Time-Domain Electromagnetic Data Interpretation using Moving-Loop Configurations for Sheet-Like Base Metal Ore Deposits in Resistive Hosts
Bibliographic record
Abstract
A simple, useful, and practical tool is proposed for the interpretation of moving-loop time-domain electromagnetic (TEM) surveys over two-dimensional (2D) sheet-like conductors embedded in a resistive host. It is based on the relationship observed between attributes of the displayed responses and the geometrical and electrical parameters of the conductive ore body. The ore body is modelled as a plate conductor for which the depth, dip, and conductance parameters are estimated. Numerical and scale modelling are used to establish the interpretative expressions. Responses computed for the various plate parameters are classed according to the following response attributes: time constant, asymmetry, and peak-to-peak distance. Three expressions relating depth, dip, and conductance to the response attributes are determined using multiple linear regression. The relationships are validated using scale-modelling data. The method allows the determination of the plate depth, conductance, and dip with an accuracy of ±10%, ±10% and ±5° respectively. The method is tested on SIROTEM survey data from Chutes-des-Passes in Quebec (Canada), where drill hole information is available. The results show that the regression relationships provide accurate estimates of the basic characteristics of the deposits.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".