Clustering Acoustic Background Noise in the Stratosphere Using Machine Learning
- Univ. of Texas at El Paso, TX (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Infrasound, characterized by low-frequency sound inaudible to humans (<20 Hz), emanates from natural and anthropogenic sources. Its efficacy for monitoring phenomena necessitates robust sensing networks. Traditional ground-based infrasound sensors have limitations due to atmospheric dynamics and noise interference. Balloon-bore sensors have emerged as an alternative, offering reduced noise and improved capabilities. This study bridges clustering algorithms with balloon borne infrasound data, a domain yet to be explored. Employing K-Means, DBSCAN, and GMM algorithms on normalized and reshaped data and only normalized data from a New Zealand-based NASA balloon flight, insights into background noise at stratospheric altitudes were revealed. Despite challenges arising from distinguishing signals amid unique background noise, this research provides vital reference material for noise analysis and calibration. Beyond infrasound event capture, the dataset enriches comprehension of background noise characteristics in the southern hemisphere.
- Research Organization:
- Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA), Office of Defense Programs (DP); Defense Threat Reduction Agency (DTRA)
- DOE Contract Number:
- NA0003525
- OSTI ID:
- 2430354
- Report Number(s):
- SAND--2023-11183
- Country of Publication:
- United States
- Language:
- English