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Deep learning categorization of infrasound array data.

Jordan W Bishop1, Philip S Blom2, Jeremy Webster2

  • 1Wilson Alaska Technical Center, Geophysical Institute, University of Alaska Fairbanks, Fairbanks, Alaska 99709, USA.

The Journal of the Acoustical Society of America
|November 1, 2022
PubMed
Summary
This summary is machine-generated.

We developed a deep learning model for infrasound detection. This method accurately identifies signals-of-interest using beamforming features, outperforming raw data analysis.

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Area of Science:

  • Geophysics
  • Signal Processing
  • Machine Learning

Background:

  • Infrasound monitoring is crucial for detecting natural and anthropogenic events.
  • Traditional methods for infrasound signal detection and categorization can be challenging due to signal complexity and noise.
  • Automated analysis is needed to improve efficiency and reliability in processing large infrasound datasets.

Purpose of the Study:

  • To develop and evaluate a deep learning methodology for infrasound detection and categorization.
  • To improve the reliability of signal detection by utilizing features from beamforming analysis.
  • To assess the performance of the deep learning model in distinguishing various infrasound signal types.

Main Methods:

  • Convolutional neural networks (CNNs) with self-attention layers were employed for signal processing.
  • Features extracted from coherence and direction-of-arrival (DOA) from beamforming were used as model inputs.
  • An analyst-reviewed dataset from three International Monitoring System stations was utilized for training and validation.
  • Models were evaluated using k-fold cross-validation, comparison with state-of-the-art methods, and transportability analysis.

Main Results:

  • The deep learning model demonstrated high accuracy in detecting signals-of-interest (SOI), with f-scores consistently above 0.96 for noise vs. non-noise and SOI categorization.
  • The model showed improved signal detection capabilities compared to using raw infrasound waveform data.
  • Performance in distinguishing between short-duration stationary and non-stationary signals was mixed.
  • Transportability analysis indicated the model's potential for application across different infrasound arrays.

Conclusions:

  • Deep learning, particularly CNNs with self-attention, offers a highly accurate approach for infrasound signal categorization.
  • Feature extraction from beamforming significantly enhances the reliability of infrasound detection.
  • The developed methodology provides a robust tool for identifying signals-of-interest in complex infrasound data, supporting global monitoring efforts.