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Record W4416177032 · doi:10.1190/tle44110838.1

Design, construction, and initial testing of a novel three-component electromagnetic transmitter system for deep mineral exploration

2025· article· en· W4416177032 on OpenAlexaff
Anthony Zamperoni, Richard S. Smith, Michael Finlayson

Bibliographic record

VenueThe Leading Edge · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsLaurentian University
Fundersnot available
KeywordsTransmitterConductorCoupling (piping)Electrical conductorInductive couplingElectromagnetic coilMineral exploration

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.052
GPT teacher head0.261
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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