Petrophysically guided geophysical inversion using a dynamic Gaussian mixture model prior
Bibliographic record
Abstract
Geophysical inversions are an essential tool for mapping the subsurface. However, the image of the underground retrieved from an inversion rarely benefits from the full range of knowledge. Geologic knowledge concerning petrophysical relationships and geologic features are not easily incorporated into traditional geophysical inversions. By developing the geophysical inverse problem from a probabilistic perspective, we redesign the objective function and the iteration steps as a suite of cyclic optimization problems across the geophysical, petrophysical and geological data, each one benefiting from the others. By quantitatively linking the geophysical, petrophysical and geological data into a single framework, we seek to recover a final inverted model that reproduces the observed petrophysical characteristics and desired geological features while fitting the geophysical data. For this purpose, we propose to replace the Gaussian prior in the Tikhonov approach by a Gaussian mixture model. After each geophysical model update, the mixture parameters are determined by the geophysical model and the expected petrophysical characteristics of the lithologies through another optimization process using the Expectation-Maximization algorithm. We then classify into rock units the model cells according to the petrophysical and geological information. These two steps over the petrophysical and geological data can be understood as a dynamic update of the reference model and weights to represent the knowledge gained at each iteration and guide the inversion towards reproducing the expected petrophysical and geological characteristics. Presentation Date: Wednesday, October 17, 2018 Start Time: 8:30:00 AM Location: 213A (Anaheim Convention Center) Presentation Type: Oral
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".