On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes
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EXPRESS LETTER
On data reduction methods for volcanic tremor characterization: the 2012 eruption of Copahue volcano, Southern Andes Ivan Melchor1* , Javier Almendros2, Roberto Carniel3, Kostas I. Konstantinou4, Marcia Hantusch1 and Alberto Caselli1
Abstract Improving the ability to detect and characterize long-duration volcanic tremor is crucial to understand the longterm dynamics and unrest of volcanic systems. We have applied data reduction methods (permutation entropy and polarization degree, among others) to characterize the seismic wave field near Copahue volcano (Southern Andes) between June 2012 and January 2013, when phreatomagmatic episodes occurred. During the selected period, a total of 52 long-duration events with energy above the background occurred. Among them, 32 were classified as volcanic tremors and the remaining as noise bursts. Characterizing each event by averaging its reduced parameters, allowed us to study the range of variability of the different events types. We found that, compared to noise burst, tremors have lower permutation entropies and higher dominant polarization degrees. This characterization is a suitable tool for detecting long-duration volcanic tremors in the ambient seismic wave field, even if the SNR is low. Keywords: Volcanic tremor, Phreatic eruption, Data reduction method, Polarization degree, Permutation entropy, Copahue volcano Introduction Active volcanic environments are prone to generate long-period sustained seismic signals, known as volcanic tremors. Due to their link with hydrothermal-magmatic systems (e.g., Ripepe and Gordeev 1999; Fujita 2008; Girona et al. 2019; Jolly et al. 2020), their detection and characterization is a key practice for volcano monitoring (Ogiso et al. 2015) and their analysis can, for example, provide insights into the dynamics of phreatic activity (Yukutake et al. 2017). The most common procedure for detecting volcanic tremors consists of a visual inspection of the seismogram and spectrogram of the component showing the highest signal-to-noise ratio which, usually, is the vertical. Then, volcanic tremor is generally classified according to the *Correspondence: [email protected] 1 Instituto de Investigación en Paleobiología y Geología, Universidad Nacional de Río Negro–CONICET, General Roca, Río Negro, Argentina Full list of author information is available at the end of the article
spectral signature between 0.5 and 10 Hz (Konstantinou and Schlindwein 2003) as monochromatic if it shows a narrow peak, harmonic if, besides, it shows overtones, and broadband-type, if it shows multiple uncorrelated peaks. The duration of the mechanism that generates the tremor source, which can last from minutes to days or years (McNutt and Nishimura 2008), and the lack of clear onset and end, has led volcanic tremor to be interpreted as a seismic signal generated by a continuous process that can be identified in the seismograms only when it exceeds the background seismic energy level (Carniel 2010). Another approach to detect volcanic tre
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