Rheological Properties of the Magmas Feeding the Campi Flegrei Caldera (Italy) and Their Influence on Mixing Processes

Abstract

This chapter reviews and discusses the main rheological and physical properties and models (viscosity and density) of the melts feeding the Campi Flegrei caldera volcanism. Viscosity and density control flow and diffusion, and thus multicomponent convection and chemical mobility in the magma chamber. These, in turn, are thought to constrain magma mixing processes in the system. Our main goal is to summarise and analyse mixing experiments using natural volcanic products of the caldera as starting material. The mixing experiments have been performed using different devices (Taylor-Couette and centrifuge). Results show how easily Campi Flegrei caldera melts may mix. They confirm that different chemical elements homogenise in the melt at differing rates, providing an innovative quantitative approach, the estimation of a new parameter for measuring multi-component diffusion in magmas: Relaxation of Concentration Variance or Concentration Variance Decay. This enables the measurement of mobility for all elements present in the melt simultaneously. Comparing experimental and natural data clearly confirm the unavoidability of mixing during the replenishment history of the caldera reservoir(s).

Appendix 1 Viscosity Model for Latitic and Shoshonitic Compositions

Dry and hydrous viscosity for latitic and shoshonitic compositions from the CFc have been studied by Giordano et al. (2006, 2009), Misiti et al. (2011) and Di Genova et al. (2014). Misiti et al. (2011) selected two natural scoria samples from the pyroclastic sequences of the FR (latitic) and MIN2 (shoshonitic) eruptions. In contrast to other studies (Romano et al. 2003; Giordano et al. 2004; Misiti et al. 2006), instead of selecting the natural glassy phase, the bulk rock was remelted in air and quenched to glass (see Table 2). Parts of these then hydrated for measurements with the “falling sphere” and the micropenetration-techniques. The correspondent dry melts were further measured by the concentric cylinder and the micropenetration methods (see Table 4). A modified VFT-equation (Eq. 6) has been used in the description of the viscosity model:

$$\log \eta = a + \frac{b}{{\left( {T - c} \right)}} + \frac{d}{{\left( {T - e} \right)}}.\exp \left( {g.\frac{w}{T}} \right)$$

(6)

where η(eta) is the viscosity in Pa*s, T is the absolute temperature, w is the amount of water in wt% and a, b, c, d, e and g being fit parameters (there is no “f” fit parameter). Note: Confusingly the fitted parameter reported in the Misiti et al. (2011) paper have been fitted with “-a” instead of the “a” parameter. In Table 4 the “a” parameter is therefore the negative “a” parameter of Misiti et al. (2011). The viscosity data set for latitic melts of the FR eruption is rather limited in compositional space. At high temperatures there are only two data points for hydrous melts, with very similar water contents of 2.84 and 3.28 wt%, respectively. At lower temperatures the data covers restricted water contents up to a maximum of 1.2 wt%. The standard deviation of the model fit (0.35) is the worst for a specific model. Thus, the fit model could be further improved. A more recent viscosity model for water-and CO₂-bearing CFc latite has been obtained by Di Genova et al. (2014). These authors parameterised the measured viscosities of the FR magma with different amounts of CO₂ and H₂O using the equation reported in Di Genova et al. (2014), who modelled the viscosity by using the VFT equation log η = A + B / (T − C)] (Eq. 1) (Vogel 1921; Fulcher 1925; Tammann and Hesse 1926) with 4 fit parameters as follows:

$$\log \eta = A_{VFT} + \frac{{b1 + b2.\log \left( {1 + H_{2} O} \right)}}{{T\left( K \right) - C1 + C2.\log \left( {1 + H_{2} O} \right)}}$$

(7)

where η is the viscosity in Pa*s, T is the absolute temperature, H₂O is the water concentration in wt%, AVFT is the pre-exponential factor, b1 and b2 are related to the pseudo-activation energy, c1 and c2 are related to the Vogel temperature.

The viscosity data has been fitted by assuming that AVFT is a constant and independent of composition (e.g., Richet and Bottinga 1995; Whittington et al. 2001). The value of the preexponential parameter AVFT is taken as −4.55 Pa*s (Giordano et al. 2009). Fitted values and standard deviations of the b and c parameters are on Table 4. Equation 7 reproduces the experimental data from Di Genova et al. (2014) with a standard error of estimation of 0.57 log units.

The viscosity dataset for a shoshonitic melt feeding the MIN2 eruption is quantitatively not sufficient. Only 12 data points cover the low temperature region with maximum water content of 2.43 wt%. In the high temperature region two data points, for relatively similar water contents (2.35 and 3.30 wt%), constrained the dataset.

Both models predict the unlikely behaviour of a distinct difference of a dry curve and a curve where all hydrous melts collapse. It remains unclear if this result is due to the sparse compositional range (water content) covered, or to the modified VFT-equation, or to both. Also, the GRD and HZ general viscosity models cannot solve this problem. Up to this point it seems that for hydrous melts of shoshonitic composition there is no sufficient dataset range and a pertinent model available yet. Thus, both dataset and fit models still need improvements.

The numerical consequences of fitting viscosity-temperature datasets using natural samples from CF to non-Arrhenian rheological models have been explored by several authors (e.g., Giordano and Dingwell 2003; Russell and Giordano 2005; Giordano et al. 2006, 2008, 2009; Misiti et al. 2011; Di Genova et al. 2014). This kind of analysis shows that strong correlations of model parameters (e.g., ATVF, BTVF, CTVF) are inherent to non-Arrhenian models (Giordano et al. 2009). Uncertainties on model parameters and covariances between parameters are strongly affected by the quality and distribution of the experimental data, as well as the degree of non-Arrhenian behaviour as pointed out by Giordano et al. (2009).

The task of modelling viscosity in natural systems is therefore the product of a joint effort of decreasing uncertainties, through thorough experimental measurements of a wide compositional range, with different techniques, and improving mathematical parameterisation. This way, step by step, we are getting closer to the natural system, towards a best-fitting model.