and Minster, J.B., Numerical Simulation of Attenuated Wavefields Using a Pade Approximant Method, Geophys. Liu, H.P., Anderson, D.L., and Kanamori, H., Velocity Dispersion due to Anelasticity Implications for Seismology and Mantle Composition, Geophys. and Friedlander, M.P., Probing the Pareto Frontier for Basis Pursuit Solutions, SIAM J.
and Friedlander, M.P., SPGL1: A Solver for Large-Scale Sparse Reconstruction, 2007. and Knio, O.M., Spectral Methods for Uncertainty Quantification, Scientific Computation, Berlin: Springer, 2010.Ĭhen, S.S., Donoho, D.L., and Saunders, M.A., Atomic Decomposition by Basis Pursuit, SIAMJ Sci. 1, Massachusetts Institute of Technology, Department of Meteorology, Cambridge, MA, 1956. Lorenz, E.N., Empirical Orthogonal Functions and Statistical Weather Prediction, MIT, Statistical Forecasting Project, Scientific Rep. Li, G., Iskandarani, M., Le Henaff, M., Winokur, J., Le Maitre, O.P., and Knio, O.M., Quantifying Initial and Wind Forcing Uncertainties in the Gulf of Mexico, Comput. and Kinali, K., Quantifying and Communicating Uncertainty in Seismic Risk Assessment, Struct. Sci., 21(2):93-102, 1983.ĭesceliers, C., Soize, C., and Cambier, S., Non-Parametric Parametric Model for Random Uncertainties in Non-Linear Structural Dynamics: Application to Earthquake Engineering, Earthquake Eng. and Spanos, S.D., Stochastic Finite Elements: A Spectral Approach, Berlin: Springer Verlag, 1991.Īhmadi, G., Stochastic Earthquake Response of Structures on Sliding Foundation, Int. Manolis, G.D., Stochastic Soil Dynamics, SoilDyn.
Maufroy, E., Chaljub, E., Theodoulidis, N.P., Roumelioti, Z., Hollender, F., Bard, P.Y., De Martin, F., Guyonnet-Benaize, C., and Margerin, L., Source-Related Variability of Site Response in the Mygdonian Basin (Greece) from Accelerometric Recordings and 3D Numerical Simulations, Bull. Earthquake Eng., 30(11):1198-1211,2010.Ĭedric, G.B., Fabrice, H., Maria, M., Alexandros, S., Elena, Z., Cecile, C., Nikolaos, V., Dimitrios, R., Artemios, A., Pierre-Yves, B., and Nikolaos, T., Imaging 3D Geological Structure of the Mygdonian Basin (Northern Greece) with Geological Numerical Modeling and Geophysical Methods, in EGU General Assembly Conference Abstracts, Vol. J., Apostolidis, P.I., and Pitilakis, K.D., 3D Soil Structure of the Mygdonian Basin for Site Response Analysis, Soil Dyn. Manakou, M.V., Raptakis, D.G., Chavez-Garcla, F. The surrogate model allows us to compute various statistical information of the uncertain prediction, including marginal and joint probability distributions, interval probability maps, and 2D fields of global sensitivity indices. We carefully validate the resulting surrogate model by estimating its predictive error using bootstrap, truncation, and cross-validation procedures.
#3d earthquake free free#
We focus on the peak ground motion at the free surface of the 3D domain, and our analysis utilizes a surrogate model combining two key ingredients for complexity mitigation: (i) a dimension reduction technique using empirical orthogonal basis functions, and (ii) a functional approximation of the uncertain reduced coordinates by polynomial chaos expansions. In addition to the large computational load of these simulations, solving the uncertainty propagation problem requires dedicated procedures to handle the complexities inherent to large dataset size and the low number of samples. To this end, we performed an ensemble of 400 large-scale simulations that requires 4 million core-hours of CPU time.
In this paper, we are interested in the seismic wave propagation into an uncertain medium.
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