Real Time Magnitude Classification of Earthquake Waveforms using Deep Learning

This study attempts to use Deep Learning architectures to design an efficient real time magnitude classifier for seismic events. Various combinations of Convolutional Neural Networks (CNNs) and Bi- & Uni-directional Long-Short Term Memory (LSTMs) and Gated Recurrent Unit (GRUs) are tried and tested to obtain the best performing model with optimum hyperparameters. In order to extract maximum information from the seismic waveforms, this study uses not only the time series data but also its corresponding Fourier Transform (spectrogram) as input. Furthermore, the Deep Learning architecture is combined with other machine learning algorithms to generate the final magnitude classifications. This study is likely to help seismologists in improving the Earthquake Early Warning System to avoid issuing false warnings, which not only alarms people unnecessarily but can also result in huge financial losses due to stoppage of industrial machinery etc.