Probabilistic multiphysics inference with flexible coupling and data covariance estimation
Bibliographic record
Abstract
We consider combining complementary information contained in multiple data types recorded from distinct physical processes interacting with the Earth’s subsurface. Such multiphysics inference of non-invasive geophysical observations can improve the resolution of Earth structure and processes, but is plagued by many subjective choices that practitioners are commonly required to make. We present the method of probabilistic multiphysics inference that employs Bayesian statistics to overcome several requirements for subjective choices. To ensure appropriate data weights, full data covariance matrices are estimated during Markov chain Monte Carlo sampling. The layering structure of the subsurface is estimated with flexible coupling, where the number of homogeneous layers is treated as unknown and the number of geophysical parameters for each layer are unknown. The latter permits flexible coupling such that parameters for different physical processes are not required to share the same layering structure, which avoids over-parametrization. We consider two examples with elastic and electromagnetic waves. In the first example, the thicknesses of shallow (tens of meters) active and permafrost layers are better constrained by probabilistic multiphysics inference. The second example resolves cratonic structure, with reduced uncertainty of a sedimentary basin and for the depth of the lithosphere-asthenosphere boundary. [Work supported by an NSERC Discovery Grant.]
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".