Accelerating Multi-attribute Unsupervised Seismic Facies Analysis With RAPIDS

TL;DR

This paper demonstrates that GPU-accelerated k-means clustering using RAPIDS significantly speeds up seismic facies classification in large 3D datasets, enabling faster analysis for exploration geophysics.

Contribution

It introduces a GPU-based implementation of k-means clustering for seismic data, achieving substantial speedups over traditional CPU methods.

Findings

Up to 258-fold faster classification with GPUs.

Effective handling of large seismic datasets (12GB to 66GB).

Validated on multiple real seismic volumes.

Abstract

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).