Site class based seismic magnitude prediction equations for earthquake early warning

• Published in: Journal of seismology Vol. 28; no. 3; pp. 765 - 786
• Main Authors: Mugesh, A., Desai, Aniket, Jakka, Ravi S., Kamal
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2024; Springer Nature B.V
• Subjects: Correlation; Correlation coefficient; Correlation coefficients; Datasets; Early warning systems; Earth and Environmental Science; Earth Sciences; Earthquake magnitude; Earthquake prediction; Earthquakes; Error analysis; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Ground motion; Hydrogeology; Movement; P waves; Parameters; Regression analysis; Seismic activity; Soil; Statistical analysis; Structural Geology; Wave motion; Waves; Site class; time-averaged shear wave velocity; Earthquake early warning parameters; Vs; Kiban Kyoshin Network (KiK-net)
• ISSN: 1383-4649; 1573-157X
• DOI: 10.1007/s10950-024-10213-8

Although earthquake early warning (EEW) systems have advanced significantly, accurately determining earthquake parameters from the initial 3 s of P-wave motion remains challenging. Factors such as the complexity of the earthquake source and variability of ground motion due to site conditions contribute to this difficulty. This article aims to investigate how local site conditions impact the correlation between EEW parameters and earthquake magnitude, to better understand the influence of site conditions on the accuracy of EEW systems. Specifically, the study examines the effect of variation site conditions on commonly used EEW parameters, such as average characteristic period ( τ c ) and peak displacement amplitude ( P d ), for different site classes. A dataset of 432 strong-motion records with magnitude ranging from 5 to 7.3 was analyzed and site characterization information from the Kiban Kyoshin Network (KiK-net) in Japan was used. A linear relationship between EEW parameters ( τ c , P d ) and magnitude for the combined dataset (all data), as well as separate datasets based on site classes C (very dense soil and soft rock) and D (stiff soil site), was developed, and then the statistical parameters, correlation coefficient value ( R ), and standard deviation error (SD) in the linear regression analysis were compared. The study finds that τ c and P d have a significant correlation with magnitude when separate correlations are developed for site classes C and D. Absolute residual error and percentage error analyses were carried out. It was found that magnitude prediction errors were reduced particularly for class D sites. Overall, the study suggests need for use of site class based magnitude prediction equations in earthquake early warning, especially for softer soil sites.