InSAR applications to earthquake-triggered ground failure mapping and modeling

Earthquake-triggered ground failure, including liquefaction, lateral spreading, and landsliding, contributes significantly to earthquake-induced damage and losses. Ground failure is a challenging process to model that requires both high-quality and high-resolution predictive variables, as well as complete inventories of past ground failure events. Here, we demonstrate the potential of Interferometric Synthetic Aperture Radar (InSAR) data and products to contribute to ground failure modeling and identification using the 2019 Ridgecrest Earthquake Sequence as a case study. We focus on two predictive variables: pre-event deformation time series and antecedent soil moisture. Soil moisture is a major factor in ground failure susceptibility, but it is difficult to measure at high spatial resolution using global remote sensing methods. By using InSAR coherence variability as a proxy for soil moisture variability, we use K-means clustering to bin pre-event (2015 to mid-2019) Sentinel-1 pixels with similar temporal behaviors to detect areas with soil moisture fluctuations. We find that certain sets of pre-event clusters coincided with mapped earthquake triggered ground failure derived from optical imagery. Using the same Sentinel-1 data, we also generated a pre-event InSAR deformation time series. We find a correlation between localized pre-seismic subsidence features in the Searles Dry Lake (on the order of 2-10 cm/year) and co-seismic lateral spreading and liquefaction. Antecedent deformation is not a known predictor of the location of earthquake-triggered ground failure. While these findings require further investigation, it suggests a potential new frontier for the utility of InSAR data in earthquake-triggered ground failure research.