  Classification of seismic facies is done by clustering seismic data samples
based on their attributes. Year after year, 3D datasets used by exploration
geophysics increase in size, complexity, and number of attributes, requiring a
continuous rise in the classification performance. In this work, we explore the
use of Graphics Processing Units (GPUs) to perform the classification of
seismic surveys using the well-established Machine Learning (ML) method
k-means. We show that the high-performance distributed implementation of the
k-means algorithm available at the RAPIDS library can be used to classify
facies in large seismic datasets much faster than a classical parallel CPU
implementation (up to 258-fold faster in NVIDIA V100 GPUs), especially for
large seismic blocks. We tested the algorithm with different real seismic
volumes, including Netherlands, Parihaka, and Kahu (from 12GB to 66GB).
