GNGTS 2017 - 36° Convegno Nazionale

714 GNGTS 2017 S essione 3.3 Fernández Martínez, J. L., García Gonzalo, E.,Fernández Álvarez, J.P., Kuzma, H. A. and Menéndez Pérez, C.O.; 2010: PSO: A powerful algorithm to solve geophysical inverse problems . Journal of Applied Geophysics, 71 (1), 13-25. Godio, A., Massarotto, A. and Santilano, A.; 2016: Particle swarm optimization of electomagnetic soundings. Proceedings from 22 nd Near Surface Geoscience EAGE Conference, Barcelona. Jones, A.G., Hutton, R.; 1979: A multi-station mgnetotelluric study in Southern Scotland - I. Fieldwork, data analysis and results . Geophysical Journal International, 56(2), 329-349. Kennedy, J. and Eberhart, R.; 1995: Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, IV, 1942-1948. Santilano, A.; 2016: Deep geothermal exploration by means of electromagnetic methods: new insights from the Larderello geothermal field (Italy). PhD thesis in Environmental engineering, XXIX cycle, Politecnico di Torino, pp. 242. Sen, M. K. and Stoffa, P. L.; 2013: Global Optimization Methods in Geophysical Inversion. Cambridge University Press, Cambridge, pp. 289. Shaw, R. and Srivastava, S.; 2007: Particle swarm optimization: A new tool to invert geophysical data. Geophysics, 72 (2), F75-F83. Shi, Y. and Eberhart, R.; 1998: A modified particle swarm optimizer.  IEEE International Conference on Evolutionary Computation Proceedings, 69-73. Zhan, Z.H., Zhang, J., Li, Y., Chung, H.S.H; 2009: Adaptive Particle Swarm Optimization . IEEE Transactions On Systems, Man and Cybernetics - part B (Cybernetics), 39 (6),1362-1381. Constraining the continental crust radioactive heat production with satellite-derived gravity models: revisiting the linear relationship A. Pastorutti, C. Braitenberg Dept. of Mathematics and Geosciences, University of Trieste, Italy Background. The resolution of satellite-derived global gravity models (GGMs) is adequate to resolve the mass distribution in the continental crust, the strong density contrast at the crust- mantle boundary (CMB), and the undulations of the lithosphere-asthenosphere boundary (LAB). These aspects suggest that GGMs can be promising tools in modelling the deep thermal state of the lithosphere, the heat transfer regimes involved and the heat flow through the Earth surface. The directly measurable near-surface temperature field is largely influenced by ongoing geo- dynamics and near-surface processes, both of which have shorter characteristic timescales than the one needed by purely conductive thermal diffusion to reach steady-state equilibrium in the lithosphere. Heat flow measurements are also costly, their distribution is often biased towards areas of increased interest (e.g. those with high fluxes, exploited for high-enthalpy geothermal energy) and public access to data is an issue. Collecting and harmonising the published datasets to a common standard is an effort spanning multiple decades (Lee and Uyeda, 1965). Gravity and geoid anomalies have already been integrated in multi-observable modelling strategies, and show a satisfactory resolving power for investigating the nature of lithospheric inhomogeneities (Fullea et al. , 2009). Still, gravity data alone –which has an unmatched global sampling regularity– can already provide estimates independently from other geophysical data, before integration. A relationship between the lithospheric mass distribution (inverted from density contrasts) and models of its thermal state must rely on laws connecting density and thermal parameters (i.e. radioactive heat production, thermal conductivity, boundary conditions), and a set of hypotheses on the heat transport mechanisms involved. A key factor is the radioactive heat production (RHP) occurring in the crystalline continental crust, which exhibits a 50-fold increase against sub-continental mantle content in U, Th, K (Vilà et al. , 2010)