Seismic facies recognition based on prestack data using deep convolutional autoencoder
This addresses the problem of identifying complex reservoirs in geophysics, but it is incremental as it adapts existing deep learning techniques to a specific domain.
The paper tackled seismic facies recognition from prestack data by framing it as an image clustering problem and using a convolutional autoencoder for feature extraction, showing superiority over conventional methods like PCA in redundancy removal and information extraction, with results applied to physical model and LZB region data.
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.