A discussion of 2D induced polarization effects in airborne electromagnetic and inversion with a robust 1D laterally constrained inversion scheme
Bibliographic record
Abstract
ABSTRACT Recently, the interest in the induced polarization (IP) phenomenon in airborne time-domain electromagnetic (ATEM) data has increased considerably. IP may affect the ATEM data significantly and mask underlying geologic structures. To simulate 2D airborne IP data, a 2D finite-element forward-modeling algorithm has been developed with the dispersive conductivity described by the well-known Cole-Cole model. We verify our algorithm by comparison with the 1D solution of the AarhusInv code. Two-dimensional forward responses on six synthetic models, mimicking archetypal 2D conductive and chargeable anomalies, have been generated, and the results indicate that 2D IP affects the data significantly. Differences between the 2D IP responses and the 1D IP responses are evident above the 2D anomalies and at their edges. These differences are similar to what is found when comparing 2D and 1D forward responses over conductive 2D anomalies without considering IP. We evaluate an effective robust inversion scheme to recover the 2D IP parameters using the 1D laterally constrained inversion (LCI) scheme. The inversion of the synthetic data using the robust scheme indicates that not only can the IP parameters be recovered, but also the IP inversions can provide more accurate resistivity sections than a resistivity-only inversion, in terms of resistivity values and anomaly thickness/depth. The field example from Hope Bay area in Canada is even more valuable, considering that part of the profile consists of only negative data, which cannot be inverted with a resistivity-only scheme. Furthermore, the edge effects at the anomaly boundaries are less pronounced in the IP parameters than in the resistivity parameter on the synthetic models with more conductive backgrounds.
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 | 0.001 |
| 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".