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To develop a machine learning method for accurately classifying different types of natural disasters using infrasound signals.

Machine learning-driven classification of natural disasters via parallel confidence fusion

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To develop a machine learning method for accurately classifying different types of natural disasters using infrasound signals.
Utilized a synergistic feature-deep learning architecture for enhanced feature representation.
Developed a triple-branch architecture for modeling propagation dynamics in infrasound signals.
Implemented a parallel confidence fusion module to combine outputs from different branches.
Achieved 98.75% accuracy on a natural earthquake infrasound dataset.
Showed robust performance on an open-source multi-class dataset.
Demonstrated improved fault tolerance to anomalies compared to traditional methods.

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Novel machine learning approach classifies natural disasters using infrasound, indicating enhanced detection methods.

Li et al. (2026) studied this question.

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