利用RAPIDS加速多属性无监督地震相分析
Accelerating Multi-attribute Unsupervised Seismic Facies Analysis With RAPIDS
摘要：
地震相分类是通过对地震数据样本进行属性聚类来完成的。年复一年，勘探地球物理学使用的3D数据集的大小、复杂性和属性数量都在增加，这就要求分类性能不断提高。在这项工作中，我们探讨了使用图形处理单元（GPU）进行地震勘探的分类，使用完善的机器学习（ML）方法k-均值。我们发现，RAPIDS库中提供的k-means算法的高性能分布式实现可用于对大型地震数据集中的相进行分类，其速度比经典的并行CPU实现快得多（在NVIDIA V100 GPU中快258倍），特别是对于大型地震块。我们使用不同的真实地震体测试了该算法，包括荷兰，Parihaka和Kahu（从12 GB到66 GB）。
英文摘要：
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).
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