In the early stage of oil and gas exploration, due to the lack of available drilling data, the automatic seismic facies classification technology mainly relies on the unsupervised clustering method combined with the seismic multiattribute. However, the clustering results are unstable and have no clear geologic significance. The supervised classification method based on manual interpretation can provide corresponding geologic significance, but there are still some problems such as the discrete classification results and low accuracy. To solve these problems, inspired by hyperspectral and spatial probability distribution technology, we have developed a classification framework called the probabilistic framework for seismic attributes and spatial classification (PFSSC). It can improve the continuity of the classification results by combining the classification probability and the spatial partial probability of the classifier output. In addition, the convolutional neural network (CNN) is a typical classification algorithm in deep learning. By convolution and pooling, we could use it to extract features of complex nonlinear objects for classification. Taking advantage of the combination of PFSSC and CNN, we could effectively solve the existing problems mentioned above in seismic facies classification. It is worth mentioning that we select seismic the multiattribute by maximal information coefficient (MIC) before the seismic facies classification. Finally, using the CNN-PFSSC and MIC methods, we can obtain high accuracy in the test set, reasonable continuity within the same seismic facies, clear boundaries between different seismic facies, and seismic facies classification results consistent with sedimentological laws.

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