SeismoGen: Seismic Waveform Synthesis Using Generative Adversarial Networks

TL;DR

This paper introduces SeismoGen, a GAN-based model that synthesizes seismic waveforms to augment training data, significantly enhancing earthquake detection accuracy with limited labeled data.

Contribution

The paper presents a novel GAN architecture for seismic waveform synthesis and demonstrates its effectiveness in improving earthquake detection performance.

Findings

Synthetic data improves detection accuracy

GAN-generated seismic samples are high quality

Data augmentation enhances model robustness

Abstract

Detecting earthquake events from seismic time series has proved itself a challenging task. Manual detection can be expensive and tedious due to the intensive labor and large scale data set. In recent years, automatic detection methods based on machine learning have been developed to improve accuracy and efficiency. However, the accuracy of those methods relies on a sufficient amount of high-quality training data, which itself can be expensive to obtain due to the requirement of domain knowledge and subject matter expertise. This paper is to resolve this dilemma by answering two questions: (1) provided with a limited number of reliable labels, can we use them to generate more synthetic labels; (2) Can we use those synthetic labels to improve the detectability? Among all the existing generative models, the generative adversarial network (GAN) shows its supreme capability in generating high-quality synthetic samples in multiple domains. We designed our model based on GAN. In particular, we studied several different network structures. By comparing the generated results, our GAN-based generative model yields the highest quality. We further combine the dataset with synthetic samples generated by our generative model and show that the detectability of our earthquake classification model is significantly improved than the one trained without augmenting the training set.