Bayesian estimation of 2-DP-velocity models from active seismic arrival time data: imaging of the shallow structure of Mt Vesuvius (Southern Italy)

Abstract

SUMMARY A multi-2-D non linear traveltime tomography of the shallow (3‐4 km deep) structure of Mt Vesuvius volcano was performed. Data have been collected during two recent active seismic experiments using a total of 17 on-land shots and about 140 three-component digital seismographs. A newly developed technique for imaging the volcano velocity structure has been applied, based on an adaptive model space investigation where the number of grid nodes is progressively increased (multi-scale approach). The optimal model parametrization is chosen according to the minimum of the Akaike Information Criteria (AIC) parameter. This corresponds to finding the best compromise between the data misfit and simplicity of the model. The model parameter estimate is performed through the computation of an a posteriori probability density function (pdf), defined following the Bayesian approach. The maximum likelihood model is searched by an optimization technique which combines the genetic and simplex algorithms. The evaluation of the a posteriori pdf is based on traveltime computations using ray tracing techniques. Constraints on the model parameters are inserted in the form of prior pdf and error maps are inferred from cross-sections of the posterior probability around the found best fit solution. The retrieved images of Mt Vesuvius volcano show variable P-velocities in the range 1700‐5800 m s −1 . A fairly detailed image of the top of the Mesozoic carbonate rocks forming the basement of the volcanic area is obtained. A 9 km long, 1 km deep depression was detected at the N side of the volcano. The presence of a shallow high velocity body is evidenced underneath the Mt Somma caldera and it can be interpreted as a sub- or palaeovolcanic structure.