Predicting Lava Dome Viscosity by Analyzing its Observed Morphology

Abstract:

Lava domes form when highly viscous magma erupts on the surface. Several types of lava dome morphology can be distinguished depending on the flow rate and the rheology of magma. Although the lava dome viscosity is complex, it depends basically on the volume fraction of crystals and the temperature. Here we present an approach to predict the viscosity of a lava dome based on the observed morphology of the dome. We consider a two-dimensional axisymmetric model of magma extrusion on the surface and lava dome evolution, and assume that the lava viscosity depends solely on the volume fraction of crystals, and this fraction in its turn depends on the time of crystal content growth (CCGT) and the discharge rate (DR). Lava domes are modeled using a finite-volume method implemented in Ansys Fluent software for various CCGT, DR, and volcanic vent size. A set of dome morphologies (namely, the shapes of the interface between the lava dome and the air) is then developed for specific time steps. We analyze the results of computational experiments and observed data as two-dimensional images. To find the viscosity of a lava dome, the difference between the observed and modeled dome morphologies (from the computed set of data) is minimized using three different norms: the symmetric difference, the peak signal-to-noise ratio, and the structural similarity index measure. Such norms are often used in the computer vision and the theory of image processing. Although each norm allows for determining the best fit between the modeled and observed shapes of lava dome, the structural similarity index measure norm seems to perform it better. This approach can be extended to three-dimensional cases and more realistic rheology of lava domes. Predicting the lava dome viscosity may help in the analysis of its dynamics and potential hazards of lava flows.