Dynamic Time Warping as an Alternative to Windowed Cross Correlation in Seismological Applications

Author:

Kumar Utpal1ORCID,Legendre Cédric. P.2ORCID,Zhao Li34ORCID,Chao Ben F.5

Affiliation:

1. Berkeley Seismological Laboratory, University of California, Berkeley, California, U.S.A.

2. Institute of Geophysics, Czech Academy of Science, Czech Republic

3. School of Earth and Space Sciences, Peking University, Beijing, China

4. Hebei Hongshan Geophysical National Observation and Research Station, Peking University, Beijing, China

5. Institute of Earth Sciences, Academia Sinica, Taipei City, Taiwan

Abstract

Abstract We investigate the feasibility of using the dynamic time warping (DTW) technique as an alternative to windowed cross correlation (WCC) for an indirect measure to quantify both the similarity and relationship between two seismic time series. We first examine the sensitivity and performance of the DTW technique by analyzing both synthetic and real seismic time series in geophysical applications. Results show that DTW efficiently retrieves useful information from seismic data and has a high sensitivity to minor variations in time series that WCC fails to detect. We further propose four potential applications of DTW to routine seismic data interpretation—earthquake detection, template matching, clustering of waveforms, and full-waveform inversion for 1D velocity models. The earthquake detection scheme employing DTW is proposed in this study as an alternative to traditional methods. The accuracy of DTW in estimating similarity is explored for template matching and clustering of seismic traces. Finally, we discuss a realistic example of 1D Earth velocity model inversion using DTW and explore its feasibility in full-waveform inversion.

Publisher

Seismological Society of America (SSA)

Subject

Geophysics

Reference101 articles.

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3. The great time series classification bake off: An experimental evaluation of recently proposed algorithms. Extended Version;Bagnall,2016

4. Earthquake cluster: What can we learn from waveform similarity?;Baisch;Bull. Seismol. Soc. Am.,2008

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