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Earthquake pattern analysis using subsequence time series clustering.

Rahul Kumar Vijay1, Satyasai Jagannath Nanda2

  • 1Department of Computer Science, Banasthali Vidyapith, Tonk, Rajasthan 304022 India.

Pattern Analysis and Applications : PAA
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Summary
This summary is machine-generated.

This study introduces a novel algorithm to distinguish earthquake aftershocks from background seismic activity using time-series clustering. The method effectively identifies seismic patterns, improving earthquake catalog analysis.

Keywords:
Coefficient of Variation.Earthquake catalogsEarthquake time seriesHomogeneous Poisson processSubsequence clustering

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Area of Science:

  • Seismology
  • Data Science
  • Time-Series Analysis

Background:

  • Earthquake catalogs contain complex temporal patterns, including aftershocks and background seismic activity.
  • Distinguishing these event types is crucial for understanding seismicity and seismic hazard.
  • Existing methods may struggle to accurately differentiate between aftershock sequences and background events.

Purpose of the Study:

  • To develop and validate a subsequence time-series clustering algorithm for identifying strongly coupled aftershock sequences and Poissonian background activity.
  • To characterize the temporal nature of earthquake sequences using inter-event time statistics, epicenter, and magnitude information.
  • To categorize long earthquake time series into meaningful temporal subsequences for clustering.

Main Methods:

  • A two-phase approach involving Gaussian kernel-based density estimation for optimal subsequence identification.
  • Inter-event time and distance-based analysis of subsequences to detect highly correlated aftershock sequences (hot-spots).
  • Application of a sliding temporal window with earthquake magnitude to filter time-correlated events and isolate Poissonian subsequences.

Main Results:

  • The proposed algorithm successfully identifies meaningful subsequences (background events) that can be modeled by a homogeneous Poisson process.
  • Achieved linear cumulative rate and time-independent inter-event time distributions for background events.
  • Demonstrated competitive performance compared to state-of-the-art and recently introduced methods on Sumatra-Andaman and ISC-GEM earthquake catalogs.

Conclusions:

  • The developed subsequence clustering algorithm provides an effective means to differentiate earthquake aftershocks from background seismic activity.
  • The method enhances the analysis of earthquake catalogs by accurately modeling background seismicity as a Poisson process.
  • This approach offers a robust tool for seismological research and seismic hazard assessment.