Elastic wavefield separation of DAS-VSP data based on nonstationary polarization projection
Bibliographic record
Abstract
ABSTRACT Distributed acoustic sensing (DAS) generally records the seismic signals by detecting the axial strain or strain rate that is stimulated by the impinging elastic wavefields along the optical fibers. It has become an important seismic observation technology, especially in vertical seismic profiling (VSP) applications, due to its low cost, easy deployment, and high-density spatial sampling. Although current DAS-VSP acquisitions typically offer only a single-component observation, they still provide valuable elastic information about the subsurface. Separating P and S waves from the DAS-VSP data and leveraging this wavefield information is very important for the inversion of elastic parameters and seismic imaging. Therefore, a polarization projection method is introduced to deal with the P/S separation of walkaway DAS-VSP data. First, the polarization directions of P and S waves are estimated using the dispersion relation derived from elastodynamic wave equations. Then, the P/S-wave separation is achieved through a nonstationary polarization projection that can take into account the effects of spatially varying wave velocities. Finally, a two-step workflow is developed to successively separate the P and S waves in common-shot and common-receiver gathers. The results of synthetic and real walkaway DAS-VSP data demonstrate that this method can effectively separate P- and S-wave signals from DAS-VSP data, and provide effective data preconditioning for subsequent velocity model building and seismic imaging.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".