Three-dimensional modeling of IP effects in time-domain electromagnetic data
Bibliographic record
Abstract
ABSTRACT Understanding the effects of induced-polarization (IP) effects on time-domain electromagnetic data requires the ability to simulate common survey techniques when taking chargeability into account. Most existing techniques preform this modeling in the frequency domain prior to transforming their results to the time domain. Even though this technique can allow for chargeable material to be easily incorporated, its application for some problems can be computationally limiting. We developed a new technique for forward modeling the time-domain electromagnetic response of chargeable materials in three dimensions. The frequency dependence of Ohms’ law translates to an ordinary differential equation when considered in the time domain. The system of ordinary-partial differential equations was then discretized using an implicit time-stepping algorithm, that yielded absolute stability. This approach allowed us to operate directly in the time domain and avoid frequency to time-domain transformations. Although this approach can be applied directly to materials exhibiting Debye dispersions, other Cole-Cole dispersions resulted in fractional derivatives in time. To overcome this difficulty, Padé approximations were used to represent the frequency dependence as a rational series of integer order terms. The resulting method was then simplified to generate a reduced time-domain model that can be used to forward model the IP decay curves in the absence of any electromagnetic coupling. We found numerical examples in which the method produced accurate results. The potential application of the method was demonstrated by modeling the full time-domain electromagnetic response of a gradient array IP survey, and the occurrence of negative transients in airborne time-domain electromagnetic data.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".