Ambient noise tomography is an established technique in seismology,where calculating single-or ninecomponent noise cross-correlation functions(NCFs)is a fundamental first step.In this study,we introduced a novel CPU-G...Ambient noise tomography is an established technique in seismology,where calculating single-or ninecomponent 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 phaseweighted 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.展开更多
基金supported by the Key Research and Development Program of China(2021YFC3000704)Institute of Geophysics,China Earthquake Administration Grant DQJB23R18+1 种基金the USTC Research Funds of the Double First-Class Initiative(YD2080002012)NSFC Grant(U2239206)。
文摘Ambient noise tomography is an established technique in seismology,where calculating single-or ninecomponent 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 phaseweighted 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.
