Electric and magnetic galvanic distortion decomposition of tensor CSAMT data. Application to data from the Buchans Mine (Newfoundland, Canada)
Bibliographic record
Abstract
We have developed a Marquardt–Levenberg inversion algorithm incorporating the effects of near-surface galvanic distortion into the electromagnetic (EM) response of a layered earth model. Different tests on synthetic model responses suggest that for the grounded source method, the magnetic distortion does not vanish for low frequencies. Including this effect is important, although to date it has been neglected. We have inverted 10 stations of controlled-source audio-magnetotellurics (CSAMT) data recorded near the Buchans Mine, Newfoundland, Canada. The Buchans Mine was one of the richest massive sulphide deposits in the world, and is situated in a highly resistive volcanogenic environment, substantially modified by thrust faulting. Preliminary work in the area demonstrated that the EM fields observed at adjacent stations show large differences due to the existence of mineralized fracture zones and variable overburden thickness. Our inversion results suggest a three-layered model that is appropriate for the Buchans Mine. The resistivity model correlates with the seismic reflection interpretation that documents the existence of two thrust packages. The distortion parameters obtained from the inversion concur with the synthetic studies that galvanic magnetic distortion is required to interpret the Buchans data since the magnetic component of the galvanic distortion does not vanish at low frequency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".