Combining spatial components and Hilbert transforms to interpret ground-time-domain electromagnetic data
Bibliographic record
Abstract
ABSTRACT We have developed a method for displaying or imaging data from a ground-time-domain electromagnetic system and for extracting the geometric parameters of a small conductor. The parameters are determined directly from the data using combinations of the spatial components of the secondary fields and their Hilbert transforms. The position of the target coincides with the peaks of the energy envelope (EE) or the T-component of the response. Here, the EE is the square root of the sum of the squares of the three spatial components and their Hilbert transforms, whereas the T-component response is an analogous quantity that excludes the Hilbert transform terms. Studies on synthetic models indicate that the T-component response is sharper than the EE in most possible target orientations. Once the position of a body has been determined using the peak of the T-component response, the dip of the target can be quantified using the ratio of the full-width at half-magnitude (FWHM) of the T-component response and the T-component Hilbert transform response, which is analogous to the EE but excludes the untransformed quantities. Finally, once all other geometric parameters have been determined, the depth of the target can be evaluated using the FWHM of the T-component response. The proposed modeling method was tested over an anomaly acquired at the Coulon field site during an InfiniTEM survey in the Abitibi greenstone belt of Quebec. The extracted geometric parameters were consistent with the available geologic information.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".