The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Yildirim, B., C. Chryssostomidis, and G.E. Karniadakis. "Efficient sensor placement for ocean measurements using low-dimensional concepts." Ocean Modelling 27.3-4 (2009): 160-173. Web.
19Keywords: 20 Phase-averaged model 21Ocean wave modeling 22Uncertainty quantification 23Generalized polynomial chaos 24Sparse grid collocation 25Sensitivity analysis 26Karhunen-Loeve decomposition Q2 27 2 8 a b s t r a c t 29 The primary objective of this study is to introduce a stochastic framework based on generalized polyno-30 mial chaos (gPC) for uncertainty quantification in numerical ocean wave simulations. The techniques we 31 present can be easily extended to other numerical ocean simulation applications. We perform stochastic 32 simulations using a relatively new numerical method to simulate the HISWA (Hindcasting Shallow Water 33 Waves) laboratory experiment for directional near-shore wave propagation and induced currents in a 34 shallow-water wave basin. We solve the phased-averaged equation with hybrid discretization based 35 on discontinuous Galerkin projections, spectral elements, and Fourier expansions. We first validate the 36 deterministic solver by comparing our simulation results against the HISWA experimental data as well 37 as against the numerical model SWAN (Simulating Waves Nearshore). We then perform sensitivity anal-38 ysis to assess the effects of the parametrized source terms, current field, and boundary conditions. We 39 employ an efficient sparse-grid stochastic collocation method that can treat many uncertain parameters 40 simultaneously. We find that the depth-induced wave-breaking coefficient is the most important param-41 eter compared to other tunable parameters in the source terms. The current field is modeled as random 42 process with large variation but it does not seem to have a significant effect. Uncertainty in the source 43 terms does not influence significantly the region before the submerged breaker whereas uncertainty in 44 the incoming boundary conditions does. Considering simultaneously the uncertainties from the source 45 terms and boundary conditions, we obtain numerical error bars that contain almost all experimental 46 data, hence identifying the proper range of parameters in the action balance equation.47
Immediately following the M w 7.2 Darfield, New Zealand, earthquake, over 180 Quake-Catcher Network (QCN) low-cost micro-electro-mechanical systems accelerometers were deployed in the Canterbury region. Using data recorded by this dense network from 2010 to 2013, we significantly improved the QCN rapid magnitude estimation relationship. The previous scaling relationship (Lawrence et al., 2014) did not accurately estimate the magnitudes of nearby (< 35 km) events. The new scaling relationship estimates earthquake magnitudes within 1 magnitude unit of the GNS Science GeoNet earthquake catalog magnitudes for 99% of the events tested, within 0.5 magnitude units for 90% of the events, and within 0.25 magnitude units for 57% of the events. These magnitudes are reliably estimated within 3 s of the initial trigger recorded on at least seven stations. In this report, we present the methods used to calculate a new scaling relationship and demonstrate the accuracy of the revised magnitude estimates using a program that is able to retrospectively estimate event magnitudes using archived data.
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