Dipartimento di Fisica, Università degli Studi di Napoli «Federico II», Napoli, Italy

Magnetotelluric (MT) soundings performed in the past in the volcanic area of Mt. Vesuvius by two independent research groups showed in the same places MT apparent resistivity curves with very similar shape, but statically shifted by one order of magnitude, at least. To try to resolve this ambiguity new controlled source audio-magnetotelluric (CSAMT) measurements have been carried out in the same MT sites. The interpretation of the CSAMT dataset, combined with that of two shallow dipole-dipole geoelectrical resistivity tomographies previously carried out in the area have allowed a reliable electrical structure to be recovered down to a few km of depth, which will next be used for a best constrained re-interpretation of the deep MT soundings.

It is illustrated that IP phenomena in rocks can be described using conductivity dispersion models deduced as solutions to a 2nd-order linear differential equation describing the motion of a charged particle immersed in an external electrical field. Five dispersion laws are discussed, namely: the non-resonant positive IP model, which leads to the classical Debye-type dispersion law and by extension to the Cole-Cole model, largely used in current practice; the non-resonant negative IP model, which allows negative chargeability values, known in metals at high frequencies, to be explained as an intrinsic physical property of earth materials in specific field cases; the resonant flat, positive or negative IP models, which can explain the presence of peak effects at specific frequencies superimposed on flat, positive or negative dispersion spectra.

We expand the theory of probability tomography to the integration of different geophysical datasets. The aim of the new method is to improve the information quality using a conjoint occurrence probability function addressed to highlight the existence of common sources of anomalies. The new method is tested on gravity, magnetic and self-potential datasets collected in the volcanic area of Mt. Vesuvius (Naples), and on gravity and dipole geoelectrical datasets collected in the volcanic area of Mt. Etna (Sicily). The application demonstrates that, from a probabilistic point of view, the integrated analysis can delineate the signature of some important volcanic targets better than the analysis of the tomographic image of each dataset considered separately.