Seismic labeled data expansion using variational autoencoders

Abstract

Supervised machine learning algorithms have been widely used in seismic exploration processing, but the lack of labeled examples complicates its application. Therefore, we propose a seismic labeled data expansion method based on deep variational Autoencoders (VAE), which are made of neural networks and contains two parts-Encoder and Decoder. Lack of training samples leads to overfitting of the network. We training the VAE with whole seismic data, which is a data-driven process and greatly alleviates the risk of overfitting. The Encoder captures the ability to map the seismic waveform $Y$ to latent deep features $z$, and the Decoder captures the ability to reconstruct high-dimensional waveform $\hat{Y}$ from latent deep features $z$. Later, we put the labeled seismic data into Encoders and get the latent deep features. We can easily use gaussian mixture model to fit the deep feature distribution of each class labeled data. We resample a mass of expansion deep features $z^{\ast }$ according to the Gaussian mixture model, and put the expansion deep features into the decoder to generate expansion seismic data. The experiments in synthetic and real data show that our method alleviates the problem of lacking labeled seismic data for supervised seismic facies analysis.