Log-validated waveform inversion of reflection data with wavelet phase and amplitude updating
Bibliographic record
Abstract
Waveform inversion is difficult to apply to near-offset surface seismic data, with issues such as the absence of large lateral wavenumbers and diving waves tending to leave iterative methods exposed to local minima. Well-logs can mitigate this issue to some degree, either by supporting the construction of reliable starting models, or, as we show in this paper, by permitting a model-validation step to be included within each iteration. The extra stability conferred by model validation allows one-way wave operators to be used in place of reverse time migration operators in the construction of the update. The approach leads to remarkably accurate results and significant computational savings. Once a log-validated waveform procedure is set up along these lines, we address the unknown-wavelet issue. A process of updating of both the amplitude and phase of an initial wavelet estimate is set up to produce stable inversions in both synthetic and field environments. In the latter case, profiles extracted from a log-validated waveform inversion of the Hussar land data set, when compared to two blind validation wells, are observed to accurately reproduce structures in zones producing significant reflection energy. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 9:20 AM Presentation Time: 10:10 AM Location: Poster Station 8 Presentation Type: Poster
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".