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A matched-filter technique with an objective threshold.

Shiro Hirano1, Hironori Kawakata2, Issei Doi3

  • 1Department of Physical Science, College of Science and Engineering, Ritsumeikan University, 1-1-1, Nojihigashi, Kusatsu, Shiga, 525-8577, Japan. s-hrn@fc.ritsumei.ac.jp.

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Summary

This study introduces an objective method for seismic signal detection using cross-correlation coefficients. It leverages Akaike's Information Criterion (AIC) and extreme value statistics for reliable outlier identification in continuous waveform data.

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Area of Science:

  • Geophysics and Seismology
  • Signal Processing
  • Statistical Analysis

Background:

  • Detecting seismic signals in continuous waveform records is crucial for seismological studies.
  • Traditional methods often rely on subjective thresholding, leading to potential inaccuracies.
  • Objective and automated approaches are needed for robust seismic event identification.

Purpose of the Study:

  • To develop an objective method for determining thresholds in cross-correlation coefficients for seismic signal detection.
  • To automate the detectability assessment using Akaike's Information Criterion (AIC).
  • To validate the method's efficacy on extensive continuous seismic waveform data.

Main Methods:

  • Empirical distribution analysis of cross-correlation coefficients among seismic waveforms.
  • Objective threshold determination guided by Akaike's Information Criterion (AIC).
  • Application of extreme value statistics to model the distribution of maximum cross-correlation coefficients.

Main Results:

  • The proposed method successfully detected seismic signals from two years of continuous waveform records.
  • Maximum network cross-correlation coefficients were found to follow extreme value statistics.
  • A parametric probability density function of maxima was derived, enabling a reasonable outlier criterion.

Conclusions:

  • The developed objective thresholding method provides a robust framework for seismic signal detection.
  • Integration of AIC and extreme value statistics enhances the reliability of outlier identification.
  • This approach offers an automated and data-driven solution for analyzing continuous seismic data.