Influence of low-cost sensors on earthquake early warning magnitude estimation using convolutional neural network model

Abstract:

To explore the impact of low-cost sensors on the earthquake early warning (EEW) magnitude estimation of convolutional neural network (CNN) model, taking five destructive earthquakes (MS≥5.8) that occurred in China in 2022 as examples, seismic data was applied to the CNN model, and the magnitude estimation results after incorporating the data recorded by low-cost sensors were analyzed. The results show that within 3 s after the P-wave arrival, based on single station, the magnitude estimation error of the low-cost sensors and the strong-motion instruments is mainly distributed in the range of ±1 magnitude unit. For the seismic records with epicentral distance less than 100 km, within 10 s after the P wave arrival, the mean value of magnitude estimation error of the low-cost sensor is closer to 0 than that of the strong-motion instrument. For the seismic records with signal noise ratio less than 20, the mean value of magnitude estimation error of strong-motion instrument is closer to 0 than that of low-cost sensor, and the low-cost sensor has greater uncertainty of magnitude estimation error. Additionally, for these 5 earthquakes, compared with the strong-motion instrument, the low-cost sensor has a larger quantity and denser distribution. the CNN model obtains robust magnitude estimation faster when considering the data recorded by low-cost sensors. The results provide a basis for the applicability of low-cost sensors in CNN magnitude estimation models, and serve as a reference for magnitude estimation in EEW systems.