Volcanic gas emissions are intimately linked to the dynamics of magma ascent and outgassing and, on geological time scales, constitute an important source of volatiles to the Earth's atmosphere. Measurements of gas composition and flux are therefore critical to both volcano monitoring and to determining the contribution of volcanoes to global geochemical cycles. However, significant gaps remain in our global inventories of volcanic emissions, (particularly for CO 2 , which requires proximal sampling of a concentrated plume) for those volcanoes where the near-vent region is hazardous or inaccessible. Unmanned Aerial Systems (UAS) provide a robust and effective solution to proximal sampling of dense volcanic plumes in extreme volcanic environments. Here we present gas compositional data acquired using a gas sensor payload aboard a UAS flown at Volcán Villarrica, Chile. We compare UAS-derived gas time series to simultaneous crater rim multi-GAS data and UV camera imagery to investigate early plume evolution. SO 2 concentrations measured in the young proximal plume exhibit periodic variations that are well correlated with the concentrations of other species. By combining molar gas ratios (CO 2 /SO 2 = 1.48-1.68, H 2 O/SO 2 = 67-75, and H 2 O/CO 2 = 45-51) with the SO 2 flux (142 ± 17 t/day) from UV camera images, we derive CO 2 and H 2 O fluxes of~150 t/day and~2,850 t/day, respectively. We observe good agreement between time-averaged molar gas ratios obtained from simultaneous UAS-and ground-based multi-GAS acquisitions. However, the UAS measurements made in the young, less diluted plume reveal additional short-term periodic structure that reflects active degassing through discrete, audible gas exhalations.

In this work, the applicability of the circular statistics to feature extraction on seismic signals is presented. The seismic signals are captured from Llaima Volcano, located in Southern Andes Volcanic Zone at 38 • 40'S 71 • 40'W. Typically, the seismic signals can be divided in longperiod, tremor, and volcano-tectonic earthquakes. The seismic signals are time-segmented using a rectangular window of 1 minute of duration. In each segment, the instantaneous phase is calculated using the Hilbert Transform, and then, one feature is obtained. Thus, the principal hypothesis of this work is that the instantaneous phase can be assumed as a circular random variable in [0, 2π) interval. A second feature is obtained using the wavelet transform due to the fact that seismic signals present high energy located in low frequency. Then, in the range 1.55 and 3.11 Hz the wavelet coefficients were obtained and their mean energy is calculated as the second feature. Real seismic data represented using this two features are classified using a linear discriminant with a 92.5% of correct recognition rate.