Nonstationary training image partition algorithm based on deep features

Abstract

Training image (TI) is a key input of multipoint geostatistical modeling. For modeling sedimentary facies under nonstationary conditions, it is common to first generate nonstationary TIs, then use a partitioned simulation approach, and finally merge the realizations of each subregion. We develop a new method for partitioning nonstationary TIs based on features extracted using a deep network model. The basic idea of the method is to crop a TI with a sliding window to obtain the subblocks of the TI and use the pretrained convolutional neural network model as a fixed feature extractor for the subblocks. We use K-means to cluster the extracted deep features and t-distributed stochastic neighbor embedding to visualize the clustering effect and assign the classification information of all feature points to the subblocks of the TI as its subregion markers. Finally, we stitch the subblocks of the marked TIs by position to obtain the partitioning results of the nonstationary TIs. Experimental results indicate that the classification accuracy of the method reaches 90.53%, and the partition effect is relatively good. Research indicates that the method can reproduce well the spatial variation characteristics of nonstationary TIs and provide a new method for processing the multipoint geostatistical nonstationarity.