A study on the effect of input data length on deep learning-based magnitude classifier
- 1Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany
- 2Institute for Theoretical Physics, Goethe Universität, 60438 Frankfurt am Main, Germany
- 3Xidian-FIAS international Joint Research Center, Giersch Science Center, D-60438 Frankfurt am Main, Germany
- 4Institute of Geosciences, Goethe-University Frankfurt, 60438 Frankfurt am Main, Germany
- 5GSI Helmholtzzentrum für Schwerionenforschung GmbH, 64291 Darmstadt, Germany
Abstract. The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Earth-quake Early Warning (EEW). In traditional EEW methods the robustness in the estimation of earthquake parameters have 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 and, further we investigate the effect of using five different durations of seismic waveform data after first P-wave arrival– 1s, 3s, 10s, 20s and 30s. 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.
Journal article(s) based on this preprint
Megha Chakraborty et al.