Open AccessInitial results from a simplified sub-sampling approach for Distributed Acoustic Sensing
Author(s) -
Robert Ellwood,
Alastair Godfrey,
Chris Minto
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1761/1/012002
Subject(s) - computer science , sampling (signal processing) , process (computing) , range (aeronautics) , data collection , real time computing , term (time) , shore , environmental science , telecommunications , engineering , statistics , geology , mathematics , aerospace engineering , physics , quantum mechanics , detector , operating system , oceanography
Recently, interest has risen in the use of Distributed Acoustic Sensing (DAS) to monitor the condition of sub-sea cables connecting off-shore windfarms. Certain failure modes of these cables develop gradually, over the course of weeks to months, in response to external environmental factors. DAS provides a wealth of information on physical processes occurring over a long linear length. A significant challenge in acquiring all this information is in managing the volume of data captured (in excess of 1TB a day). This paper presents results from an investigation into an approach to adapt the way the data is acquired and stored, whilst not inherently biasing the process. The approach combines a range of traditional techniques, as well as a simplified implementation of the already well established sparse sampling technique. This approach is applied to the collection of data from a windfarm export cable over a period of 876 hours. Analysis of this data demonstrates the systems capability to practicably capture long term trends in the data due to environmental factors.