A Deep-Learning Approach for Reducing the Probability of False Alarms in Smartphone-Based Earthquake Early Warning Systems

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 the need to control the probability of false alarm and the probability of missed detection. This chapter proposes a deep-learning approach to address this challenge. The methodology is tested using data coming from the Earthquake Network citizen science initiative, which implements a global smartphone-based EEWS.