  Distributed acoustic sensing technology is increasingly being used to support
production and well management within the oil and gas sector, for example to
improve flow monitoring and production profiling. This sensing technology is
capable of recording substantial data volumes at multiple depths within an oil
well, giving unprecedented insights into production behaviour. However the
technology is also prone to recording periods of anomalous behaviour, where the
same physical features are concurrently observed at multiple depths. Such
features are called `stripes' and are undesirable, detrimentally affecting well
performance modelling. This paper focuses on the important challenge of
developing a principled approach to identifying such anomalous periods within
distributed acoustic signals. We extend recent work on classifying locally
stationary wavelet time series to an online setting and, in so doing, introduce
a computationally-efficient online procedure capable of accurately identifying
anomalous regions within multivariate time series.
