Initial results from a simplified sub-sampling approach for Distributed Acoustic Sensing

Abstract

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.