Coupled simulation of electromagnetic induction and induced polarization effects using stretched exponential relaxation
Bibliographic record
Abstract
We have developed a new algorithm for 3D time-domain electromagnetic (EM) modeling, taking full account of induced polarization (IP) and the coupling between EM and IP effects. The algorithm can be used to model grounded source IP surveys that indicate EM induction effects and airborne time-domain EM surveys that exhibit IP effects. IP effects are most often approximated as static or modeled in the frequency domain, using frequency-dependent electrical conductivity. It is difficult to translate the frequency-dependent conductivity approach directly to the time domain in a computationally efficient manner. We take an alternative approach in which we model IP relaxations in time using the stretched exponential (SE) function. We incorporate this IP model into a direct time-stepping discretization of the quasistatic time-domain Maxwell equations. We found that modeling of IP effects with this SE approach is asymptotically equivalent to the commonly used Cole-Cole model of IP transformed to the time domain. We have implemented our algorithm using efficient numerical methods that allow it to tackle large-scale problems and are amenable to use in inversion. In particular, we have developed a parallel time-stepping technique that allows us to compute transient electric fields at multiple time steps simultaneously. We demonstrate the behavior of the SE model of IP decay and the efficiency of our algorithm by applying it to synthetic numerical examples that simulate a grounded source IP survey with significant EM effects and a concentric-loop airborne EM sounding over a chargeable body.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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".