Multi-EM systems inversion - Towards a common conductivity model for the Tli Kwi Cho complex
Bibliographic record
Abstract
Summary The magnetic and electromagnetic responses from airborne systems at Tli Kwi Cho, a kimberlite complex in the Northwest Territories, Canada, have received considerable attention over the last two decades but a complete understanding of the causative physical properties is not yet at hand. Our analysis is distributed among three posters. In the first we find a 3D magnetic susceptibility model for the area; in the second we find a 3D conductivity model; and in the third we find a 3D chargeability model that can explain the negative transient responses measured over the kimberlite pipes. In this second paper we focus upon the task of finding a conductivity model that is compatible with three airborne data sets flown between 1992 and 2004: one frequency-domain data set (DIGHEM) and two time-domain systems (AeroTEM and VTEM). The goal is to obtain a 3D model from which geologic questions can be answered, but even more importantly, to provide a background conductivity needed to complete the 3D IP inversion of airborne EM data. We begin by modifying our pre-existing 1D frequency and time domain inversion codes to produce models that have more lateral continuity. The results are useful in their own right but we have also found that 1D analysis is often very effective in bringing to light erroneous data, assisting in estimating noise floors, and providing some starting information for developing a background model for the 3D EM inversion. Here we show some results from our Laterally Constrained Inversion (LCI) framework. The recovered conductivity models seem to agree on the general location of the kimberlite pipes but disagree on the geometry and conductivity values at depth. The complete 3D inversions in time and frequency, needed to resolved these issues, are currently in progress.
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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) | 0.000 | 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 | 0.000 |
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".