Implementing geological rules within geophysical inversion: A PGI perspective
Bibliographic record
Abstract
Inferring geologically meaningful information from a geophysical inversion is a challenging task. Moreover, prior knowledge about the petrophysical contrasts or the relationships between various geological units can also prove difficult to translate into quantitative input for the inversion. In previous works, we developed a Petrophysically and Geologically guided Inversion (PGI) framework that enables desired petrophysical characteristics to be reproduced. This information is encoded into the objective function’s smallness through a Gaussian Mixture Model (GMM). The resulting discrete geological representation of the subsurface thus fits both the geophysical and petrophysical information. The way we included geological information was limited to favoring the occurrence of chosen rock units in user-defined areas on a cell-by-cell approach. Transferring geological information from one area to another, such as an expected stratigraphy, was not easily done. Moreover, structural information (dip orientation, etc.), which by definition depends on multiple cells at once, was left to the bjective function’s smoothness, which acts on the physical property models rather than on the geological representation itself. We improve upon the existing PGI framework to make the inversion result geologically realistic by including geological rules as part of the process that builds the geological representation throughout the inversion’s iterations. For this purpose, we incorporate image segmentation tools using Markov Random Field (MRF) as part of the PGI framework. The final recovered model fits geophysical and petrophysical information while reproducing geological characteristics, thus providing a more faithful and informed representation of the underground.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".