EGU2020-22441
https://doi.org/10.5194/egusphere-egu2020-22441
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Rapid Detection of Earthquake Rupture Directivity Using Strong Ground Motion Data in Taiwan

Cheng-Feng Wu, Ting-Li Lin, and Ying-Chi Chen
Cheng-Feng Wu et al.
  • Department of Earth Sciences, National Cheng Kung University, Tainan, Taiwan

In the past decade, there have been several disaster earthquakes occurred in Taiwan.
From the observed data of the disaster earthquakes, the stations located in the source
rupture direction have obvious directivity pulses, and the distribution of the earthquake
disaster is related to the peak ground velocity. Therefore, how to use a large and high-
dense seismic database to develop a near-real-time detection system on the earthquake
rupture directivity, which is a very important task in Taiwan. In this study, we determine
the earthquake rupture directivity using near-field velocity data from 1991 to 2018, which
were collected under the Taiwan Strong Motion Instrument Program (TSMIP). The used
method is mainly constructed in the interpolation of the peak-ground-velocity map and
the directional attenuation regression analysis. Through the analysis of moderate-to-large
magnitude (M L > 5.5) seismic events, the source rupture directivity can be detected
effectively and quickly by the applied method. The detection results are also comparable
with those from the previous source studies. We also find out a linear relationship between
the directivity effect and earthquake magnitude. Since the TSMIP station may provide
real-time services in the future, the detection system proposed by this research can quickly
provide disaster prediction information, which is of great importance for earthquake
emergency response and hazard mitigation.

How to cite: Wu, C.-F., Lin, T.-L., and Chen, Y.-C.: Rapid Detection of Earthquake Rupture Directivity Using Strong Ground Motion Data in Taiwan, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22441, https://doi.org/10.5194/egusphere-egu2020-22441, 2020