Using combinations of spatial gradients to improve the detectability of buried conductors below or within conductive material
Bibliographic record
Abstract
ABSTRACT The detection of conductive bodies is an important capability when exploring for massive sulfide deposits or looking for unexploded ordnance. When these bodies are buried below conductive overburden or embedded in conductive material, the use of an electromagnetic system to identify the bodies becomes problematic because the response of the overlying conductive material can be much greater that the response of the buried conductor. I calculated the response of five models representing different conductivity distributions (a buried conductor, a uniform overburden with changes in the system altitude, a paleochannel, a thicker overburden, and a thinner overburden). The subtle response of the buried conductor was difficult to identify because it looked very similar to the responses of other structures that are not necessarily of interest. The spatial gradients for the same five models showed that the greatest improvement in the relative size of the anomalous gradient response compared with the background gradient came for the cases in which the material closest to the surface changes, in particular the paleochannel and thickening overburden models. However, identification of the deeper buried conductor was still problematic because of the large background gradients. In theory, the cylindrical symmetry of a dipole transmitter over a layered earth ensured that there were exact relations between the spatial derivatives. Hence it was possible to define two specific combinations that should be zero over a layered earth. Calculating these combinations for the five models showed that the anomalous zones stood out with significantly greater anomaly-to-background ratios. The measurement of the gradients and the calculation of these combinations therefore provided a means of identifying anomalous zones in and below a conductive earth. Different relative sizes and shapes of the two combinations for different models provided a way of discriminating between the vertical conductor model and the four other models.
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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 | 0.000 | 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".