2D inversion of 3D magnetotelluric data: The Kayabe dataset
Bibliographic record
Abstract
In the last two Magnetotelluric Data Interpretation Workshops (MT-DIW) the participants were asked to model the Kayabe magnetotelluric dataset, a dense (100 m) grid of thirteen lines, with thirteen stations in each line. Bahr’s phase-sensitive skew and the Groom and Bailey decomposition were used to select those lines for which the data could be considered two-dimensional. For these lines we used a 2D inversion algorithm to obtain a series of resistivity models for the earth. Finally, we constructed a 3D model using the 2D models and critically examined the validity and practicality of this approach based on 3D model study. We found that in the Kayabe dataset case the common practice of using 2D models to depict 3D models, can only be used to create a starting model for 3D interpretation. The sequential 2D models as a representation of a 3D body is unacceptable in terms of fit to the observed data. We question the validity of some of the conductivity structures in the 2D models, as they can be mere artifacts created by the algorithm to match 3D effects.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".