Unsupervised Seismic Data Interpolation via Deep Convolutional Autoencoder

Abstract

Summary Seismic data interpolation is an essential prerequisite for multiple removal, imaging and seismic attributes analysis. We proposed a specific deep convolutional autoencoder (DCAE) for seismic data interpolation in an unsurpervised approach. DCAE is trained on undersampled seismic data with randomly 50% missing traces and only the reconstruction error on the uncurropted data is considered without generating labels on missing traces. Synthetic data and field data testing shows that the structure of DCEC is capble to capture of the low-level statics prior information from undersampled seismic data, and the tranied model has a good interpolation performance on regular and irrgular sampled seismic data.