Alessandro Santilano

Shallow Geothermal Exploration by Means of SkyTEM Electrical Resistivity Data: An Application in Sicily (Italy)

A novel procedure for estimating the geothermal energy exchanged by a unit volume was tested in northern Sicily (Italy), where public well data for depicting the complex geological setting were insufficient. An airborne electromagnetic survey was carried out in 2011, providing a 3D cell distribution of resistivity values. The integrated analysis of geological and resistivity data was used to identify six Litho-Electrical Units and to build a 3D geological model. This model was integrated with laboratory thermal conductivity measurements on rock samples, and was used to characterize the heat exchange at depths of up to 200 m, which in turn can be exploited for planning and designing geothermal heating and cooling plants using GSHP (Ground Source Heat Pump).

Electrical Resistivity Structures and their Relation to Geological Features at the Larderello Geothermal Field (Italy)

In this paper Magnetotelluric data were used to investigate the electrical resistivity structures of the Larderello-Travale geothermal system (LTGS) in southern Tuscany, Italy. MT data acquired by ENEL (Italian power company) in the ’90 were re-analysed and two-dimensional inversions were performed along a E-W profile crossing the LTGS. The near surface inhomogeneity effects have been reduced by means of tensor decomposition and inversion for static shift. The integration of geological, geophysical and well data was performed in a three-dimensional environment (Petrel, Schlumberger) in order to improve the 2D MT inversion by setting a well-constrained a-priori model and to check inversion results supporting the interpretation. Despite the lithological features, it was clear that an important reduction of the electrical resistivity was required in the crystalline basement to fit experimental data. The results of the MT inversion along the profile have been compared with available public geophysical data.

Particle Swarm Optimization of Electromagnetic Data with Parallel Computing in the 2D Case

Summary This work presents the application to electromagnetic data processing of the particle swarm optimization (PSO) algorithm, with the advantage of its high speed of convergence to the problem solution compared with other evolutionary methods. We focus on the inversion of MT synthetic data in the two-dimensional case. The PSO problem is solved complying with upper and lower bounds and a priori information, which is initially given only to a small amount of particles of the swarm. The fitness function is properly defined including smoothing parameter (Occam’s razor). Since it is a computationally demanding problem, a practical tool of parallel computing is tested and validated, thus allowing large computation time savings. Results show encouraging outcomes in terms of minimization of fitness function and data fitting. This approach represents the starting point for the 2D PSO application to MT data, Audio Magneto Telluric data and other near surface applications implying two-dimensional interpretation.

Particle Swarm Optimisation of Electromagnetic Soundings

We discuss through synthetic and real data some the application of PSO in electromagnetic soundings. The suggested approach can be easily adapted to resistivity soundings (RS), time domain soundings (TDEM) , magneto-telluric (MT) and audio-magneto-telluric survey (AMT). We propose an overview on the PSO for solving 1D problems with a priori information and/or lateral constraints. The application of PSO on AMT data is suggested by the high speed of convergence to a problem’s solution respect other evolutionary methods. Application on the synthetic dataset allow us to analyze the relevance of the setting parameters, and to select the optimal solutions when a priori information or additional constraints are introduced. We demonstrate how PSO could be an effective approach in AMT data processing (1D). The results can be selected as starting model for a subsequent gradient-based inversion.