Can Normalizing Flows make Uncertainty Quantification Practical for Time-Lapse Seismic Monitoring
Bibliographic record
Abstract
Due to the nonlinearity of inversion as well as the noise in the data, seismic inversion results certainly have uncertainties. Whether quantifying these uncertainties is useful depends at least in part on the computational cost of computing them.  Bayesian techniques dominate uncertainty quantification for seismic inversion.  The goal of these methods is to estimate the probability distribution of the model parameters given the observed data. The Markov Chain Monte Carlo algorithm is widely employed for approximating the posterior distribution. However, generating the posterior samples by combining the prior and the likelihood is intractable for large problems and challenging for smaller problems. We apply a machine learning method called normalizing flows, which consists of a series of invertible and differentiable transformations, as an alternative to the sampling-based methods. In our work, the normalizing flows method is combined with full waveform inversion(FWI) using a numerically exact local solver to quantify the uncertainty of time-lapse changes. We integrate uncertainty quantification(UQ) and FWI by estimating UQ on the images generated by FWI making it computationally practical. In this way, a reasonable posterior probability distribution is directly predicted and produced by transforming from a normal distribution, measuring the amount and spread of variation in FWI images by sample mean and standard deviation. In our numerical results, the method for calculating the posterior distribution of the model is verified to be practical and advantageous in terms of effectiveness.
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 | 0.001 | 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 | 0.001 |
| 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".