GNGTS 2018 - 37° Convegno Nazionale

GNGTS 2018 S essione 3.3 761 References Ciaramella A., De Lauro E., Falanga M. and Petrosino S.; 2011: Automatic detection of long-period events at Campi Flegrei Caldera (Italy). Geophys. Res. Lett. 38 , L18302, DOI 10.1029/2011GL049065 Capuano P., De Lauro E., De Martino S. and Falanga M.; 2016: Detailed investigation of long-period activity at Campi Flegrei by Convolutive Independent Component Analysis. Phys. Earth Planet. Int. 253 , 48–57. Capuano P., De Lauro E., De Martino S., Falanga M. and Petrosino S.; 2017: Convolutive independent component analysis for processing massive datasets: a case study at Campi Flegrei (Italy) . Nat. Hazards, 86 (2), 417-429, DOI 10.1007/s11069-016-2545-0 De Lauro E., De Martino S., Falanga M. and Petrosino S; 2016: Fast wavefield decomposition of volcano-tectonic earthquakes into polarized P and S waves by Independent Component Analysis. Tectonophysics, 690 , 355-361. DOI 10.1016/j.tecto.2016.10.005 Hyvärinen, A., Karhunen, J., Oja, E., 2001. Independent Component Analysis. John Wiley, New York. AN INNOVATIVE GLOBAL OPTIMIZATION ALGORITHM FOR IDENTIFYING THE SOURCE PARAMETERS OF POTENTIAL FIELDS. APPLICATION TO MULTIPLE SELF-POTENTIAL ANOMALIES E. Piegari 1 , R. Di Maio 1 , R. Carbonari 1 , E. Vitagliano 1 , L. Milano 2,3 1 Dipartimento di Scienze della Terra, dell’Ambiente e delle Risorse, Università di Napoli Federico II, Napoli, Italy 2 Dipartimento di Fisica, Università di Napoli Federico II, Napoli, Italy 3 INFN sez. di Napoli, Complesso Universitario di Monte S. Angelo, Napoli, Italy Introduction. In the last decades, an increasing number of global optimization algorithms has been proposed to solve geophysical inverse problems (Sen and Stoffa, 2013). Indeed, an inverse problem can be posed as an optimization problem where the function to be optimized, usually called objective function, misfit function or fitness, provides an estimate of the difference between observed data and synthetic data computed by a trial model. In the framework of probabilistic global optimization methods (Weise, 2011), some algorithms use statistical distributions inspired by physical processes to get suggestions on which solution candidate has to be tested next, as for example the Simulated Annealing algorithms, which select the next solution candidate according to the Boltzmann probability factor of atom configurations of cooling metals (Kirkpatrick et al. , 1983; Tlas and Asfahani, 2008; Biswas and Sharma, 2014). Other algorithms, such as the Genetic Algorithms, are instead inspired by Darwinian evolution processes and treat solution candidates as individuals that compete in a virtual environment controlled by the genetic mechanisms of mutation, reproduction and crossover (Goldberg, 1989). In this work, we present and discuss some applications of a new hybrid global optimization algorithm, GPA, recently proposed by the authors for quantitative interpretations of potential field data (Di Maio et al. , 2016). The proposed approach includes a mutation operator in a controlled random search algorithm and its effectiveness has been proved on several synthetic and field data concerning single self-potential (SP) anomalies. Here, the results of a numerical study focused on multiple self-potential anomalies are shown for two of the main application fields of the SP method, i.e. for volcanic and soil contamination risk. The global optimization algorithm. The proposed algorithm for geophysical data inversion is an improvement of the first controlled random search algorithm proposed by Price (1976) for finding the absolute extreme point of a scalar function S of m variables. After defining the initial search domain for each of the m variables, a number N of trial points, with N >> m, is chosen randomly within the initial search domain, and the S values at each point are stored in the search array A . At each iteration, if the value of S in the selected trial point P is less