A multiple transmitter and receiver electromagnetic system for improved target detection
Bibliographic record
Abstract
ABSTRACT We have developed an alternative strategy for the inevitable deeper inductive electromagnetic (EM) exploration, which will be required as shallow deposits are exhausted. Rather than using very large magnetic moment ground loops, measurement stations are repeated using many smaller sized loops with smaller moments. The multiple transmitter data are then weighted and summed into a single high signal-to-noise ratio (S/N) composite transmitter. The composite transmitter can be thought of as a postprocessing method that uses the collected multitransmitter data to construct/simulate a transmitter, which maximizes the coupling to a particular target. The appropriate transmitter weights to use will depend on the target location and geometry, and, as such, different weighting schemes allow for the construction of different composite transmitters, each of which will maximally highlight different targets. We have assumed no prior knowledge of the location and orientation of the exploration targets, and we constructed composite transmitters for each possible location of a discretized subsurface and 324 possible target orientations (dipole embedded within a fully resistive medium). A modified difference of squares and a dipole look-up table was used to assess the fit between each composite transmitter and the suggested target location and orientation. Synthetic studies using conductive plate target(s) embedded within a fully resistive medium found that the target locations and orientations could be accurately determined and that the S/N of the composite transmitter was significantly higher than that of standard fixed-loop ground and airborne surveys. In a ground time-domain EM field test, 23 transmitter positions were used, and a shallow target (conductive dike) could be identified using the developed methodology. The composite transmitter data we produced was considerably easier to interpret and had a larger amplitude than that of any one single transmitter.
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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".