Cooperative inversion of multiphysics data using joint minimum entropy constraints
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT The inversion of geophysical data is a classical ill‐posed problem that is complicated by considerable uncertainty and ambiguity in the resulting inverse models. One way to reduce this uncertainty is based on the cooperative inversion of multiphysics data. In most cases, the information provided by different geophysical data is mutually complementary, making it natural to consider a cooperative (joint) inversion of different geophysical data to a shared earth model. Many existing joint inversion methods are based on the known relationships between the different physical properties of the rocks. This paper introduces a new approach to cooperative geophysical inversion, which does not require a priori knowledge about specific empirical or statistical relationships between the different models' parameters. Our approach is based on a novel joint minimum entropy stabilizer, which forces the simplest multiphysics solution that fits the multimodal data. This novel stabilizer characterizes the degree of joint disorder or uncertainty in the distribution of the different model parameters. By minimizing this stabilizing functional in the framework of the regularized inversion, we produce a consistent image of the same geological structure expressed in different geophysical data. We implement a joint minimum entropy stabilizer in the context of re‐weighted regularized conjugate gradient inversion. The paper demonstrates the developed method using a synthetic model study and by joint inversion of airborne gravity and magnetic data collected in the McFaulds Lake area of Ontario, Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it