3D joint inversion of magnetotelluric and magnetovariational data to image conductive anomalies in Southern Alberta, Canada
Bibliographic record
Abstract
Summary It is well known that magnetotelluric (MT) impedance can be distorted by near-surface inhomogeneities (NSI), which complicates the interpretation of MT data and the correct imaging of deep geoelectrical structures. This paper demonstrates that the inclusion of magnetovariational (MV) tipper data in a three-dimensional (3D) inversion jointly with MT impedance provides better resolution of deep conductive anomalies than stand-alone MT impedance. This is significant because MV data can be collected alongside the MT impedance data for virtually no additional cost. Electric and magnetic fields in forward modeling are determined using the integral equation (IE) method. The inverse problem is solved with the re-weighted regularized conjugate gradient (RRCG) method with limited sensitivity domain. We present the results of both a synthetic model and case study using EarthScope data gathered in Southern Alberta, Canada. In both cases, the joint inversion provides more accurate information about deep conductive anomalies than the inversion of impedance stand-alone data.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".