Design, construction, and initial testing of a novel three-component electromagnetic transmitter system for deep mineral exploration
Bibliographic record
Abstract
Abstract Electromagnetic (EM) methods are used in mineral exploration to detect conductive ore bodies. The ability of an EM system to detect geological targets at depth is partly dependent on the coupling the interaction between the transmitter’s primary magnetic field and the target conductor which is highly dependent on the target’s orientation. A novel three-component transmitter (3CTx) EM system was developed for ultimate implementation in airborne, semi-airborne, or ground systems. Its additional transmitter coils and multiple transmitter locations could provide a greater signal-to-noise ratio for deep geological bodies, while the co-orthogonal coils and resultant multiplicity of primary-field directions increased coupling with a wide range of target orientations. A prototype 3CTx system was designed, constructed, and field-tested. The primary objective was to validate the fundamental concept: that signals from the three transmitters could be acquired simultaneously by a receiver and then successfully separated during processing. Initial results from three field tests demonstrated that the individual transmitter signals could be separated with minimal cross-coupling, producing data comparable to that from a conventional single-component ground-loop system. This success validated the methodology, a crucial first step toward developing a full-scale system for deep exploration.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".