论文标题
与急流一起加速多属性无监督的地震相分析
Accelerating Multi-attribute Unsupervised Seismic Facies Analysis With RAPIDS
论文作者
论文摘要
地震相的分类是通过基于其属性聚类的地震数据样本来完成的。年复一年，探索地球物理学使用的3D数据集的大小，复杂性和属性数量增加，需要分类性能不断提高。在这项工作中，我们探讨了使用图形处理单元（GPU）使用良好的机器学习（ML）方法K均值进行地震调查的分类。我们表明，Rapids库中可用的K-Means算法的高性能分布式实现可用于将大型地震数据集中的相分类得比经典的平行CPU实现快得多（在NVIDIA V100 v100 GPU中最多258倍），尤其是用于大型地震块。我们用不同的真实地震量测试了算法，包括荷兰，Parihaka和Kahu（从12GB到66GB）。
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).
