Quantifying Uncertainties in Ground Motion-Macroseismic Intensity Conversion Equations. A Probabilistic Relationship for Western China

ABSTRACT

We analyze the global and local uncertainties of ground motion-macroseismic intensity conversion equations and derive new probabilistic relationships between the macroseismic intensity and peak ground motion parameters (peak ground acceleration and peak ground velocity) for western China based on the assumption that the peak values of ground motion are randomly distributed for each MMI level, bounded by a normal distribution. For this purpose, a strong ground motion database consisting of 37 moderate- to large-magnitude earthquakes that occurred in western China between 1994 and 2017 is employed along with the corresponding modified Mercalli intensity (MMI) information inferred from isoseismal maps and earthquake damage reports. The mean value and standard deviation parameters in the probabilistic formulas are obtained from the result of the analysis of the local uncertainties of the data compiled for Western China. We propose a method for the rapid assessment of computer-generated intensity maps using the new probabilistic formulas. Our method composed of two steps. (1) According to a Bayesian formula, the possibility, expectation and standard deviation of the seismic intensity are calculated by the ground motion parameters recorded at a site station. (2) Furthermore, expectation and standard deviation maps of the seismic intensity throughout an area are obtained through spatial interpolation. Finally, an application is illustrated to validate this new methodology by presenting rapidly estimated (computed) intensity maps of the 8 December 2016 Hutubi (Ms 6.2) earthquake in western China.

KEYWORDS:

• Ground motion
• macroseismic intensity
• uncertainties
• intensity maps
• modified Mercalli intensity

Acknowledgments

The author’s visiting professorship at the Stanford University, where discussions with Gregory G. Deierlein initiated the author’s interest in this subject. The comments from Jose Moratalla at GNS Science, C. Bruce Worden at the USGS and one anonymous reviewer were very helpful in improving and focusing the paper.

Additional information

Funding

The work presented in this paper was supported by fund from the National Natural Science Foundation of China (No. 51878631), the Scientific Research Fund of Institute of Engineering Mechanics, CEA (No.2019B05, 2019C10), and the Heilongjiang Provincial Foundation for the Returned Overseas Chinese Scholars (2017QD0045).