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Statistical Picking of Multivariate Waveforms.

Nicoletta D'Angelo1, Giada Adelfio1,2, Marcello Chiodi1,2

  • 1Dipartimento di Scienze Economiche, Aziendali e Statistiche, Università degli Studi di Palermo, 90128 Palermo, Italy.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

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Testing for a General Changepoint in Medical and Psychometric Studies: Changes Detection and Sample Size Planning.

Statistics in medicine·2025
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This study introduces a new method using generalized linear regression to detect variance changes in multivariate data, aiding in simultaneous change point detection in functional time series. This approach effectively identifies seismic P- and S-wave arrival times, even in noisy data.

Area of Science:

  • Geophysics
  • Statistics
  • Signal Processing

Background:

  • Seismic data analysis requires accurate identification of P- and S-wave arrival times.
  • Existing methods may struggle with noisy waveforms or low-magnitude seismic events.
  • Multivariate functional time series analysis presents challenges in detecting simultaneous changes.

Purpose of the Study:

  • To develop a novel method for detecting change points in the variance of multivariate-covariance Gaussian variables.
  • To apply this method for simultaneous detection of change points in functional time series, specifically seismic waveforms.
  • To introduce a new algorithm for automatically picking P- and S-wave arrival times from seismograms.

Main Methods:

  • Fitting a generalized linear regression model to detect variance changes.
Keywords:
change pointschanges in variationcumulative segmentationseismic phase pickingseismogram

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  • Applying the method to multivariate waveforms for simultaneous change point detection.
  • Utilizing the approach as a picking algorithm for seismic P- and S-wave arrivals.

    • Main Results:

    • The proposed method successfully detects variance change points in piecewise constant variance functions.
    • Simultaneous detection of change points in multivariate functional time series was achieved.
    • The algorithm accurately picked P- and S-wave arrival times in simulated and real, noisy seismic data from low-magnitude events.

    Conclusions:

    • The generalized linear regression approach offers a robust method for change point detection in multivariate data.
    • The developed algorithm effectively identifies seismic wave arrival times, improving upon existing methods for noisy and low-magnitude events.
    • This work provides a valuable tool for seismological analysis and functional time series research.