Multiazimuth Elastic Full‐Waveform Inversion of Fiber‐Optic and Accelerometer Vertical Seismic Profile Data
Bibliographic record
Abstract
Distributed acoustic sensing (DAS) is a rapidly developing technology enabling the recording of seismic data using fiber-optic cables. Intensive efforts have been devoted to optimizing the application of seismic processing, imaging, and inversion methods to DAS data. We examine the response of an elastic full-waveform inversion (FWI) approach, combining DAS and accelerometer vertical seismic profile (VSP) data. The problem is formulated by combining strain and displacement components in one objective function. Accelerometer data are proportional to particle acceleration, whereas DAS data are proportional to strain rate/strain along the fiber axis; thus, both datasets require conversion to displacement and strain. To prepare the DAS VSP field data for inversion, we develop a depth registration method based on cross-correlation scanning and use first-break picking as quality control to obtain a robust DAS depth for each trace. An effective source scheme is incorporated into the VSP FWI to address complex near-surface wave propagation. Application of the FWI approach to 2D two-azimuth walkaway VSP datasets acquired at Newell County, Alberta, reveals horizontal layering consistent with the site's known geology and limited azimuthal variations. Reverse-time migration imaging further corroborates the inversion results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".