Transient Deformation from the 2016 Meinong Earthquake in Southwestern Taiwan Revealed by Kalman Filter Analysis of GNSS Time Series

Weighted least-squares is commonly used in GNSS time series analysis due to its simplicity and efficiency. However, practical applications often face fitting issues due to unknown transient and time-varying signals in the data. To address this, the study adopts the Kalman filter, a least-squares estimation method for stochastic processes, to estimate transient deformations associated with the 2016 Mw 6.4 Meinong earthquake in southwest Taiwan. Because crustal stress undergoes changes and requires time to recover and adjust following a large earthquake, it would be natural to include acceleration in the state vector of Kalman filter when fitting the GNSS time series. Through synthetic data tests, we demonstrated that a simple AR(1) process for acceleration can significantly improve the estimation of co-seismic and post-seismic displacements. Using this model, we analyzed time series from 76 continuous GNSS stations in the study area and found that notable transient ground displacements are confined to the region bounded by active faults. These movements lasted for about two years after the earthquake and are primarily associated with thrust fault­-fold structures involving the Gutingkeng mudstone formation at shallow depths. Our findings imply that inelastic deformation can be prevalent under large seismic stress in southwestern Taiwan, emphasizing the need to consider such factors when assessing potential earthquake hazards.