GNGTS 2018 - 37° Convegno Nazionale

764 GNGTS 2018 S essione 3.3 Bresco M., Raiconi G., Barone F., De Rosa R. and Milano L.; 2005: Genetic approach helps to speed classical Price algorithm for global optimization. Soft Comput., 9 , 525–535. Di Maio R., Mauriello P., Patella D., Petrillo Z., Piscitelli S. and Siniscalchi A.; 1998: Electric and electromagnetic outline of the Mount Somma-Vesuvius structural setting. Journal of Volcanology and Geothermal Research, 82 , 219–238. Di Maio R., Rani, P. Piegari E. and Milano L.; 2016: Self-potential data inversion through a Genetic-Price algorithm. Comput. Geosci., 94 , 86-95. De Natale G., Chiarabba C., Troise C., Trigila R., Dolfi D. and Kissling E.; 2004: Seismicity and 3D sub-structure at Somma–Vesuvius volcano: evidence for magma quenching due to H2O exolution? Earth Planet. Sci. Lett., 221 , 181– 196. Goldberg D.E.; 1989: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Welsey Publishing Company, INC, NewYork, p.432. Milia A., Torrente M.M. and Bellucci F.; 2012: A possible link between faulting, cryptodomes and lateral collapses at Vesuvius Volcano (Italy). Global and Planetary Change, 90-91 , 121-134. Kirkpatrick S., Gelatt C.D. Jr. and Vecchi M.P.; 1983: Optimization by simulated annealing. Science, 220(4598) , 671–680. Price W.L.; 1976: A controlled random search procedure for global optimization . The Computer Journal, 20 , 357-370. Sen M.K. and Stoffa P.L.; 2013: Global Optimization Methods in Geophysical Inversion . 2nd edn. Cambridge University Press. Tlas M. and Asfahani J.; 2008: Using of the Adaptive Simulated Annealing (ASA) for quantitative interpretation of self-potential anomalies due to simple geometrical structures . JKAU: Earth Sci., 19 , 99–118. Tramparulo F.D.A., Vitale S., Isaia R., Tadini A., Bisson M. and Prinzi E.P.; 2018: Relation between alternating open/ closed-conduit conditions and deformation patterns: An example from the Somma-Vesuvius volcano (southern Italy). Journal of Structural Geology, 112 , 138–153. Weise, T.; 2011: Global Optimization Algorithms - Theory and Application . Available at http://www.it-weise.de/ projects/book.pdf Sources/CVS-Repository: goa-taa.cvs.sourceforge.net. SANDS UNDER STRESS: CAN WE DETERMINE ELASTIC VELOCITIES FROM STRESS-STRAIN MEASUREMENTS IN THE CASE OF IRREVERSIBLE DEFORMATION AND HYSTERESIS? A. Sajeva 1 , R. Filograsso 2 , S. Capaccioli 3 1 Dipartimento Scienze della Terra, Università di Pisa, Italy 2 CGG, Crawley, Gran Bretagna 3 Dipartimento Fisica, Università di Pisa, Italy Introduction. For granular media, such as unconsolidated sands, it is well known that static elastic moduli differ from dynamic elastic moduli. Static elastic moduli measure the response to quasi static deformation of the sample, whereas dynamic elastic moduli are related to wave propagation. The difference among them is due to the difference in the amount of strain. In fact, the strain involved in static measurements is orders of magnitude higher than the strain involved in dynamic measurements. Large strain causes grain rearrangement, slip and rotation of grains. Differently, no irreversible deformation occurs during the perturbation caused by the propagation of a wave throughout a granular medium. In geo-mechanical laboratories, stress-strain curves and ultrasonic velocities can be measured in order to retrieve both static and dynamic elastic moduli. Differently, in seismic surveys, we only get dynamic elastic moduli from P-wave and S-wave velocities which are in turn estimated via an inversion procedure. In the oil industry, dynamic elastic parameters are eventually converted into petrophysical mesoscopic parameters which play a role in the reservoir characterization (Aleardi and Ciabarri, 2017).