The role of fiber geometry and gauge length in multiparameter elastic FWI of coiled DAS fiber data
Bibliographic record
Abstract
Distributed acoustic sensing (DAS) is a rapidly developing technology for seismic data acquisition. The ease with which it can be deployed in boreholes provides access to the rarely sampled transmitted portion of the wavefield making DAS attractive for FWI in reservoir settings. Multi-parameter FWI is conventionally formulated for one or more components of particle velocity, whereas DAS supplies single component measurements of tangential strain along the fiber. The directionality of the fiber is therefore expected to play a significant role in parameter resolution. However, due to the long gauge lengths relative to the fiber wind required for meaningful signal-to-noise ratio of acquired DAS data, the directional information supplied by shaped fibers is limited. The gauge length in relation to fiber shape is also expected to significantly affect parameter resolution. With this in mind we set out with the goals of (1) developing analytic description for the relationship of fiber shape, gauge length, and elastic wave sensitivity, (2) the development of a strategy for inclusion of DAS in FWI, (3) understanding the role of fiber geometry in parameter resolution, and (4) acquiring insight into the role of the gauge length relative to the fiber geometry in parameter resolution. By examining 2D simulations for helical fibers embedded in a simple model we examine the effect fiber wind rate and gauge length have on parameter resolution in FWI of DAS data. It is observed that the wind rate of the fiber has important implications for the accuracy of parameter estimates and that gauge lengths much smaller than the wind-rate are required to fully leverage the fiber shape. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 1:50 PM Presentation Time: 3:55 PM Location: Poster Station 3 Presentation Type: Poster
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".