Electromagnetic coupling in frequency-domain induced polarization data: a method for removal
Bibliographic record
Abstract
Electromagnetic (EM) coupling is generally considered to be noise in induced polarization (IP) data and interpretation is difficult when its contribution is large compared to the IP signal. The effect is exacerbated by conductive environments and large‐array survey geometries designed to explore deeper targets. In this paper we present a methodology to remove EM coupling from frequency‐domain IP data. We first investigate the effect of EM coupling on the IP data and derive the necessary equations to represent the IP effect for both amplitude and phase responses of the signal. The separation of the inductive response from the total response in the low‐frequency regime is derived using the electric field due to a horizontal electric dipole and it is assumed that at low frequencies the interaction of EM effects and IP effects is negligible. The total electric field is then expressed as a product of a scalar function, which is due to IP effects, and an electric field, which depends on the EM coupling response. It is this representation that enables us to obtain the IP response from EM‐coupling‐contaminated data. To compute the EM coupling response we recognize that conductivity information is necessary. We illustrate this with a synthetic example. The removal method developed in this work for the phase and the per cent frequency effect (PFE) data are applicable to 1‐D, 2‐D and 3‐D structures. The practical utility of the method is illustrated on a 2‐D field example that is typical of mineral exploration problems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".