FWI Imaging: Full-wavefield imaging through full-waveform inversion
Bibliographic record
Abstract
Full wavefield data that includes transmitted waves, primary reflections, and their multiples, has been widely used for velocity model building through full-waveform inversion (FWI). However, migrating full wavefield data to image the subsurface remains very challenging. Higher order scattering energy potentially helps infill illumination holes of primary reflections, but it also introduces crosstalk noise into the migration image when imaging algorithms cannot properly handle this higher order scattering energy. With the advancement of high-performance computing and progress in FWI algorithms to tackle problems such as cycle-skipping and amplitude mismatch, FWI has found success using different data types in a variety of geologic settings. Here we take a step further to modify the FWI workflow to output the subsurface image or reflectivity directly, eliminating the need to go through the time-consuming seismic imaging process that involves preprocessing, velocity model building, and migration. Compared with a conventional reverse-time migration image, the reflectivity image directly output from FWI provides additional structural information with more balanced illumination, since FWI by nature is a least-squares fitting process of the full wavefield data. This paper was accepted into the Technical Program but was not presented at the 2020 SEG Annual Meeting.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.035 |
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".