Bayesian source-mechanism inversion for microearthquakes
Bibliographic record
Abstract
Using a physics-based shear-tensile crack model, we develop a Bayesian approach to simultaneously calculate source mechanisms for a set of microearthquakes and rigorously quantify the uncertainties of model parameters. To that end, we use the normalized displacement amplitudes of direct P-waves as observations. The Bayesian inference employs Markov-chain Monte Carlo (McMC) sampling with parallel tempering and principal component diminishing adaption to ensure efficient sampling. The model-parameter uncertainties are quantified through a series of posterior distributions. In the inversion, we adopt new prior bounds for model parameters to reduce the number of modes within the marginal posterior distribution for strike and overcome the issue of half-Gaussian distribution for the dip of near-vertical faults. Finally, the effectiveness of the proposed algorithm is demonstrated through the application to three representative events in a passive seismic dataset acquired during a four-well hydraulic-fracture completion program west of Fox Creek, Alberta.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".