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Earthquake is one of the major natural disasters, which could kill or injure thousands of people and cause huge property loss. According to the statistics, the number of earthquake events is about five million times per year and two thousands of them exceed magnitude 5. If we can win few seconds before the earthquake comes, it may save lots of lives and reduce economic losses. The earthquake early warning becomes an issue that cannot be ignored.
Earthquake early warning system (EEWS) needs rapid transmission of seismic information. Moreover, it requires accurate and fast algorithm to support the detection of earthquakes. In the past decades, progress has been made to invest the EEWS in countries where earthquake occurs frequently. For example, the United States of America, Canada, Japan and Taiwan have participated in doing the researches of EEWS. The earthquake warning detection methods such as: Artificial Neural Networks, Kruskal-wallis test, Fourier transform, Wavelet transform, Support Vector Machine are potential algorithms with high complexity. Nowadays, the EEWS is expected to use a large number of devices to form an earthquake detection network to increase the reliability. However, the algorithms with high computation complexity are not conducive to be implemented on general devices, such as smart phones, tablets or IoT-devices.
In this thesis, we aim to reduce the complexity of the seismic algorithm. To accomplish it, we use a large number of real earthquake events as the analysis samples to verify the algorithm and improve accuracy. | en_US |