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Introduction {#intro}
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The ability to accurately analyse geoscience data at, or close to, real time is becoming increasingly important. For example, within the oil and gas sector this need can arise as a consequence of (i) the sheer volume of data now being collected and (ii) operational considerations. It is this setting that we consider in this article, seeking to enable the rapid identification of certain anomalous features within Distributed Acoustic Sensing data obtained from an oil producing facility. Specifically, we seek to build on recent work within the non-stationary time series community to develop an approach that permits the online monitoring of these complex signals.

The technology used to generate the data considered in this article, Distributed Acoustic Sensing (DAS), involves the use of a fibre-optic cable as a sensor in which the entire length of the fibre is used to measure acoustic or thermal disturbances. DAS originates from the defence industry where it is commonly used in security and border monitoring [@Owen]. Recently, the technology has been applied within the oil and gas industry, for example in pipeline monitoring 