Conductivity-depth imaging of helicopter-borne TEM data based on a pseudolayer half-space model
Bibliographic record
Abstract
Abstract Helicopter-borne time-domain electromagnetic (HTEM) systems with a concentric horizontal coil configuration have been used increasingly in mineral exploration. Conductivity-depth imaging (CDI) is a useful tool for mapping the distribution of geologic conductivity and for identifying conductive targets. A CDI algorithm for HTEM systems with a concentric coil configuration is developed based on the pseudolayer half-space model. Primary advantages of this model are immunity to altimeter errors and better resolution of conductive layers than other half-space models. Effective depth is derived empirically from the diffusion depth and apparent thickness of the pseudolayer. A table lookup procedure is established based on the analytic solution of a half-space model to speed up processing. This efficiency makes generation of real-time conductivity-depth images possible. Tests on synthetic data demonstrate that the pseudolayer conductivity-depth-imaging algorithm maps a wider range of conduc-tivities and does a better job of resolving highly conductive layers, compared with that of the homogeneous half-space model. Effective depths are close to true depths in many circumstances. Field examples show stable and geologically meaningful conductivity-depth images.
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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 | 0.000 | 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) | 0.001 | 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 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".