GNGTS 2018 - 37° Convegno Nazionale

GNGTS 2018 S essione 3.3 739 References Carbonari, R., D’Auria, L., Di Maio, R. and Petrillo, Z.; 2017: Denoising of magnetotelluric signals by polarization analysis in the discrete wavelet domain . Computers & Geosciences, 100 , 135-141. D’Auria, L., Esposito, A.M., Petrillo, Z. and Siniscalchi, A.; 2015: Denoising magnetotelluric recordings using Self- Organizing Maps . In Advances in Neural Networks: Computational and Theoretical Issues, Springer, Cham, 137-147. Egbert, G.D.; 1997: Robust multiple-station magnetotelluric data processing . Geophysical Journal International, 130.2 , 475-496. Escalas, M., Queralt, P., Ledo, J. and Marcuello, A.; 2013: Polarisation analysis of magnetotelluric time series using a wavelet-based scheme: A method for detection and characterisation of cultural noise sources . Physics of the Earth and Planetary Interiors, 218 , 31-50. Esposito, A.M., Giudicepietro, F., D’Auria, L., Scarpetta, S., Martini, M., Coltelli, M. and Marinaro, M.; 2008: Unsupervised neural analysis of very-long-period events at Stromboli volcano using the self-organizing maps . Bulletin of the Seismological Society of America, 98 , 2449-2459. Jones, A.G., Chave, A.D., Egbert, G., Auld, D. and Bahr, K.; 1989: A comparison of techniques for magnetotelluric response function estimation . Journal of Geophysical Research: Solid Earth, 94(B10) , 14201-14213. Kohonen, T.; 1998: The self-organizing map . Neurocomputing, 21(1-3) , 1-6. Liu, Y. and Weisberg, R.H.; 2011: A review of self-organizing map applications in meteorology and oceanography . In Self Organizing Maps-Applications and Novel Algorithm Design. InTechOpen. Ritter, O., Junge, A. and Dawes, G.J.; 1998: New equipment and processing for magnetotelluric remote reference observations . Geophysical Journal International, 132(3) , 535-548. Simpson, F. and Bahr, K.; 2005: Practical magnetotellurics . Cambridge University Press. Sutarno, D. and Vozoff K.; 1989: Robust M-estimation of magnetotelluric impedance tensors . Exploration Geophysics, 20.3 , 383-398. Weidelt, P.; 1972: The inverse problem of geomagnetic induction. J. Geophys., 38 , 257-289. Weckmann, U., Magunia A. and Ritter O.; 2005: Effective noise separation for magnetotelluric single site data processing using a frequency domain selection scheme. Geophysical Journal International, 161.3 , 635-652. SIGNALS DAMPING ANALYSIS AS A TOOL FOR INVESTIGATING ONGOING ROCK MASS DAMAGING D. D’Angiò 1 , R. Iannucci 1 , L. Lenti 2 , S. Martino 1 1 Department of Earth Sciences and Research Centre for Geological Risks (CERI), “Sapienza” University of Rome, Italy 2 French Institute of Science and Technology for Transport, Development and Networks (IFSTTAR) - Paris East University, France Introduction. Microseismic monitoring represents an affirmed diagnostic tool to detect vibrational signals in several contexts, like in structural monitoring, mining excavation, tunnelling, and in slope stability assessment as well (Colombero et al. , 2018). Due to the resolution and sensitivity of modern seismic devices, it is possible to record very weak signals with a broad frequency resolution. As it regards applications devoted to rock fall and rock slide risk prevention (Spillmann et al., 2007), the signals recorded by microseismic monitoring systems can be related to the ongoing damaging of the rock mass, i.e. formation of new fractures, thus indicating a worsening of its stability conditions, that can lead to rock failures (Loew et al. , 2016). By considering a microseismic dataset collected in an experimental test site, this study proposes an analysis of microseismic events focused on investigating damping coefficients from recorded signals, since damping could be regarded as a possible indicator of variations in rock mass micro-fracture network.