ConvEQ: Convolutional neural network for earthquake phase classification using short time frequency transform

Abstract

We present ConvEQ as a tool for discriminating seismic phases, leveraging artificial intelligence technique (Convolutional Neural Network) for short-time Frequency Transform of the seismic signal. Timely detection of the vertical (P) wave from an earthquake can generate a warning several tens of precious seconds before the more destructive waves strike. We propose a train-for-each-station approach for an Internet-of-Things-based Smart Earthquake Early Warning System, where lightweight neural networks trained for the seismic data belonging to each station are implemented on edge devices directly interfaced with seismometers. The approach has the potential to get the most from the sparse seismic network for Pakistan and other third-world countries. We train networks for multi-station and single-station data and achieve 96% and 99% accuracy, respectively, proving that train-for-each-station maximizes accuracy. The total processing time (including preprocessing and inference) is about 30ms for each event, thus suitable for real-time deployment. We further compare the performance of ConvEQ on simulated real-time data with several state-of-the-art contemporary algorithms. Our proposed approach demonstrates a robust response on diverse metrics. The ConvEQZ classifies the vertical seismic signal component with high accuracy and the ConvEQX can classify any seismic data component, inculcating robustness against connectivity issues.