EM-coupling removal from time-domain IP data
Bibliographic record
Abstract
Electromagnetic (EM) coupling of frequency-domain induced polarisation (IP) data has been the subject of many studies, and a number of ‘de-coupling’ procedures have been devised. However, there has been far less emphasis on coupling in the time domain, the normal approaches being to wait until late times and assume the EM contribution is insignificant or, less frequently, to invoke a Cole-Cole model to account for the EM-coupling response. A fast and simple procedure has been devised for suppression of EM-coupling effects in time-domain IP data. The essence of the approach is to represent the EM-coupling as a half-space decay. The half-space resistivity (EM apparent resistivity) is adjusted via inversion until the fit to the observed transient voltage decay is optimal in the least squares sense. The EM voltages associated with this best-fitting EM half-space decay are then subtracted from the measured voltages to yield a de-coupled ‘IP transient’. Transients that are well represented by an EM half-space decay are deemed ‘non-responsive’ in the context of IP. Transients that deviate markedly from an EM half-space decay are indicative of high apparent chargeability. The application of the new procedure is illustrated on dipole-dipole IP data from the Yandal greenstone belt of Western Australia.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".