AN INTEGRATED TIME-SERIES SURFACE DEFORMATION ANALYSIS USING INSAR, GNSS, AND NLDAS DATA

In late 2020, the City of Wichita in Kansas was rattled by over 20 earthquakes with a magnitude greater than 2.5. Deep analysis of the late 2020 Wichita earthquake indicated the largest of the earthquakes has a magnitude of 3.8 and all the earthquakes were in the northeast part of the city. The demarcated epicenters indicated a general northeast-trend and orthogonal northwest strike directions. These trends have raised a concern about the possibility of triggering larger seismological phenomena if the earthquakes reactivated the Precambrian granitic basement faults. Hence, we investigate time-series surface deformation dynamics from January 2017 to May 2021. We utilize Geodetic observations, particularly those from Remotely Sensed Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) stations found at and near the city. Additionally, we compare the InSAR time-series deformation with the soil moisture variation through time using the North American Land Data Assimilation System (NLDAS). Our results revealed that SBA-derived time-series at those pixels located at GNSS stations exhibited similar vertical displacement dynamics. For example, the GNSS time-series data recorded at the ICT3-IGS14 station has shown equivalent surface displacement measurements with the SBA time-series for over four years. Particularly both measurements exhibited an abrupt uplift of 45 millimeters from 8 May 2018 to 13 June 2018. The distinct uplifting detected by both the ICT3-IGS14 station and InSAR time-series and the comparable correlation of ICT1-IGS14 and KSBA-IGS14 with Zone 1 and Zone 3 indicated that the results from our SBA time-series techniques are robust. Moreover, our results revealed similar time-series trends and seasonality patterns over the four years, especially the surface deformation dynamics is distinct when there is a transition between wet season to the dry season and vice versa. Our results from SBA time-series techniques and the time series soil moisture variation (NLDAS) are highly correlated with a correlation coefficient of 0.72. Yet, most earthquakes are expected to have a strike-slip mechanism and are relatively low moment magnitudes; hence, the surface displacement by the earthquakes either near or below the detection threshold of the InSAR SBA time-series.