Application of the Empirical Hybrid Rupture Fault Model in the June 1, 2022 Lushan Earthquake

Xingzhe Li, Xueliang Chen () and Kelin Chen
Additional contact information
Xingzhe Li: Guilin University of Technology, College of Civil Engineering
Xueliang Chen: Institute of Geophysics, China Earthquake Administration
Kelin Chen: Beijing University of Technology, College of Architecture and Civil Engineering

A chapter in Proceedings of the 11th Annual Meeting of Risk Analysis Council of China Association for Disaster Prevention (RAC 2024), 2025, pp 219-224 from Springer

Abstract: Abstract This study applies empirical magnitude-source parameter relationships to the Mw 5.9 Lushan earthquake on June 1, 2022. By employing a hybrid source model combining a deterministic asperity model and the stochastic K2 model, 30 sets of source rupture models were generated using a truncated normal distribution method to randomly sample global and local source parameters consistent with empirical constraints. Acceleration response spectra for these 30 source models were simulated at 20 stations via the stochastic finite-fault method. The source model exhibiting the smallest residual between its response spectra and the average response spectrum was selected as the optimal source characterization for the Lushan earthquake.

Keywords: Source parameters; asperity model; k-squared model; finite faults (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-946-9_28

Ordering information: This item can be ordered from
http://www.springer.com/9789464639469

DOI: 10.2991/978-94-6463-946-9_28

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().