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Earthquake Detection in a Static and Dynamic Environment Using Supervised Machine Learning and a Novel Feature

Irshad Khan¹, Seonhwa Choi², Young-Woo Kwon¹

¹School of Computer Science, Kyungpook National University, Daegu 41566, Korea.

Sensors (Basel, Switzerland)

|February 7, 2020

Summary

This study introduces a machine learning approach for real-time earthquake detection using smartphones. The model effectively distinguishes seismic events from noise, reducing false alarms in static environments.

Keywords:

Earthquake detection Internet of Things machine learning time-series features

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Area of Science:

Geophysics
Computer Science
Machine Learning

Background:

Real-time earthquake detection using smartphones and IoT devices is challenging due to signal similarity and environmental noise.
Traditional seismic methods struggle with the complexities introduced by human activities on mobile devices.

Purpose of the Study:

To develop and evaluate a machine learning model for accurate, real-time earthquake detection using smartphone sensors.
To differentiate between earthquake signals and non-earthquake signals (noise) in both static and dynamic environments.

Main Methods:

Leveraged machine learning techniques focusing on earthquake-specific features, moving beyond traditional seismic analysis.
Categorized the detection task into static and dynamic environments, evaluating various features and models.
Proposed and experimentally validated a machine learning model optimized for static environments to minimize false alarms.

Main Results:

The proposed machine learning model demonstrated high accuracy in detecting earthquakes in the static environment.
Experimental results showed promising generalization capabilities on unseen data, indicating model robustness.
The study confirmed the model's potential applicability in dynamic environments with appropriate dataset training.

Conclusions:

Machine learning offers a viable alternative to traditional methods for smartphone-based earthquake detection.
The developed model effectively reduces false alarms and enhances detection accuracy, particularly in static settings.
Further research and training are needed to adapt the model for reliable performance in dynamic environments.