Electromagnetic imaging of a complex ore body: 3D forward modeling, sensitivity tests, and down-mine measurements
Bibliographic record
Abstract
Abstract This study investigated the capability of audio-magnetotellurics (AMT) not only to detect, but also to delineate complex conductive ore bodies at minable depths. A detailed 3D numerical-electrical resistivity model of the Bathurst no. 12 deposit (New Brunswick, Canada) was constructed using available geologic and geophysical information. Different geologic and data acquisition conditions were simulated: presence of overburden; different geometries, dimensions, and positions of the ore body; and different data sampling regimes. The behavior of the surface 3D electromagnetic fields was compared with that from bodies of infinite strike extent. The 3D and 2D AMT responses were similar at high frequencies, so 2D modeling was shown to be both valid and sufficient. However, at low frequencies only those responses for current flow perpendicular to the body (the transverse magnetic mode in a 2D case), were reasonably similar. The 2Dinversions showed that the position and the top of the 3D ore body were well resolved, but the bottom of the ore body's resis-tivity were poorly resolved. To increase resolution at depth below ore bodies, and to possibly extend mine life, we recommend that AMT measurements are taken from within mines. Simple mod-els of ore bodies were simulated to show responses at depth, and to undertake body stripping of the overlying structures. The tests showed that conductive structures above the measurement level can have a strong influence on imaging of conducting zones below, and can produce distortion effects in apparent resistivity and phase. Although the body-stripping approach reduces these effects and gives an indication of whether there are conductive structures below, the resulting image is considerably different from that of a model without overlying conductive structures. Full 3D inversion, holding known structures at constant, is required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".