Magnetotelluric static shift: bias and estimation using the cokriging method
Bibliographic record
Abstract
The magnetotelluric static shift is a distortion effect that affects only the apparent resistivity data but not the phase. Correction for that distortion is then critical to determine the “true” resistivities and depth of the model in the inversion process. We present in this paper our recent development on a geostatistical method to correct MT sites from static shift. The cokriging method uses the spatial data structures of both the measured apparent resistivities and phases at MT sites at a few selected frequencies, and their known intrinsic correlation, to compute an new estimate of the apparent resistivity. The cokriged value may represent a good estimate of the apparent resistivity that is not affected by static shift. An application of that method on a 3D MT synthetic example using a biased uniform distribution of the static shift is presented in this paper, and results demonstrate the potential of that method. In particular, analysis of different solutions demonstrate the importance to determine sites that are affected or not by static shift in order to completely remove the bias in the static shift. Finally, an application to the 2D MT dataset COPPROD 2S2 is presented.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".