PyFWI: A Python package for full-waveform inversion and reservoir monitoring
Bibliographic record
Abstract
Full-waveform inversion (FWI) of seismic data is a technique that can be used to image the subsurface as well as to monitor time-lapse changes in the subsurface (TL-FWI). PyFWI is a package that has been designed to carry out FWI and TL-FWI efficiently on GPU for research purposes. Several time-lapse strategies are implemented in PyFWI, such as parallel, double-difference, cascaded, central-difference, cross-updating, simultaneous, and weighted-average. An important challenge of TL-FWI is the crosstalk between parameters across different vintages. To alleviate this problem, PyFWI allows using different parameterizations. PyFWI is written in Python and relies on OpenCL for enabling calculations on GPUs, which leads to significant reduction of computation time compared to CPU implementation. Using OpenCL makes PyFWI portable across systems built with GPUs from different manufacturers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.022 |
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".