Seismicity‐Scanning Based on Navigated Automatic Phase‐Picking
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We propose a new method, named Seismicity‐Scanning based on Navigated Automatic Phase‐picking (S‐SNAP), that is capable of delineating complex spatiotemporal distributions of seismicity. This novel algorithm takes a cocktail approach that combines source scanning, kurtosis‐based phasepicking, and the maximum intersection location technique into a single integrated workflow. This method is automated, detecting and locating earthquakes efficiently, comprehensively, and accurately. We apply S‐SNAP to a data set recorded by a dense local seismic array during a hydraulic fracturing operation to test this novel approach and to demonstrate its effectiveness in relation to existing methods. Overall, S‐SNAP found about 3.5 times as many high‐quality events as a template matching‐based catalogue. All events in the previous catalogue are identified with similar epicenters, depths, and magnitudes, while no false detections are found by visual inspection.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it