Removal of galvanic distortion effects in 3D magnetotelluric data by an equivalent source technique
Bibliographic record
Abstract
ABSTRACT The galvanic distortion induced by the electric charge buildup across near-surface inhomogeneities can severely affect the interpretation of magnetotelluric (MT) data for deeper structures. In addition to the methods already available, we have developed an alternative approach for processing MT impedance data with such distortions using an equivalent source technique. One prerequisite for the method is that all data are acquired on the surface, which is nearly always the case in land-based MT surveys. The method works with the electric field scaled from the impedance data and constructs an equivalent electrical polarization layer that attempts to reproduce the signal in the data while misfitting the galvanic distortion. Because of the uncorrelated characteristics of galvanic distortions across multiple stations at the same frequency, they can be distinguished and removed by constructing an equivalent source layer of electrical polarization using a regularized inverse formulation. The tradeoff between the signal and distortion is achieved through the use of generalized cross-validation method during the equivalent source construction, whereas the choice of equivalent source parameters also affects the separation. Numerical tests indicate that good results are obtained when the depth of the equivalent source layer is slightly greater than 10 times the nominal data spacing, and the lateral extent is twice that of the data area. The simultaneous processing with multiple frequencies yields more stable apparent resistivity curves than the separate single-frequency processing. The method has performed well in removing the galvanic distortions in the synthetic- and field-data examples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".