Bibliographic record
Abstract
These notebooks were used to generate the figures shown in the article A framework for petrophysically and geologically guided geophysical inversion. We perform PGI over various examples: a 1D MT layered-earth, a DC profile over 2 cylinders, a stitched FDEM survey for saline water intrusion Contents There are 3 notebooks in this repository: 1_MT_PGI_Sharp_Smooth.ipynb: Magnetotelluric data are acquired over a layered-earth that has sharp and smooth features. The PGI algorithm is provided with the true petrophysical distribution and the goal is to use it along with the MT data to find a solution that has the desired contrast features. 2_DC_PGI_2cylinders.ipynb: A DC resistivity profile is acquired over two cylinders. We illustrate the performance of this framework when no physical property mean values are available, and compared it to the result with full petrophysical information. We highlight then how geological information from borehole logs can be incorporated into this framework. 3_FDEM_PGI_Bookpurnong.ipynb This example illustrates an application of the PGI approach on a field frequency-domain EM dataset, in conjunction with a structurally constraining regularization, without using extensive geological or petrophysical information. We demonstrate how to use this framework to test hypothesis, such as a recovering a specific number of distinct units, and to build confidence, or doubts, in geological features displayed by the inversions. Update: 0.0.4: Stricter Requirements for reproducibility
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.341 | 0.244 |
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".