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Seismic facies recognition based on prestack data using deep convolutional autoencoder

Abstract · Apr 8, 2017 06:17 ·

seismic facies prestack recognition som trace map pca redundancy cs-cv

Arxiv Abstract

  • Feng Qian
  • Miao Yin
  • Ming-Jun Su
  • Yaojun Wang
  • Guangmin Hu

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.

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