Multiazimuth Elastic Full‐Waveform Inversion of Fiber‐Optic and Accelerometer Vertical Seismic Profile Data
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it