Modelling and straight‐ray tomographic imaging studies of cross‐hole radio‐frequency electromagnetic data for mineral exploration
Bibliographic record
Abstract
ABSTRACT Radio‐frequency electromagnetic tomography (or radio imaging method) employs radio‐frequency (typically 0.1–10 MHz) electromagnetic wave propagation to delineate the distribution of electric properties between two boreholes. Currently, the straight‐ray imaging method is the primary imaging method for the radio imaging method data acquired for mineral exploration. We carried out synthetic studies using three‐dimensional finite‐element modelling implemented in COMSOL Multiphysics to study the electromagnetic field characteristics and to assess the capability of the straight‐ray imaging method using amplitude and phase data separately. We studied four sets of experiments with models of interest in the mining setting. In the first two experiments, we studied models with perfect conductors in homogeneous backgrounds, which show that the characteristics of the electromagnetic fields depend mainly on the wavelength. When the borehole separations are less than one wavelength, induction effects occur; conductors with simple geometries can be recovered acceptably with amplitude data but are incorrectly imaged on the phase tomogram. When the borehole separations are longer than two wavelengths, radiation effects play a major role. In this case, phase tomography provides images with acceptable quality, whereas amplitude tomography does not provide satisfactory results. The third experiment shows that imaging with both original and reciprocal datasets is somewhat helpful in improving the imaging quality by reducing the impact of noise. In the last experiment, we studied models with conductive zones extended into the borehole plane with different lengths, which were not accurately recovered with amplitude tomography. The experiment implies that it is difficult to determine the extent of a mineralised zone that has been intersected by one of the boreholes. Due to the large variation of the wavelength in the radio‐frequency range, we suggest investigating the local electric properties to select an operating frequency prior to a survey. We conclude that straight‐ray tomography with either amplitude or phase data cannot provide high‐quality imaging results. We suggest using more general methods based on full electromagnetic modelling to interpret the data. In circumstances when computational time is critical, we suggest saving time by using either induction methods for borehole separations less than one wavelength or wave‐based methods (only radiation fields are considered) for borehole separation larger than two wavelengths.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".