Rapid earthquake detection through GPU-Based template matching

Dawei Mu, En Jui Lee, Po Chen

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)305-314
Number of pages10
JournalComputers and Geosciences
Volume109
DOIs
Publication statusPublished - 2017 Dec

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computers in Earth Sciences

Fingerprint Dive into the research topics of 'Rapid earthquake detection through GPU-Based template matching'. Together they form a unique fingerprint.

Cite this