Infrasound signal classification represents a critical challenge that demands immediate attention. Feature extraction stands as the core concept for enhancing classification accuracy in infrasound signal processing. However, existing feature extraction methodologies fail to meet the requirements for long-distance detection scenarios. To address these limitations, this study proposes a novel classification framework based on the spatiotemporal characteristics of infrasound signals. The proposed framework incorporates advanced signal processing techniques, signal enhancement algorithms, and deep learning architectures to achieve precise classification of infrasound signals. This paper designs three sets of comparative experiments, and the results demonstrate that the proposed method achieves a classification accuracy rate of 83.9% on chemical explosion and seismic infrasound datasets, outperforming eight other comparative classification methods. This substantiates the efficacy of the proposed approach.

Infrasound signal classification remains a critical challenge in geophysical monitoring systems, where classification performance is fundamentally constrained by feature extraction efficacy. Existing two-dimensional feature extraction methods suffer from inadequate representation of spatiotemporal signal dynamics, leading to performance degradation in long-distance detection scenarios. To overcome these limitations, we present a novel classification framework that effectively captures spatiotemporal infrasound characteristics through Gramian Angular Field (GAF) transformation. The proposed method introduces an innovative encoding scheme that transforms one-dimensional infrasonic waveforms into two-dimensional GAF images while preserving crucial temporal dependencies. Building upon this representation, we develop an advanced hybrid deep learning architecture that integrates ConvLSTM networks to simultaneously extract and correlate spatial and spectral features. Extensive experimental validation on both chemical explosion and seismic infrasound datasets shows our approach achieves 92.4% classification accuracy, demonstrating consistent superiority over four state-of-the-art benchmark methods. These findings demonstrate the effectiveness of the proposed method.