ZTEM data inversion and interpretation using the UBC-GIF MTinv3D code: A case history at the Silver Queen project, British Columbia
Bibliographic record
Abstract
SummaryZ-axis Tipper Electromagnetic (ZTEM) surveys are rapidly becoming an integral part of geophysical exploration. This airborne AFMAG EM system measures the tipper of natural magnetotelluric fields at frequencies typically from 30Hz to 720Hz.The ZTEM system responds primarily to current channelling and operates at lower frequencies than active-source EM systems. As such, it maps bulk conductivity of the ground to lower values and greater depths than active-source airborne EM systems. ZTEM is particularly suited to mapping large regional structures, sulfide vein systems and intrusives that characterize porphyry copper deposits. The 3D resistivity model produced by inversion of ZTEM data using the UBC-GIF MT3Dinv code proves very useful for focussing exploration into the most prospective zones of a project area.The Silver Queen polymetallic vein system is a high grade past producer south of Houston, British Columbia. Current exploration around the old Silver Queen mine by New Nadina Explorations Ltd is conceptually based targeting of a blind, buried bulk tonnage deposit near the old mine and deeper in the mineralized system. Inversion of the ZTEM data and the magnetic data acquired over and surrounding the old mine, has identified a favourable setting close to an interpreted nearby intrusive body and within a large regional structure that flexes around it. Exploration is now focussed in this area, with a deeply penetrating induced polarization, electrical resistivity, and magnetotelluric ground survey completed over the target areas to direct drilling. The ZTEM processing and the inversion results from the ZTEM and magnetic data are presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".