Seismic Signal Synthesis by Generative Adversarial Network with Gated Convolutional Neural Network Structure

Detecting earthquake events from seismic time series signal is a challenging task. Recently, detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, accuracy of those methods rely on sufficient amount of high-quality training data. In many situations, the high-quality data is difficulty to obtain. We address and resolve this issue by using a Generative Adversarial Network (GAN) model for seismic signal synthesis. GAN already shows its powerful capability in generating high quality synthetic samples in multiple domains. In this paper, we propose a GAN model with gated CNN which can excellently capture sequential structure of seismic time series. We demonstrate its effectiveness via earthquake classification performance. The results show the synthetic data generated by our model indeed can improve the classification performance over the one trained with only real samples.