Inversions of time-domain spectral induced polarization data using stretched exponential
Bibliographic record
Abstract
SUMMARY We provide a two-stage approach to extract spectral induced polarization (SIP) information from time-domain IP data. In the first stage we invert dc data to recover the background conductivity. In the second, we solve a linear inverse problem and invert all time channels simultaneously to recover the IP parameters. The IP decay curves are represented by a stretched exponential (SE) rather than the traditional Cole–Cole model, and we find that defining the parameters in terms of their logarithmic values is advantageous. To demonstrate the capability of our simultaneous SIP inversion we use synthetic data simulating a porphyry mineral deposit. The challenge is to image a mineral body that is hosted within an alteration halo having the same chargeability but a different time constant. For a 2-D problem, we were able to distinguish the body using our simultaneous inversion but we were not successful in using a sequential (or conventional) SIP inversion approach. For the 3-D problem we recovered 3-D distributions of the SIP parameters and used those to construct a 3-D rock model having four rock units. Three chargeable units were distinguished. The compact mineralization zone, having a large time constant, was distinguished from the circular alteration halo that had a small time constant. Finally, to promote the use of the SIP technique, and to have further development of SIP inversion, all examples presented in this paper are available in our open source resources (https://github.com/simpeg-research/kang-2018-spectral-inducedpolarization).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".