Salvatore Scudero

3D Subsoil Model of the San Biagio ‘Salinelle’ Mud Volcanoes (Belpasso, Sicily) derived from Geophysical Surveys

Mud volcanoes are common in active mountain fronts. At Mt. Etna, located just between the Apennine front in Sicily and its foredeep, there are some manifestations of mud volcanism in the lower border of the volcanic edifice. The activity of these mud volcanoes is characterized by persistent emission of muddy water mixed with salts, which rises to the surface due to the gas pressure in the subsoil. The San Biagio Salinelle is one of the three mud volcano fields located around the Paternò eruptive monogenic apparatus; this old volcanic structure was one of the first subaerial volcanic manifestations that formed in the pre-Etnean phase. It is not fully clear whether and how the activity of the mud fields is connected with the volcanic activity of Mt. Etna. Noninvasive geophysical surveys were carried out in the area of the active cone of the San Biagio Salinelle, in order to identify the probable ascent path of the emitted products. Seismic ambient noise records were collected at the nodes of a specially designed grid and, subsequently, the Vs values were obtained from an active seismic survey. A digital elevation model (DEM) of the area was obtained by a topographic survey, carried out with the GNSS technique (global navigation satellite system), in real-time kinematic mode. The DEM and the topographic benchmark installed will represent the reference surface for future periodic monitoring of the ongoing deformation in the area. Our results provide an accurate and detailed 3D subsurface model showing the shallower feeding system of the investigated mud volcano.

Landslide Processes and Susceptibility Mapping in NE Sicily, Italy

Many destructive debris flows hit several villages in the Peloritani Mountains area (Sicily, Italy) in October 2009. The collection of several types of spatial data, together with the landslides inventory, allowed the assessment of the landslide susceptibility by the application of two different bivariate techniques.