Waveform similarity for quality control of event locations, time picking, and moment tensor solutions
Bibliographic record
Abstract
ABSTRACT Multiplet analysis assumes that events with highly similar waveforms originate in the same source region and with the same source mechanism. We have determined how waveform similarity could be used at different stages of microseismic processing and interpretation for quality-control purposes to assess internal consistency among events from the same multiplet group. Crosscorrelation (CC) values decayed with interevent distance, and we found that doublets with large separation distances revealed location errors due to mispicks. Using histograms of differential times between highly correlated events (doublets), time-picking errors could also be detected. Waveform correlation could also detect inconsistencies in derived source mechanisms. Doublets should fall within similar regions in the Hudson source-type plots even for events with large nondouble-couple components or when the inversion results were internally inconsistent. A similar assessment was done by plotting the P- and T-axes of multiplets in a focal sphere. We used a minimum CC threshold of 90%, a sufficiently high level, such that the assumption of waveform similarity remained valid, and we analyzed a microseismic data set recorded during two stages of a hydraulic fracturing experiment to demonstrate these quality-control procedures. These techniques could be used in other settings, such as geothermal studies, mining projects, reservoir monitoring, and earthquake seismology and were most beneficial in scenarios in which repeating events were expected, facilitating the detection of compounded errors commonly encountered in microseismic analysis.
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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".