Revealing Crustal Deformation and Strain Rate in Taiwan Using InSAR and GNSS

Abstract

Taiwan is located between the Eurasian Plate and the Philippine Sea Plate. The Philippine Sea Plate moves toward northwest with a rate of >80 mm/yr and causes an oblique collision with the Eurasian Plate (Lin et al., 2010;Yu et al., 1997). This collisional tectonics has given rise to a few geologic provinces, including (from west to east) Coastal Plain (CP), Western Foothills (WF), Hsueshan Range (HR), Central Range (CR), Longitudinal Valley (LV), and the Coastal Range (CoR) (Figure 1a). The high collision rate has resulted in a large number of earthquakes in Taiwan, and several devastating events have been located in west Taiwan where the majority of the population resides.Interseismic deformation describes the gradual accumulation of crustal strain within the tectonic plate and along the plate boundaries before its sudden release as earthquakes. Global Navigation Satellite Systems (GNSS) and InSAR have been widely used to quantify the interseismic crustal deformation (Elliott et al., 2016). The high convergence rate in Taiwan is among the highest in the world and suggests significant elastic strain accumulation in a relatively short period of time (Figure 1b).Although Taiwan has one of the highest GNSS network densities in the world, GNSS measurements alone still could not confidently identify interseismically creeping faults when they are not close to these faults. For example, a GNSS-based dilatation rate map (Figure 1c) highlights regions undergoing interseismic contraction in east and southwest Taiwan and extension in northeast Taiwan in a broader scale, but it remains challenging to identify active faults or to determine interseismic fault slip for closely spaced faults. Alternatively, InSAR provides high spatial resolution measurements of surface deformation at a cm-level accuracy level (Bürgmann et al., 2000;

Revealing the Kinematic Characteristics and Tectonic Implications of a Buried Fault through the Joint Inversion of GPS and Strong-Motion Data: The Case of the 2022 Mw7.0 Taiwan Earthquake

“…The following conclusions are obtained: (1) The displacement obtained from the strong-motion data after the baseline correction through two integrations has a good consistency with the displacement obtained from the GPS data, which indicates the reliability of the displacement obtained from the strong-motion data. (2) The horizontal displacement field generally shows a four-quadrant distribution, indicating the left-lateral strike-slip characteristics of the earthquake. The east and west sides of the LVFZ are dominated by subsidence and uplift, respectively.…”

“…We note three key features of the model: (1) slip is concentrated at depths greater than 20 km and extends along the strike for a length of approximately 40 km, with a maximum slip of 2.5 m, at a depth of approximately 10 km. (2) The optimal results of the joint inversion indicate that the Mw7.0 earthquake primarily consists of two asperities. The larger main asperity is located in the northeast direction of the hypocenter at a depth of approximately 10 km, while a relatively smaller asperity is positioned in the northeast direction of the larger asperity, close to the surface.…”

“…As a stress release process, this earthquake sequence indicates that there is high stress accumulation in the southeastern region of Taiwan. The Taiwan orogen is a critical tectonic position where the Philippine Sea Plate interacts with the Eurasian Plate, that acts as a transition zone for the convergence mechanism of the Philippine Sea Plate relative to the Eurasian Plate [2,3]. The Philippine Sea Plate subducts northward beneath the Eurasian Plate in northeastern Taiwan, while it becomes the Eurasian Plate subducting eastward under the Philippine Sea Plate in southeastern Taiwan [4].…”

Open Access GNSS Data for Studies of the Lithosphere

“…InSAR can provide a more spatially dense estimation but is limited by temporal resolution (currently every week or so), only providing displacements or velocities in the direction of the line-of-sight of the satellite, which is at a high angle from the Earth's surface, and InSAR observations are nearly insensitive to northsouth oriented displacements or velocities because of the orbital geometry. Because of these limitations, the most comprehensive results are obtained when combining InSAR and GNSS data in some optimal fashion (e.g., M. Chen et al, 2024;Franklin & Huang, 2022;Maubant et al, 2022;Ou et al, 2022;Watson et al, 2024;W. Wu et al, 2024).…”