A-MAD: An Adaptive-Microseismic Activity Detector Based on Gaussian Mixture Models and Spectral Subtraction

• Published in: IEEE access 2023, Vol.11, p.141289-141298
• Main Authors: Rosero, Karen, Parra, Carla, Grijalva, Felipe, Larco, Julio Cesar, Lara-Cueva, Roman, Garzon, Nathaly Orozco
• Format: Article
• Language: English
• Subjects: Automation; Background noise; Detectors; Early warning systems; Event detection; Gaussian mixture models; Hidden Markov models; Microseisms; Monitoring; Noise levels; Probabilistic models; Real-time systems; Seismic activity; Signal to noise ratio; Speech processing; Voice activity detectors; Voice recognition; Volcanic activity; Volcanic eruptions; Volcanic seismic events; volcano monitoring; Volcanoes
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3342046

Continuous monitoring of active volcanoes is essential for understanding their behavior and providing accurate forecasts and warnings. Automating the detection of volcano microseismic events plays a crucial role in large-scale analysis, early warning systems, and efficient handling of large-scale data. This paper presents a novel approach for the automated detection of microseismic events associated with volcanic activity, focusing on the Cotopaxi volcano in Ecuador. Inspired by voice activity detection (VAD) systems used in speech processing, we propose an Adaptive-Microseismic Activity Detector (A-MAD) that modifies VAD techniques to identify frames within seismic signals containing volcanic activity. The A-MAD system incorporates a spectral subtraction stage to mitigate the impact of environmental noise and employs Gaussian Mixture Models (GMMs) to model the probabilistic distribution of microseismic events. Mel Frequency Cepstral Coefficients (MFCCs) are used as features to describe the seismic signals, enabling adaptability for varying noise levels. Experimental results on signals from Cotopaxi Volcano in Ecuador demonstrate the effectiveness of the A-MAD system, achieving high accuracy (96.39% for discrete events and 98.45% for continuous signals) while meeting the efficiency requirements of volcano monitoring institutions. This work contributes to the advancement of early warning systems for volcanic eruptions, providing a robust and adaptive approach for microseismic activity detection.