P-Detector: Real-Time P-Wave Detection in a Seismic Waveform Recorded on a Low-Cost MEMS Accelerometer Using Deep Learning

Abstract

Recently, the Internet of Things (IoT) systems have been widely used for earthquake detection because of the easy construction of a dense seismic network, communication capabilities, and low cost of sensors. However, when utilizing MEMS sensors as seismic sensors, earthquake detection capabilities are often affected by various types of noises because such sensors are installed in heterogeneous environments. In earthquakes, P-waves first arrive, but their lengths are only a few seconds, and their amplitudes are also relatively smaller than to S-waves. As a result, it is difficult to accurately detect P-waves in IoT systems where environmental noises are always present. Furthermore, when using deep learning approaches for earthquake detection, inference time usually becomes a critical factor for real-time processing because of the complex architecture of a detection model. To that end, in this letter, we present a deep learning model that can detect P-waves in noisy environments. The model outputs the detection probability before the arrival of strong shakes. We tested our model on earthquakes recorded by the IoT-based seismic sensors deployed in South Korea. Our model can detect P-waves within 1.5–2.5 s after the first arrival of P-wave with the accuracy of 98.8%, making it applicable in real-time earthquake detection.