Diffusion Modelling as a Useful Petrological Tool for Near-Real-Time Volcanic Eruption Monitoring

Abstract:

Diffusion modelling is a well-established petrological technique for investigating the timescales of sub-surface processes occurring within magma storage bodies and transport systems prior to eruption. The technique typically produces – at best – results some weeks after a volcanic eruption has commenced.

This contribution describes progress made on a user-friendly, easy-to-use petrological ‘tool’ that can be deployed in near-real time at the onset of and during an eruption. This is important for fast timescale retrieval (within days rather than weeks) without compromising the reliability of the timescale retrieved. This has implications for eruption monitoring and hazard mitigation, providing a petrological time-series complementing existing geophysical monitoring techniques.

Current methods are constrained by data processing rates and the geometrical corrections required to control for random sectioning, crystal shape uncertainties and mineral anisotropy.

Using a set of Piton de la Fournaise (Réunion Island) lava flow samples and a suite of Mauna Loa (HI, US) air fall and lava flow samples, magmatic timescales for Mg-Fe diffusion in olivine have been retrieved.

Piton has a monodisperse crystal population, making a near-perfect baseline from which to pick apart the current diffusion modelling method. In so doing, a greater understanding of the sources of scatter and uncertainty in the process of timescale retrieval was obtained. The variety of potential sectioning orientations and their interaction with diffusion processes led to the proposal by Shea et al, 2015, in press, of selection rules to select boundaries, based on numerical models.

Combined with evaluations of crystal shape, crystal axial ratios, interfacial angles, U-stage measurements and a statistical approach, such selection rules should allow the orientation of the grain within a sample to be inferred, negating the need for independent EBSD measurements and enabling a faster processing technique.