Adaptive Gaussian Mixture Model and Convolution Autoencoder Clustering for Unsupervised Seismic Waveform Analysis

Seismic facies analysis can effectively estimate reservoir properties and seismic waveform clustering is a useful tool for facies analysis. We developed a deep learning-based clustering approach called the modified deep convolutional embedded clustering with adaptive Gaussian mixture model (AGMM-MDCEC) for seismic waveform clustering. Trainable feature extraction and clustering layers in AGMM-MDCEC are implemented using neural networks. The two independent processes of feature extraction and clustering are fused, such that extracted features are modified simultaneously with the results of clustering. A convolutional autoencoder is used in the algorithm for extracting features from seismic data and reduce data redundancy. At the same time, weights of clustering network are fined-tuned through iteration to obtain state-of-the-art clustering results. We apply our new classification algorithm to a data volume acquired in western China to map architectural elements of a complex fluvial depositional system. Our proposed method obtains superior results over those provided by traditional K-means, Gaussian mixture model, and some machine learning methods, and improves the mapping of the extent of the distributary system.