Magnitude calibration for microseismic events from hydraulic fracture monitoring
Bibliographic record
Abstract
Magnitude estimation for microseismic events is critical to microseismic mapping of hydraulic fracture stimulation, discrete fracture network (DFN) modeling, as well as accurate estimated stimulated rock volume (SRV). We estimated magnitude of microseismic events based on the mean peak ground velocity (PGV), calibrated by a reference moment magnitude obtained from moment tensor inversion. For 265 good signal to noise ratio (SNR) events recorded using a near-surface seismic array (receivers buried at an approximate depth of 30m), the square of the correlation coefficient (R2) is 0.95 between moment magnitudes and calibrated magnitudes based on mean PGV values. The high correlation coefficient suggests that reliable magnitude estimations for detected events from surface or near surface observations are obtainable. The b-value based on frequency-magnitude distribution for 10,594 events detected from this project is about 2.0, a typical value for fracturing related events.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".