A statistical methodology for classifying earthquake detections and for earthquake parameter estimation in smartphone-based earthquake early warning systems

被引:1
作者
Massoda Tchoussi, Frank Yannick [1 ]
Finazzi, Francesco [1 ]
机构
[1] Univ Bergamo, Dept Econ, Bergamo, Italy
基金
欧盟地平线“2020”;
关键词
maximum likelihood (ML); Monte Carlo simulation (MC); hypothesis testing (HT); optimization algorithm; classification;
D O I
10.3389/fams.2023.1107243
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Smartphone-based earthquake early warning systems (EEWSs) are emerging as a complementary solution to classic EEWSs based on expensive scientific-grade instruments. Smartphone-based systems, however, are characterized by a highly dynamic network geometry and by noisy measurements. Thus, there is a need to control the probability of false alarms and the probability of missed detection. This study proposes a statistical methodology to address this challenge and to jointly estimate in near real-time earthquake parameters like epicenter and depth. The methodology is based on a parametric statistical model, on hypothesis testing and on Monte Carlo simulation. The methodology is tested using data obtained from the Earthquake Network (EQN), a citizen science initiative that implements a global smartphone-based EEWS. It is discovered that, when the probability to miss an earthquake is fixed at 1%, the probability of false alarm is 0.8%, proving that EQN is a robust smartphone-based EEW system.
引用
收藏
页数:10
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