Elastic full Waveform Inversion with Coiled Distributed Acoustic Sensing Fibres
Bibliographic record
Abstract
Summary Distributed acoustic sensing (DAS) is a a powerful technology for seismic data acquisition. Employing optical fibres, DAS senses strain induced by seismic wavefields along the tangent of the fibre. The noninvasive nature of DAS allows for its use in borehole applications not accessible by traditional geophones, including producing wells, injecting wells, and treatment wells during hydraulic fracture treatment. The complementary data supplied by DAS at transmission angles holds the potential to improve parameter estimates provided by inversion frameworks like full waveform inversion (FWI). Little work has focused on the inclusion of the data supplied by DAS in FWI, usually assuming a straight fibre in a vertical well. Here we present a method for full waveform inversion of DAS data that is flexible in its ability to consider data from arbitrarily shaped DAS fibres. The approach we propose here also offers the ability for simultaneous inversion of complementary DAS and geophone datasets, improving the quality of parameter estimates over considering either dataset alone.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".