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

TL;DR

This paper introduces a deep learning model for earthquake magnitude classification and investigates how different waveform data lengths affect its performance, finding minimal variation across durations.

Contribution

It proposes a novel deep learning model combining CNN and BiLSTM for earthquake magnitude classification and analyzes the impact of data length on model accuracy.

Findings

Model performance is stable across different data lengths.

Variation due to data length is comparable to model randomness.

Deep learning can effectively classify earthquake magnitudes with varying input durations.

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 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 of length 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.