Automated Insar Time-Series Analysis Tool for Geological Interpretations in Near-Real Time

Large EO datasets are becoming increasingly challenging to analyse and interpret due to their size, a problem that will continue to worsen with longer satellite operating times. In this manuscript, we present an automatic tool to analyse interferometric synthetic aperture radar (InSAR)-derived deformation time series using a supervised machine learning regression algorithm. This processing tool at its current stage of development can map areas of anomalous ground motions, and different extents of seasonal behaviours. With the addition of British Geological Survey proprietary geological hazard susceptibility datasets, we can begin to make further interpretations of the ground movements. This algorithm will aid in the task of identifying areas of substantial long-term ground motions due to specific geological hazards. This will be applied specifically to vulnerable locations such as coastlines, and will be used to identify locations at risk of exacerbated seasonal ground motions due to the worsening effects of climate change.