A procedure for collecting electromagnetic data using multiple transmitters and receivers capable of deep and focused exploration
Bibliographic record
Abstract
ABSTRACT Many ground controlled-source electromagnetic (EM) systems have been deployed, and under ideal conditions these systems are capable of detecting large conductors to depths of approximately 800 m; however, more common detection limits are less than 400 m. Although these systems have been used with great success, they may experience two weaknesses when exploring for deeper conductors: poor coupling with the target and small signal-to-noise ratios (S/Ns), both of which decrease the quality and interpretability of the data. We evaluated a novel time-domain EM procedure that addresses these weaknesses. The coupling weakness was addressed through multiple transmitter locations and multiple receiver locations, and the S/N was increased by spatial stacking of measurements (from the various transmitter-receiver combinations). A field test of this procedure was undertaken. Reciprocity data indicated that the noise levels of the vertical component data we acquired were about −0.004 μV/Am2. Spatial stacking of the data can reduce the noise levels by a factor of seven. This means that a small conductor previously only visible to 150 m could be seen to 275 m and a conductor visible to 300 m could be seen to 575 m. One challenge of the new procedure was the time required to collect all the transmitter-receiver combinations — this time can be reduced using the principle of reciprocity and not repeating approximately reciprocal measurements. Another challenge was to visualize and interpret the large volumes of data collected using the procedure — this has been partially addressed by creating equivalent-dipole depth sections. Synthetic and real equivalent-dipole depth sections appeared very similar and illustrated that these images of the subsurface could be interpreted. However, the features appeared too deep on the sections, so better visualization techniques could be developed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".