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Authors: Rincon-Yanez, Diego* 
De Lauro, Enza* 
Petrosino, Simona* 
Senatore, Sabrina* 
Falanga, Mariarosaria* 
Title: Identifying the Fingerprint of a Volcano in the Background Seismic Noise from Machine Learning-Based Approach
Journal: Applied Sciences 
Series/Report no.: /12 (2022)
Publisher: MDPI
Issue Date: 6-Jul-2022
DOI: 10.3390/app12146835
Keywords: seismic noise
Neapolitan volcanoes
Colima volcano
multi-layer perceptrons
convolutional neural network
Subject Classification04.08. Volcanology 
05.04. Instrumentation and techniques of general interest 
05.06. Methods 
Abstract: This work is devoted to the analysis of the background seismic noise acquired at the volcanoes (Campi Flegrei caldera, Ischia island, and Vesuvius) belonging to the Neapolitan volcanic district (Italy), and at the Colima volcano (Mexico). Continuous seismic acquisition is a complex mixture of volcanic transients and persistent volcanic and/or hydrothermal tremor, anthropogenic/ambient noise, oceanic loading, and meteo-marine contributions. The analysis of the background noise in a stationary volcanic phase could facilitate the identification of relevant waveforms often masked by microseisms and ambient noise. To address this issue, our approach proposes a machine learning (ML) modeling to recognize the “fingerprint” of a specific volcano by analyzing the background seismic noise from the continuous seismic acquisition. Specifically, two ML models, namely multi-layer perceptrons and convolutional neural network were trained to recognize one volcano from another based on the acquisition noise. Experimental results demonstrate the effectiveness of the two models in recognizing the noisy background signal, with promising performance in terms of accuracy, precision, recall, and F1 score. These results suggest that persistent volcanic signals share the same source information, as well as transient events, revealing a common generation mechanism but in different regimes. Moreover, assessing the dynamic state of a volcano through its background noise and promptly identifying any anomalies, which may indicate a change in its dynamics, can be a practical tool for real-time monitoring.
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