Using DAS for reflection seismology - lessons learned from three field studies
Bibliographic record
Abstract
Distributed acoustic sensing (DAS) has rapidly gained recognition for its potential for seismic imaging. For surface reflection seismology, the wide spatial aperture afforded by DAS is a primary motivation for its application, however the lower SNR of DAS has proven to be a significant impediment to acquiring data that can replace conventional receiver arrays. A further limitation of DAS cables is that the strain-dependent response is insensitive to acoustic energy which arrives orthogonal to the cable axis, reducing its effectiveness at seeing energy reflected from the deep subsurface. To enhance the sensitivity of DAS cables for reflection seismology, we have trialed at three field sites DAS cables with helical construction in which there is a significant component of optical fiber that is coincident with arriving broadside energy. We have installed helically wound DAS cables at the PTRC Aquistore Project in Saskatchewan, Canada and the CO2CRC Otway Project in Nirranda South, Victoria, Australia in shallow trenches. For the ADM Intelligent Monitoring Systems Project in Decatur, Illinois, USA we used a horizontal directional drilling method to install DAS cables at a depth that is greater than can be achieved using trenched installation. At the Otway and ADM sites we operated surface orbital vibrators (SOVs) at fixed locations to enhance sensitivity by stacking large numbers of sweeps. We present survey results from the three sites. Analysis of both vibroseis survey and SOV results show that the helical cable design achieves its primary objective of improving sensitivity to reflected energy, with further gains needed to achieve the sensitivity of conventional geophones.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".