4D non-linear tomography of the Campi Flegrei caldera

Since magma and gas uprise and accumulation into the crust differently affect seismic velocities, seismic tomography is revealing a potential tool to detect how melt versus exsolved magmatic volatile phase reservoirs are distributed at depth into the volcanic plumbing systems. 4D tomography (in space and time) can detect temporal changes in seismic velocities, enabling to follow gas and magma accumulation, a crucial aspect in monitoring open vent volcanoes and unresting calderas. To achieve this goal we combined fully non-linear tomography with improved and fast seismic phases detection allowed by machine-learning. The use of a fully non-linear approach allows overcoming some limits of standard linearized methods, which can obscure details because of damping, smearing and blurring of seismic anomalies, due to the need of global regularization of the inverse problem. We are developing a Bayesian approach to local earthquake tomography that erases the dependence on arbitrary starting velocity models, providing more reliable absolute velocity values and model uncertainties. This is an important aspect when observed changes in seismic velocity have to be compared with theoretical predictions from petrophysical modeling. Furthermore, a data-driven self-adapting parameterizations of the earth structure strongly enhances the resolution capability in regions where high gradients in velocity are expected and in regions poorly illuminated by seismic rays, permitting to reveal seismic anomalies not detectable by standard approaches because of pre-determined model parametrization. Here, we present our novel approach and the application to the Campi Flegrei caldera, where the discrimination between gas and magma injection at shallow depth is crucial for unraveling the causal process of the unrest and in multirisk assessment and forecasting.