Improving Earthquake Early Warning Initial Peak Ground Motion Magnitude Estimation with Station Corrections: A Case Study Using the P-Alert Network in Taiwan

ABSTRACT

Earthquake early warning systems assess the size of an earthquake based on initial ground motion and provide warnings before the arrival of large waves. We employ the iterative regression to simultaneously estimate the station correlations and the corresponding linear relations for magnitude. We report the standard error reductions of the magnitude estimation based on the initial peak P-wave acceleration ($P_a$), velocity ($P_v$), and displacement ($P_d$) as $14.52\mathrm{\%}$, $7.63\mathrm{\%}$, and $7.58\mathrm{\%}$, respectively. Factor analysis of the stationary correction reveals its correlation with several factors. This study shows that station correction can help to improve the precision of magnitude estimation in the future.

KEYWORDS:

• Earthquake Early Warning
• Statistical Learning
• Ground Motion
• Ground Motion

Authors’ Contributions

T.C. H developed the theoretical formalism, conducted the analysis, and wrote the paper. Y.M. W supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The strong-motion waveform records from the P-Alert network can be downloaded at http://palert.earth.sinica.edu.tw/db/(last accessed July 2022).

Additional information

Funding

Our work was supported by the Ministry of Science and Technology (MOST) of Taiwan under MOST 106-2116-M-002-019-MY3 and 109-2116-M-002-030-MY3. Our work was also supported by the Research Center of Future Earth of the National Taiwan University under grant number 107L901002 and financially supported by the NTU Research Center for Future Earth from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan. The authors thank Mr. Benjamin Ming Yang and Mr. Chia-Hao Yeh for their help to access to Vs30 and Z1.0 EGDT database for TSMIP.