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A Deep Learning Approach for Distant Infrasound Signals Classification.

Xiaofeng Tan¹, Xihai Li¹, Hongru Li¹

¹School of nuclear Engineering, Rocket Force University of Engneering, Xi'an 710025, China.

Sensors (Basel, Switzerland)

|April 12, 2025

Summary

This summary is machine-generated.

This study introduces a new infrasound signal classification method using spatiotemporal features. The novel approach achieves 83.9% accuracy, improving long-distance detection capabilities.

Keywords:

CNN deep learning infrasound signal classification noise reduction station signal combination

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Area of Science:

Geophysics
Signal Processing
Machine Learning

Background:

Infrasound signal classification is crucial but challenging, especially for long-distance detection.
Current feature extraction methods are insufficient for these demanding scenarios.

Purpose of the Study:

To develop a novel infrasound signal classification framework utilizing spatiotemporal characteristics.
To enhance the accuracy and effectiveness of infrasound detection for long-distance events.

Main Methods:

Proposed a new classification framework integrating advanced signal processing and deep learning.
Employed spatiotemporal features for enhanced infrasound signal characterization.
Conducted comparative experiments against eight other classification methods.

Main Results:

The proposed method achieved an 83.9% classification accuracy on chemical explosion and seismic infrasound datasets.
Demonstrated superior performance compared to existing classification techniques.
Validated the framework's efficacy in real-world detection scenarios.

Conclusions:

The novel spatiotemporal feature-based classification framework significantly improves infrasound signal classification accuracy.
The approach addresses limitations of current methods in long-distance detection.
This work offers a promising solution for critical infrasound monitoring applications.