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Earthquake multi-classification detection based velocity and displacement data filtering using machine learning

Muhammad Ary Murti1, Rio Junior1, Ali Najah Ahmed2

  • 1Telkom University, Bandung, 40257, Indonesia.

Scientific Reports
|December 9, 2022
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Summary

This study developed a machine learning model for accurate earthquake detection, distinguishing seismic events from non-earthquake and vandalism vibrations. The Artificial Neural Network (ANN) proved most effective, enhanced by velocity and displacement data.

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Area of Science:

  • Geophysics
  • Machine Learning
  • Seismology

Background:

  • Earthquakes pose significant societal risks, necessitating reliable detection methods.
  • Current seismic sensors often detect non-earthquake vibrations, leading to false positives.
  • Distinguishing between seismic and non-seismic vibrations is crucial for accurate monitoring.

Purpose of the Study:

  • To propose a machine learning approach for multi-classification earthquake detection.
  • To differentiate seismic vibrations from non-seismic (e.g., vandalism) vibrations.
  • To enhance detection performance by incorporating derived seismic features.

Main Methods:

  • Utilized acceleration seismic waves as primary input data.
  • Incorporated velocity and displacement as additional features, derived from acceleration.
  • Evaluated multiple machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN).

Main Results:

  • The Artificial Neural Network (ANN) demonstrated superior performance in classifying earthquake, non-earthquake, and vandalism vibrations.
  • ANN exhibited greater robustness across different input features.
  • The inclusion of velocity and displacement data significantly improved the performance of all tested machine learning models.

Conclusions:

  • Machine learning, particularly ANN, offers a promising solution for accurate multi-classification seismic event detection.
  • Integrating derived features like velocity and displacement enhances the reliability of earthquake detection systems.
  • The developed method effectively distinguishes genuine seismic activity from other vibrational sources.