Abstract: Ambient noise seismology uses the ambient noise signals recorded by seismic stations to calculate the cross-correlation between stations, and thereby derive information about geological structures. In recent years, it has been widely used in fields such as Earth structure and oil and gas exploration. Seismic noise data processing often requires preprocessing calculations to reduce interference from instruments and seismic signals, which involves various signal processing calculations. As seismic stations are increasingly deployed in China, the continuous accumulation of seismic waveform files has greatly increased the time required for preprocessing calculations. To address the issue of computational time, a parallel seismic noise preprocessing algorithm has been proposed based on a graphics processing unit (GPU) heterogeneous computing platform. The parallel algorithm designed a parallel computing framework in three dimensions: stations, time, and segments. It implemented computational kernel functions for the calculation process in preprocessing and achieved adaptive processing of large batches of files through batch calculations. Experimental results show that the parallel preprocessing algorithm achieved an acceleration ratio of about 95 times, with good acceleration ratio and parallelism. 

Key words: ambient noise seismology, data preprocessing, parallel computing, heterogeneous computing, graphics processing unit (GPU)