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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".