A study on the effect of input data length on a deep-learning-based magnitude classifier

Abstract

Abstract. The rapid characterisation of earthquake parameters such as its magnitude is at the heart of earthquake early warning (EEW). In traditional EEW methods, the robustness in the estimation of earthquake parameters has been observed to increase with the length of input data. Since time is a crucial factor in EEW applications, in this paper we propose a deep-learning-based magnitude classifier based on data from a single seismic station and further investigate the effect of using five different durations of seismic waveform data after first P-wave arrival: 1, 3, 10, 20 and 30 s. This is accomplished by testing the performance of the proposed model that combines convolution and bidirectional long short-term memory units to classify waveforms based on their magnitude into three classes: “noise”, “low-magnitude events” and “high-magnitude events”. Herein, any earthquake signal with magnitude equal to or above 5.0 is labelled as “high-magnitude”. We show that the variation in the results produced by changing the length of the data is no more than the inherent randomness in the trained models due to their initialisation. We further demonstrate that the model is able to successfully classify waveforms over wide ranges of both hypocentral distance and signal-to-noise ratio.