Frequency- and spatial-correlated noise on layered magnetotelluric inversion
Bibliographic record
Abstract
Correlated data errors (noise) are common in magnetotelluric (MT) data, but MT inversions typically neglect error correlations without investigating the impact of this simplification on inversion results. This paper examines effects of neglecting frequency- and spatially correlated noise on MT inversion, based on a nonlinear Bayesian formulation which quantifies the uncertainties of inversion results in terms of marginal posterior probability densities and credibility intervals. To do so, data with frequency- and spatially correlated noise of differing degrees are generated for several layered (1-D) synthetic cases. Bayesian MT inversions are carried out for these data sets with and without accounting for error correlation (i.e. applying full and diagonal covariance matrices, respectively, in the inversion), and the results are compared. For cases with noise that is strongly correlated over frequency or space, parameter uncertainties estimated using the diagonal-covariance simplification (neglecting error correlations) are found to often be significantly underestimated compared with results computed using the full covariance matrix.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".