Enhancing Uncertainty Quantification Performance via Deep Learning-Assisted Markov Chain Monte Carlo
Bibliographic record
Abstract
Summary Seismic Full Waveform Inversion (FWI) is a powerful tool for subsurface estimation, but its results are inherently non-unique. By quantifying the uncertainty in FWI results, this non-uniqueness can be better characterized, leading to more reliable and insightful interpretations of the subsurface. Bayesian inference, applied through the Metropolis-Hastings MCMC algorithm, addresses this need but is computationally intensive, especially for complex forward simulations. This study proposes a deep learning-assisted workflow to improve MCMC efficiency, replacing the traditional physics-based solver (SPECFEM2D) with a Convolutional Neural Network (CNN) for data misfit calculations. Results show the CNN approach offers a 4-fold speed increase while maintaining accuracy in posterior distribution estimation, underscoring its potential as a fast, reliable alternative for complex seismic inversion tasks.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".