Time-lapse full-waveform inversion using Hamiltonian Monte Carlo: A proof of concept
Bibliographic record
Abstract
Uncertainty quantification is an important aspect of time–lapse imaging and is typically done using Bayesian inference. Traditional random–walk sampling methods are slow to converge and they fail to efficiently explore the high dimensional space that must be characterized in time-lapse imaging. We propose the use of a local acoustic Helmholtz solver for an efficient time–lapse Hamiltonian Monte Carlo (HMC) inversion. Using a local acoustic solver offers the advantage of quick and local gradient computations. Our numerical models demonstrate the robustness of the method over the Metropolis–Hastings algorithm, and set up the path towards non–linear uncertainty quantification of high dimensional velocity models. To our knowledge this is the first direct comparison of Metropolis– Hastings and HMC on a seismic example. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 1:50 PM Presentation Time: 3:55 PM Location: Poster Station 3 Presentation Type: Poster
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".