11:00 AM - 11:15 AM
[SCG60-08] Preliminary Analysis of Near-Fault Ground Motion Records During the 2022 Taitung, Taiwan, Earthquake (Mw6.9)
Keywords:2022 Taitung Earthquakes, Strong-motion, Permanent Displacement, Surface Fault
In September 2022, a foreshock (Mw6.6) and mainshock (Mw6.9) occurred in southeastern Taiwan. Surface faults appeared, and long-period pulses (approximately 4 s) and permanent displacements were observed in the Taitung area. It is extremely rare for such observation records to be obtained from multiple stations near a fault. In this study, to investigate the relationship between the source characteristics and near-fault ground motions of earthquakes in which faults appeared on the ground surface, we inferred fault planes and fault rupture behavior of the mainshock based on a preliminary analysis of observation records near surface faults.
2.Surface faults and active faults
The Central Geological Survey (2022) has surveyed the locations where surface faults caused by the earthquakes. The active faults, strong-motion stations, and locations of surface faults are shown in Figure 1. The Longitudinal Valley Fault (Chihshang Fault) is recognized as an east-dipping reverse fault and Central Range Fault (CRF) is considered to be a west-dipping reverse fault. Based on the aftershock distribution, the hypocenter of the mainshock is inferred to be deep along the CRF. In other words, the surface faults and aftershock distributions of these earthquakes are inconsistent.
3.Observation records
A comparison of the peak ground acceleration (PGA) and peak ground velocity (PGV) on the ground of the mainshock with the ground motion model (GMM) (Si and Midorikawa, 1999) are shown in Figure 2. The ground conditions in the GMM are assumed to be equivalent to those of the bedrock because the averaged S-wave velocity to 30 m around the epicenter is hard, 360-760 m/s (Chen et al., 2022). The observed PGAs are approximately within the standard deviation of the GMM. The PGAs near the fault tend to not exceed approximately 500 cm/s2. Because the PGVs of the stations north of the epicenter are larger than those of the stations near the epicenter, a directivity effect owing to the propagation of the rupture to the north of the fault is suggested.
The observed displacement vectors of the GNSS (Central Geological Survey, 2022) and strong-motion records of the mainshock are shown in Figure 3. Examples of permanent displacements obtained from strong-motion records are shown in Figure 4. At a station south of the epicenter, the horizontal displacements (maximum displacement of approximately 1 m were) recorded. Remarkable vertical and horizontal displacements of approximately 1 m were recorded at the station north of the epicenter. This suggests that the slip distribution differs between the north and south of the fault area. The maximum fault displacement estimated from the observed permanent displacement near the fault agrees with the empirical relationship proposed by Matsuda (1975).
4. Summary
A preliminary analysis of observation records near the fault of the 2022 Taitung earthquake was conducted. The following conclusions were drawn.
・Surface fault locations and aftershock distribution including the mainshock do not agree with each other.
・The attenuation characteristics of PGA and PGV are comparable to those of the previous GMM.
・Horizontal displacement is predominant on the southern side of the fault. However, both vertical and horizontal displacements are predominant on the northern side of the fault, suggesting that the fault slip was complex.
・The fault displacement estimated from the permanent displacement of the strong-motion record is almost the same as the empirical relationship.
In the future, we plan to study the source characteristics focusing on broadband period range ground motion for the mainshock (Mw6.9). We will set fault planes based on fault survey information and distribution of particle motions of strong-motion records. Then, we will carry out the waveform inversion analysis and the forward modeling of strong-motion generation area using the empirical Green’s function method.