# Offline-Online Change Detection for Sentinel-1 InSAR Time Series
## 5 Conclusions  
In this paper we present a statistical approach for detecting offsets and gradient changes in InSAR time series. A key requirement for both our offset and the gradient change detectors is that a sufficiently long time series already exists from which the standard deviation can be calculated. Over 5 years of data is now available from the Sentinel-1 satellite constellation. We believe that this is sufficient to determine the population standard deviation of the detection variables. We assume that this dispersion level remains the same for new additions to the time series, which enables online detection. Our offset detector assumes that the first, second and third difference series are stationary and identifies statistically significant peaks in these series. The gradient change detector uses rolling windows to identify statistically significant moments in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data (approximate 5 by 15 m pixel size) and the spatial continuity of geophysical deformation signals to reduce false detections. Assuming that spatially isolated detections are false positives, we reduce these by using a Gaussian blur filter. In our test data stack over Hatfield Moors in the UK this resulted in an average reduction of 26% in the number of change detections. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation that precede dangerous geophysical phenomena, for example from landslides or volcanic eruptions. Alternatively it could be used to monitor for changes resulting from subsurface fluid injection/withdrawal processes.  
Conceptualization, E.H. and A.N.; methodology, E.H.; software, E.H. and A.N.; validation, A.N., L.B.; formal analysis, E.H.; investigation, E.H., L.B.; resources, C.J.; data curation, E.H., A.N.; writing--original draft preparation, E.H.; writing--review and editing, E.H., A.N., C.J., L.B.; visualization, E.H.; supervision, C.J.; project administration, E.H.; funding acquisition, E.H., A.N. All authors have read and agreed to the published version of the manuscript.  
The research for technique development and testing was funded by the Innovation Flexible Fund (IFF) at the British Geological Survey. The data processing was supported by the ACT-2 SENSE project. SENSE (Assuring integrity of CO2 storage sites through ground surface monitoring) project No. 299664, has been subsidized through ACT (EC Project no. 691712) by Gassnova, Norway, United Kingdom Department for Business, Energy and Industrial Strategy, Forschungszentrum Julich GMBH, Projekttrager Julich, Germany, The French Agency for the Environment and Energy Management, The United States Department of Energy and State Research Agency, Spain. Additionalsupport from Equinor and Quad Geometrics and permission to use data from the Krechba Field by In Salah Gas JV are appreciated.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments:** We gratefully acknowledge helpful discussions with colleagues during the early stages of this research, particularly [PERSON] at the University of Leeds. Thanks to [PERSON] for the tip on using numpy stride tricks to improve efficiency. We would like to thank the European Space Agency for free provision of the Sentinel-1 radar imagery. The authors publish with the permission of the Executive Director of the British Geological Survey.  
**Conflicts of Interest**: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.