Smartphones equipped with motion sensors can be manipulated as a Community Seismic Network for earthquake detection. But, there still have many challenges such as the limited sensing capability of the off-the-shelf sensors and unpredictable diversity of daily operations by phone users in current smartphones, which yield poor monitoring quality. So we present a suite of algorithms towards detecting anomalous seismic events from sampling data contaminated by users operations, including a lightweight signal preprocessing method, a two-phase events picking and timing scheme on local smartphones, and a decision fusion scheme to maximize anomaly detection performance at the fusion center while meeting the requirements on system false alarm rate. We experimentally evaluate the proposed approach on networked smartphones and shake tables. The results verify the effectiveness of our approach in distinguishing anomalous seismic events from noises due to normal daily operation.
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