Volcanic plume vent conditions retrieved from infrared images: A forward and inverse modeling approach

Abstract

International audienceWe present a coupled fluid-dynamic and electromagnetic model for volcanic ash plumes. In a forward approach, the model is able to simulate the plume dynamics from prescribed input flow conditions and generate the corresponding synthetic thermal infrared (TIR) image, allowing a comparison with field-based observations. An inversion procedure is then developed to retrieve vent conditions from TIR images, and to independently estimate the mass eruption rate.The adopted fluid-dynamic model is based on a one-dimensional, stationary description of a self-similar turbulent plume, for which an asymptotic analytical solution is obtained. The electromagnetic emission/absorption model is based on Schwarzschild's equation and on Mie's theory for disperse particles, and we assume that particles are coarser than the radiation wavelength (about 10 μm) and that scattering is negligible. In the inversion procedure, model parameter space is sampled to find the optimal set of input conditions which minimizes the difference between the experimental and the synthetic image.Application of the inversion procedure to an ash plume at Santiaguito (Santa Maria volcano, Guatemala) has allowed us to retrieve the main plume input parameters, namely mass flow rate, initial radius, velocity, temperature, gas mass ratio, entrainment coefficient and their related uncertainty. Moreover, by coupling with the electromagnetic model we have been able to obtain a reliable estimate of the equivalent Sauter diameter of the total particle size distribution.The presented method is general and, in principle, can be applied to the spatial distribution of particle concentration and temperature obtained by any fluid-dynamic model, either integral or multidimensional, stationary or time-dependent, single or multiphase. The method discussed here is fast and robust, thus indicating potential for applications to real-time estimation of ash mass flux and particle size distribution, which is crucial for model-based forecasts of the volcanic ash dispersal process

Characterization of moderate ash‐and‐gas explosions at Santiaguito volcano, Guatemala, from infrasound waveform inversion and thermal infrared measurements

“…Here we have shown that the strength of acoustic sources, and the directivity of the associated wavefield, during volcanic explosions can be estimated by inversion of infrasound waveforms, and the erupted volume can be retrieved from analysis of TIR or visual imagery. The strength of the acoustic monopole source time function presented here, in Figure c, is consistent with previous measurements of eruption rates at Santiaguito [ Cerminara et al , ] based on plume modeling and inversion of TIR data. The strength of the dipole function at Santiaguito (Figure b) is about 2 orders of magnitude smaller than values reported for larger Vulcanian explosions at Tunghuraua volcano [ Kim et al , ] and Strombolian explosions at Mount Erebus [ Johnson et al , ].…”

Real-Time Geophysical Monitoring of Particle Size Distribution During Volcanic Explosions at Stromboli Volcano (Italy)

“…Particle size distributions are currently assessed based on strategies that use satellite data collected in the thermal infrared (Prata and Bernardo, 2009;Cerminara et al, 2015), ground-based disdrometers and radar systems (Gouhier and Donnadieu, 2008;Scollo et al, 2009), and thermal and visible cameras (Chouet et al, 1974;Ripepe et al, 1993;Bombrun et al, 2015). Each of these methods has their own potential and limitations in terms of the size range considered, as well as spatial and temporal resolution, and specific strategies need to be tailored to the type of emission.…”

From magma ascent to ash generation: investigating volcanic conduit processes by integrating experiments, numerical modeling, and observations

“…Parameters as plume exit velocity and trajectory, mass discharge rates, gas fluxes, gas compositions, and grain size distributions can now be measured in real time using high-speed visual, thermal, infrared and sulfur dioxide cameras [Bani et al 2013, Harris 2013, Bombrum et al 2014, Gaudin et al 2014a, b, Valade et al 2014, Barni et al 2015, Bombrum et al 2015, Cerminara et al 2015, Bombrun et al 2016, Gaudin et al 2016], a combination of thermal camera, weather radar observations and/ or infrasound measurements , De Angelis et al 2016, Vulpiani et al 2016, and open-path Fourier transform infrared (OP-FTIR) spectrometry [La Spina et al 2015, Allard et al 2016. These measurements have highlighted the unsteady nature of these parameters.…”