Bayesian inversion for elastic properties and microseismic event locations in HTI media: A physical modeling study
Bibliographic record
Abstract
One-dimensional layered isotropic velocities are typically used to locate microseismic events in conventional microseismic data processing. To account for anisotropy caused by the presence of a set of aligned vertical fractures, an inversion procedure is presented to simultaneously estimate microseismic event locations and the velocity model for horizontal transverse isotropic (HTI) media. The procedure employs Bayesian inference via Markov-chain Monte Carlo (McMC) sampling with parallel tempering and diminishing adaptation to ensure efficient sampling of the parameter space. This algorithm is exemplified with an application to a physical modeling data set, in which a phenolic CE material is used to simulate the HTI medium. In contrast to deterministic inversion algorithms, this approach provides a natural nonlinear uncertainty quantification by approximating the posterior probability density with an ensemble of model-parameter sets for both HTI velocity parameters and event locations. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 9:20 AM Presentation Time: 9:20 AM Location: Poster Station 11 Presentation Type: Poster
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".