Local shear wave (S-wave) velocity structure and sedimentary cover thickness are essential parameters that control local amplification of ground motion and associated seismic hazard during earthquakes. Recently, analy...
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Local shear wave (S-wave) velocity structure and sedimentary cover thickness are essential parameters that control local amplification of ground motion and associated seismic hazard during earthquakes. Recently, analysis of environmental background noise from individual stations has been used to estimate horizontal-to-vertical spectral ratio (H/V) curves. In the present study, we inverted H/V curves using environmental noise immediately prior to earthquake P-wave arrival to retrieve S-wave velocity (or shear velocity, V (S)) profiles at four temporary seismic monitoring stations (YDF, YDS, YDU, and YDD) near the Yedang Reservoir Dam. In the first step, we used a random search algorithm to constrain the subvolume of the parameter space (S-wave velocity structure) where the minimum of the misfit was located. In the second step, we independently applied two non-linear processes (a monte carlo sampling algorithm and a simulated annealing algorithm) to force the inversion towards an optimal solution, using the minimum misfit model determined in the first step as an initial estimate;we then compared the results. The feasibility and effectiveness of this two-step approach were verified by inversion of H/V curves for seismic noise recorded at four seismic monitoring stations near the Yedang Reservoir Dam. Borehole and topographical data from the four stations provided a well-constrained estimation of the local shear wave profile. Comparisons of synthetic and observed H/V curves showed that combining the two inversion algorithms efficiently overcame the extreme non-linearity of the inversion problem and provided a good resolution of S-wave structures at the Yedang Reservoir Dam. The S-wave velocity profile at the YDF station, which is situated on fresh, uniform bedrock, ranged from similar to 2300 to 2700 m/s, which was consistent with the borehole data. Both the YDU station (which exhibited fundamental and first-order resonance frequency harmonics) and the YDD station (whic
Today, there are more mature and relative perfect means of how to learn structures or parameters from completed data and learn parameters of fixed structure from uncompleted data. But it is a more difficult thing that...
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ISBN:
(纸本)081946452X
Today, there are more mature and relative perfect means of how to learn structures or parameters from completed data and learn parameters of fixed structure from uncompleted data. But it is a more difficult thing that learning structures of Bayesian Networks from uncompleted data. A compound learning algorithm is proposed;it combines the EM algorithm, monte carlo sampling algorithm and evolution algorithm together, uses EM algorithm to learn parameters of networks in uncompleted data, then samples the best network, converts the uncompleted data to completed data, and then evolves the structure using evolution algorithm. This algorithm could get over the defect of EM algorithm that frequently gains local maximum. Because data processing is based on posterior networks structures, structures of Bayesian Networks is optimizing and optimizing with evolution computing, the reliability of complementary data is higher. Learning rate is high and performance of this algorithm is good.
Computer exploits often involve an attacker being able to compromise a sequence of hosts, creating a chain of "stepping stones" from his source to ultimate target. Stepping stones are usually necessary to ac...
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ISBN:
(纸本)9781479974863
Computer exploits often involve an attacker being able to compromise a sequence of hosts, creating a chain of "stepping stones" from his source to ultimate target. Stepping stones are usually necessary to access well-protected resources, and also serve to mask the attacker's location. This paper describes means of constructing models of networks and the access control mechanisms they employ to approach the problem of finding which stepping stone paths are easiest for an attacker to find. While the simplest formulation of the problem can be addressed with deterministic shortest-path algorithms, we argue that consideration of what and how an attacker may (or may not) launch from a compromised host pushes one towards solutions based on montecarlosampling. We describe the samplingalgorithm and some preliminary results obtained using it.
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