Rapid earthquake detection through GPU-Based template matching. (December 2017) - Mu, Dawei; Lee, En-Jui; Chen, Po
Abstract: The template-matching algorithm (TMA) has been widely adopted for improving the reliability of earthquake detection. The TMA is based on calculating the normalized cross-correlation coefficient (NCC) between a collection of selected template waveforms and the continuous waveform recordings of seismic instruments. In realistic applications, the computational cost of the TMA is much higher than that of traditional techniques. In this study, we provide an analysis of the TMA and show how the GPU architecture provides an almost ideal environment for accelerating the TMA and NCC-based pattern recognition algorithms in general. So far, our best-performing GPU code has achieved a speedup factor of more than 800 with respect to a common sequential CPU code. We demonstrate the performance of our GPU code using seismic waveform recordings from the ML 6.6 Meinong earthquake sequence in Taiwan. Highlights: We have developed efficient GPU code for normalized cross-correlation coefficient (NCC) calculations. Our current GPU code has achieved more than 800 times speedup with respect to a sequential CPU code. Our GPU-based NCC code has been applied to the template-matching algorithm (TMA) for earthquake detection.