Three‐Dimensional Inversion of Magnetotelluric Data for a Resistivity Model With Arbitrary Anisotropy
Bibliographic record
Abstract
Abstract Electrical anisotropy is increasingly recognized as an important aspect of the resistivity models required to explain magnetotelluric (MT) observations. However, a limited number of practical MT inversion algorithms that can consider anisotropy have been published to date. To address this problem, we have developed a three‐dimensional (3‐D) MT inversion algorithm that recovers a 3‐D resistivity model that considers arbitrary electrical anisotropy. The inversion uses the same inversion algorithm as the widely used ModEM inversion algorithm, and a novel forward modeling algorithm to consider the anisotropic Earth. The algorithm was tested on both synthetic and field MT data. Inversions considered both a completely general anisotropy tensor with six components and approximations with less parameters. Synthetic inversions show that the two horizontal components of resistivity and the anisotropy strike can be well recovered, while the vertical component of resistivity is poorly resolved, primarily because current flow in MTs is dominantly horizontal. The synthetic examples confirm the limitation of the axial anisotropic inversion technique when applied to MT data produced by a resistivity model with arbitrary anisotropy. The synthetic inversions also showed that inversion of data from an isotropic model will not result in an artificially anisotropic model. Compared to the isotropic inversion model of the real MT data, the anisotropic model clearly shows some features that are consistent with the mapped geology. As expected, the results showed that a given data set can be fit by a range of models, with an inherent trade‐off from 3‐D heterogeneity to 3‐D anisotropy. This uncertainty can be reduced with the use of prior information in the inversion.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".