Transdimensional multimode surface-wave dispersion inversion of seismic data recorded on trench-deployed distributed acoustic sensing fiber
Bibliographic record
Abstract
Surface-deployed distributed acoustic sensing (DAS) fiber has the potential to provide high quality fiberoptic seismic data for estimating near-surface velocity profiles using surface-wave dispersion (SWD) inversion methods. Because of the challenges of robust, multimode curve picking, most SWD inversion approaches only utilize the fundamental mode. However, putting this generally leads to damaging trade-offs in the resulting inferred models. We observe that surface DAS data, with its wide frequency range, makes multimodal dispersion curve picking much more straightforward, opening up as a practical possibility of using these higher modes to enhance resolution in shallow and deeper regions of the near surface simultaneously. In this study, a SWD approach based on three important ingredients is set out. First, we make use of DAS data with its benefits; second, we incorporate multiple modes of data in the inversion; and third we carry out the near surface model determination through a trans-dimensional inversion procedure, in which model size is included as an unknown. Parallel tempering with twenty chains is employed to speed up the convergence. Data errors are estimated through a non-parametric iterative process. Synthetic testing suggests that multimode surface wave inversion not only provides additional constraints on the structure, but also improves the noise estimation. Our method is further applied to the active source DAS dataset acquired at the Containment and Monitoring Institute Field Research Station in Newell County, Alberta, Canada. The results are consistent with the regional lithology background.
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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 | 0.001 | 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".