Synthetic Study for Optimizing an Efficient Grid Size for MT and Long Period MT Measurements
Bibliographic record
Abstract
While the Ultra Wide-Band Magnetotelluric technology (10,000Hz - 50,000 seconds) covers AMT, MT and LP bends in simultaneously measured time series, many research and exploration projects use dedicated MT (300Hz to 10,000 seconds) or LP (10 seconds - 50,000 seconds) systems. This is usually dictated by available equipment or a traditional approach to deep target studies. Surface anomalies (up to 2-5km) formed by conductive mineral bodies or fault systems considered to be less important and are often completely ignored during deep lithospheric studies. The upper layers conductivity is being estimated and averaged over the whole survey area. As it is very well known, the Magnetotelluric sounding signal measured on the surface represents an apparent resistivity at a depth dependent on frequency and conductivity of averaged ground thickness above. This assumption works generally well in smoothly layered geology, but might integrate an error in estimations and inversions in more complicated situations. In our previous studies we observed an effect of an upward shift of anomalies obtained after inversion of smaller grid size data for long period measurements. This seems to happen when the localized 3D conductive bodies are becoming dominant over the average layer conductivity and it cannot be assumed as a homogeneous thickness. In this study, our intention is analysing the efficient grid size that would be effective for MT band and LP band surveys. To achieve our estimated results, we designed a geoelectrical model that would be typical for Canadian shields with different grid sizes for MT band signal and LP band signals. We did run inversions using ModEM and observed vertical fit of inversion results to an original model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".