Data-driven modeling of time-domain induced polarization
Bibliographic record
Abstract
ABSTRACT Induced polarization (IP) measurements are affected by various types of noise, which should be removed prior to data interpretation. However, existing data processing methods often rely on empirical assumptions about the standard shape of IP decay curves. Our goal is to introduce a data-driven approach for modeling and processing time-domain IP measurements. To reach this goal, we train a variational autoencoder (VAE) on 1,600,319 IP decays collected in Canada, the United States, and Kazakhstan. The proposed deep learning approach is unsupervised and avoids the pitfalls of IP parameterization with empirical Cole-Cole and Debye decomposition models, simple power-law models, or mechanistic models. Four applications of VAEs are key to modeling and processing IP data: (1) synthetic data generation, (2) Bayesian denoising, (3) evaluation of signal-to-noise ratio, and (4) outlier detection. Furthermore, we interpret the IP data compilation’s latent representation and reveal a correlation between its first dimension and the average chargeability. Finally, we determine that a single real-valued scalar parameter contains sufficient information to encode IP data. This new finding suggests that modeling time-domain IP data using mathematical models governed by more than one free parameter is ambiguous, whereas modeling only the average chargeability is justified. A pretrained implementation of the VAE model is available as open-source Python code.
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 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".