuates the noise significantly while preserving the signals. Thus, this time-frequency method is suitable for enhanced event detection and subsequent processing, including magnitude estimation and moment tensor inversion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".