# Offline-Online Change Detection for Sentinel-1 InSAR Time Series  
[PERSON]  
detect changes in ground motion and provide alerts to responsible authorities that changes are occurring at their site of interest. For example, at volcanoes where 1 in 3 active volcanoes now have InSAR measurements [12], or at a mining site where incipient motion could be a precursor to tailings collapse [13]. In this paper, we describe a new methodology that exploits the existing archive and continuous data collection from the Sentinel-1 satellite to detect anomalous ground motions statistically.  
Time series change detection is not new. There are many existing methods from Bayesian change point detection, CUMSUM, segmentation and machine learning methods (see [PERSON] and [PERSON] [14] for a review). These are used extensively in the technology sector, e.g., by streaming services to monitor spikes in complaints, and by analysts to monitor changes in stock markets [15; 16]. These methods are designed to accurately detect changes in a single, albeit long, time series. Most of these methods are not suitable for InSAR time series where in any one scene we may have hundreds of thousands if not millions of pixels, each with its own deformation history. The computational costs of applying complex methods to InSAR time series is therefore prohibitive.  
There are generally two approaches for automatic time series change detection, offline methods and online methods. Offline methods determine changes in an existing series, where data exists before and after a change point. This is relatively easier and prone to fewer detection errors. These methods have already been applied to InSAR to determine change points. For example [PERSON] et al. [17] used the Bayesian Information Criterion (BIC) to determine break points considering the whole time series for a given pixel. However, for near-real time analysis we require online detection methods where only information preceding a data point can be used to check for changes. This enables continuous monitoring as new data are added to the time series.  
Recently, machine learning approaches have been used to search through large volumes of InSAR images to detect patterns that may be related to geological hazards [18; 19; 20]. These methods generally require large volumes of training data and have mostly been applied to detect volcanic deformation in radar interferograms or InSAR time series [8; 21].  
Our method, detailed in this paper, uses a hybrid offline-online statistical approach that is parallelised and vectorised for computational efficiency in order to deal with the large data volumes associated with InSAR time series. Therefore it is not constrained to detecting deformation from any specific geophysical process. Additionally, we exploit the spatial correlation of deformation signals to reduce the false positives in our detections.  
We apply and test our method to an InSAR time series dataset generated over Hatfield Moors (Yorkshire, UK) (Figure 1). Hatfield Moors along with Thorne, Goole and Crowle Moors make up the Humberhead Peatlands, the largest raised bog wilderness in lowland Britain covering a total of 2887 hectares [22; 23]. This remnant of a large wetland is a Natural England nature reserve that overlies a former hydrocarbon reservoir where gas was extracted between 1986 to the late 1990s. Since 2000, this site has been used as a gas storage facility run by Scottish Power [24]. Natural Gas is compressed and pumped into the natural underground sandstone reservoir for storage and then extracted when needed. 4.1 billion cubic tonnes of gas can be stored here. As part of the SENSE project [25], the British Geological Survey is monitoring the ground stability conditions at this site as an analogue for Carbon Capture and Storage (CCS) sites. It is hoped that monitoring potential ground motion related to the injection and extraction of stored gas will allow the development of InSAR based techniques to monitor on-land CCS sites in the future.