九分量环境噪声互关的高性能CPU-GPU异构计算方法

Jingxi Wang , Weitao Wang , Chao Wu , Lei Jiang , Hanwen Zou , Huajian Yao , Ling Chen
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引用次数: 0

摘要

环境噪声层析成像是地震学中的一项成熟技术,其中计算单分量或九分量噪声互相关函数(nfc)是基本的第一步。在这项研究中,我们引入了一种新的CPU-GPU异构计算框架,旨在显著提高从地震环境噪声数据中计算9分量nfc的效率。该框架不仅利用计算统一设备架构(CUDA)加速了计算过程,而且通过创新的叠加技术,如时频域相位加权叠加(tf-PWS),提高了信噪比(SNR)。我们使用多个数据集验证了该程序,确认了其优越的计算速度、改进的可靠性和nfc的更高信噪比。我们的综合研究为优化噪声互相关函数的计算过程提供了详细的见解,从而提高了环境噪声成像的精度和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-performance CPU-GPU heterogeneous computing method for 9-component ambient noise cross-correlation
Ambient noise tomography is an established technique in seismology, where calculating single- or nine-component noise cross-correlation functions (NCFs) is a fundamental first step. In this study, we introduced a novel CPU-GPU heterogeneous computing framework designed to significantly enhance the efficiency of computing 9-component NCFs from seismic ambient noise data. This framework not only accelerated the computational process by leveraging the Compute Unified Device Architecture (CUDA) but also improved the signal-to-noise ratio (SNR) through innovative stacking techniques, such as time-frequency domain phase-weighted stacking (tf-PWS). We validated the program using multiple datasets, confirming its superior computation speed, improved reliability, and higher signal-to-noise ratios for NCFs. Our comprehensive study provides detailed insights into optimizing the computational processes for noise cross-correlation functions, thereby enhancing the precision and efficiency of ambient noise imaging.
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