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Seismic Discrimination between Earthquakes and Explosions Using Support Vector Machine.

Sangkyeum Kim¹, Kyunghyun Lee¹, Kwanho You¹

¹Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea.

Sensors (Basel, Switzerland)

|April 2, 2020

Summary

This summary is machine-generated.

This study introduces a novel seismic discrimination method using support vector machines (SVM) and a heterodyne laser interferometer. The approach effectively distinguishes between earthquakes and explosions by analyzing seismic wave amplitudes and compensating for errors.

Keywords:

Hough transform heterodyne laser interferometer measurement accuracy seismic discrimination support vector machine

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Area of Science:

Geophysics
Seismic Signal Analysis
Machine Learning Applications

Background:

Distinguishing seismic events like earthquakes from explosions is crucial for seismic signal analysis.
Traditional methods face challenges in accurately identifying event types due to signal complexities.

Purpose of the Study:

To propose and validate a robust seismic discrimination method.
To enhance seismic wave detection accuracy using advanced optical and computational techniques.

Main Methods:

Utilized Support Vector Machine (SVM) with P-wave and S-wave amplitudes as feature vectors.
Employed a heterodyne laser interferometer for precise seismic wave detection.
Applied the Hough transform to compensate for periodic nonlinearity errors in the laser interferometer.

Main Results:

SVM demonstrated effective discrimination between earthquakes and explosions using selected feature vectors.
The heterodyne laser interferometer and Hough transform method showed outstanding performance in precision seismic measurement and nonlinearity error compensation.
Experimental validation confirmed the effectiveness of the proposed method via receiver operating characteristic curves.

Conclusions:

The proposed seismic discrimination method, integrating SVM with a heterodyne laser interferometer and Hough transform, offers a reliable approach for event identification.
The study highlights the potential of advanced optical sensing and machine learning for improving seismic monitoring and analysis.