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The rapid characterisation of earthquake parameters such as its magnitude isat the heart of Earthquake Early Warning (EEW). In traditional EEW methods therobustness in the estimation of earthquake parameters have been observed toincrease with the length of input data. Since time is a crucial factor in EEWapplications, in this paper we propose a deep learning based magnitudeclassifier and, further we investigate the effect of using five differentdurations of seismic waveform data after first P wave arrival of length 1s, 3s,10s, 20s and 30s. This is accomplished by testing the performance of theproposed model that combines Convolution and Bidirectional Long-Short TermMemory units to classify waveforms based on their magnitude into three classes"noise", "low magnitude events" and "high magnitude events". Herein, anyearthquake signal with magnitude equal to or above 5.0 is labelled as highmagnitude. We show that the variation in the results produced by changing thelength of the data, is no more than the inherent randomness in the trainedmodels, due to their initialisation.

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