Detecting induced polarisation effects in time-domain data: a modelling study using stretched exponentials
Bibliographic record
Abstract
The potential for extracting and interpreting induced polarisation (IP) data from airborne surveys is now broadly recognised. There is, however, still considerable discussion about the conditions under which the technique can provide knowledge about the subsurface and thus, its practical applications. Foremost among these is whether, or under what conditions, airborne IP can detect chargeable bodies at depth. To investigate, we focus on data obtained from a coincident-loop time-domain system. Our analysis is expedited by using a stretched exponential rather than a Cole-Cole model to represent the IP phenomenon. Our paper begins with an example that illuminates the physical understanding about how negative transients (the typical signature of an IP signal in airborne data) can be generated. The effects of the background conductivity are investigated; this study shows that a moderately conductive and chargeable target in a resistive host is an ideal scenario for generating strong IP signals. We then examine the important topic of estimating the maximum depth of the chargeable target that can generate negative transients. Lastly, some common chargeable earth-materials are discussed and their typical IP time-domain features are analysed. The results presented in this paper can be reproduced and further explored by accessing the provided Jupyter notebooks.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".