The transient electromagnetic response of a resistive sheet: straightforward but not trivial
Bibliographic record
Abstract
Thin conductive sheets are often used to model base metal deposits for mineral exploration. In a similar manner, thin resistive sheets can be used as the building blocks of oil, gas or gas hydrate reservoir models. One would expect that the calculation of the electromagnetic response of a resistive sheet using an integral equation method should follow trivially from the well-studied solution methods for a conductive sheet. The only physical difference between the two situations is the fact that the conductive solution relies on the continuity of the electric field tangential to the sheet surface whereas the resistive solution requires continuity of the normal current density. The authors started to develop the building blocks for a practical tool which would be useful in marine controlled-source electromagnetic projects. However, progress proved slow. It was eventually realized that the style of the approximations used in the conductive calculation resulted in solutions in the resistive case that failed surprisingly to converge. It turns out that the resistive problem is far more subtle than one might expect, not only for the full 3-D case, but also for the simplified 2-D version. This paper derives and examines the quasi-analytical solution for the response of a 2-D resistive sheet buried in a double half-space and excited by a 2-D inline dipole—dipole system. Non-trivial differences between the resistive and conductive solution techniques are illuminated, and a deeper understanding of the underlying physics behind the resulting response is gained. Results show that a large resistive sheet of finite transverse impedance behaves in the same way as a layer with corresponding thickness and resistivity, a well-known result in the resistivity method. The response of a finite sheet shows characteristics similar to those observed in full numerical modelling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".