  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.
