  Prestack seismic data carries much useful information that can help us find
more complex atypical reservoirs. Therefore, we are increasingly inclined to
use prestack seismic data for seis- mic facies recognition. However, due to the
inclusion of ex- cessive redundancy, effective feature extraction from prestack
seismic data becomes critical. In this paper, we consider seis- mic facies
recognition based on prestack data as an image clus- tering problem in computer
vision (CV) by thinking of each prestack seismic gather as a picture. We
propose a convo- lutional autoencoder (CAE) network for deep feature learn- ing
from prestack seismic data, which is more effective than principal component
analysis (PCA) in redundancy removing and valid information extraction. Then,
using conventional classification or clustering techniques (e.g. K-means or
self- organizing maps) on the extracted features, we can achieve seismic facies
recognition. We applied our method to the prestack data from physical model and
LZB region. The re- sult shows that our approach is superior to the
conventionals.
