Full Waveform Inversion in the Western Canadian Basin: From Near Surface to Deep
Bibliographic record
Abstract
Summary Full waveform inversion (FWI) has become a standard component of velocity model building workflows in marine exploration. In contrast, challenges such as poorer data quality, the presence of elastic effects and surface topography, have precluded the same integration from occurring for land exploration. In this study, we present one of the first applications of FWI to a land dataset from the western Canadian basin. We detail an end-to-end workflow that begins with data preprocessing and initial model building. The Cynthia 2D dataset is characterized by a lack of quality low-frequency information (below 8 Hz) and maximum offsets of 6.4 km. These properties limit the interrogation depth of conventional diving wave FWI. To counteract this, we devise two independent schemes for acoustic FWI. The first employs diving waves to update the near-surface (0.75 km maximum depth) P-wave velocity structure. The second uses reflection data to update structure to a maximum depth of 3 km. Both schemes employ multi-scale strategies, phase-based objective functions and gradient preconditioning to mitigate non-linearities in the inversion process. Standard quality control measures support the validity of the inverted models. The study provides a reference for future applications of FWI in the region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".