Variational Autoencoders for P-Wave Detection in Strong Motion Earthquake Records

Abstract

Earthquake early warning systems rely on accurate detection of Primary waves before the destructive Secondary waves arrive. However, identifying P-wave onsets in strongmotion accelerograms is challenging due to high noise, limited labeled data, and complex waveforms. This paper proposes a Variational Autoencoder framework for self-supervised P-wave detection in strong-motion data. A Convolutional VAE is trained to reconstruct P-wave segments while rejecting noise and non-P-wave inputs. We employ a sliding window method, combining reconstruction loss and normalized cross-correlation, to locate P-wave arrivals. Experimental results on 1, 2, and 3 second segments show robust performance with area-under-the-curve up to 0.97, demonstrating improved accuracy for longer segments and reduced computational cost for shorter segments.