DAS field dataset to compare technologies and deployment scenarios
Bibliographic record
Abstract
This report describes a Distributed Acoustic Sensor (DAS) dataset acquired by DigiMon partners at the Containment and Monitoring Institute’s (CaMI) Field Research Station (FRS), Canada, between 6th to 10th September 2021. The field dataset contributes to the Deliverable D1.1 of the DigiMon project (DAS field dataset to compare technologies and deployment scenarios), which supports tasks 1.2 and 1.3 of the project.\nThe objective of the DigiMon project is to develop an early-warning system for Carbon Capture and Storage (CCS), which utilises a broad range of sensor technologies including DAS. While the system is primarily focused on CCS projects located in shallow offshore environment of the North Sea, it is also intended to be adaptable to onshore settings. Some of the key areas that the systems will monitor include the movement of the plume within the reservoir, well integrity, and CO2 leakage into the overburden. A combination of both active and passive seismic methods will be deployed to track the movement of CO2, for example seismic reflection to image seismic velocity changes and microseismics to capture fault activation. Acquiring seismic surveys using DAS is highly novel and offers cost-effective approach which can significantly increase the spatial resolution of the survey data; however, it has had limited use in the operational environment with several technical challenges still needing to be resolved, such as the transfer function of DAS.\nCaMi FRS was selected as a field test location as the site has been specifically established to advance the development of monitoring technologies and protocols for CCS operations. At CaMi FRS, several different monitoring arrays have been installed which are directly applicable to DigiMon. This includes a 5km loop of DAS optical fibre, located with a 1.1 km surface trench and two observation wells, an array of surface and borehole geophone nodes, and 6 broadband seismometers operating by the University of Bristol. This monitoring infrastructure has been primarily installed to monitor CO2 injections into the Basal Belly River sandstone formation at approximately 300m below ground level. Injection of CO2 began at FRS in 2019 and during this time microseismic events have been recorded, albeit at shallower levels than the injection point. The site therefore provides a potential DAS dataset which contains both active and passive measurements for the DigiMon project. The abundance of instrumentation including DAS, geophones, and broadband seismometers provides a unique chance to test the capacity of these instruments for C02 storage monitoring.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.007 | 0.010 |
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".