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Bayesian Source Mechanism Inversion and Uncertainty Quantification With Dense Array Strong Motion Data for 2022 Luding Earthquake in China’s Sichuan

Description: 

Sichuan in China has a high level of seismic activity due to the complex active fault zones nearby, resulting from the collision between the Indian Plate and Eurasian Plate. Understanding the source physics of past earthquakes in Sichuan is significant for ground motion prediction and seismic risk analysis for the future. In this study, we developed a Bayesian approach to infer the source mechanisms of earthquakes given first P polarities and S/P amplitude ratios, accounting for the velocity model misspecifications. The first P polarity is described by a probit model, while the S/P amplitude ratio is represented by a Gaussian model. This method was applied to the 09/05/2022 M6.8 Luding earthquake, the most devasting earthquake in Sichuan since 2017, using acceleration data from 510 local strong motion stations. The Bayesian inference was conducted to strong motion data bandpass filtered to different frequency ranges. Our results show a frequency dependent moment tensor distribution. Although the strong motion instruments are less sensitive compared to the broadband data, the good coverage of strong motion stations in the region with epicenter distance less than 50 km provides good constraints for the moment tensor and reduces the uncertainties.

Session: Earthquake Source Parameters: Theory, Observations and Interpretations

Type: Oral

Date: 4/18/2023

Presentation Time: 05:00 PM (local time)

Presenting Author: Chen Gu

Student Presenter: No

Invited Presentation: 

Authors