In seismic imaging, a long sought after goal has been either full or partial automation of the seismic image segmentation and interpretation processes. In this study, we present a novel supervised learning method for textural classification of seismic image patches, based on a topological tool called persistent homology. The basic workflow starts by taking an image and calculating its persistent homology, which gives us a list of birth-death pairs for different homology dimensions. Polynomial feature vectors are then extracted from these pairs, which are used to train three commonly used classifiers --- support vector machines, random forests, and neural networks, whose performances we compare. In addition, we experiment with different derived textural attributes and test the impact of using them instead of the raw images in the workflow. Our proposed method is tested on the publicly available LANDMASS datasets, which contains two sets of 2D seismic image patches grouped into four classes. The results indicate that persistent homology derived features can be quite powerful for automated textural segmentation of seismic images.


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