Modeling and inversion of dispersion curves of surface waves in shallow site investigations

详细信息    Modeling and inversion of dispersion curves of surface waves in shallow site investigations

• 出版日期：2007.
• 页数：1 online resource.
• 第一责任说明：Donghong Pei.
• 分类号：a631
• ISBN：9780549164340(e-book) :

MARC全文

62h0047252 20131203091048.0 cr an |||||||| 131024s2007 a fsbm |000|0 eng | 3275830 9780549164340(e-book) : CNY371.35 NGL eng NGL a631 Pei, Donghong. Modeling and inversion of dispersion curves of surface waves in shallow site investigations [electronic resource] / Donghong Pei. 2007. 1 online resource. Description based on online resource; title from title page (viewed Oct. 24, 2013) Thesis (Ph.D.)--University of Nevada, Reno.bGeology, 2007. Includes bibliographical references. The shallow S-wave velocity structure is very important for the seismic design of engineered structures and facilities, seismic hazard evaluation of a region, comprehensive earthquake preparedness, development of the national seismic hazard map, and seismic-resistant design of buildings. The use of surface waves for the characterization of the shallow subsurface involves three steps: a) acquisition of high-frequency broadband seismic surface wave records generated either by active sources or passive ambient noise microtremors or microseisms), b) extraction of phase dispersion curves from the recorded seismic signals, and c) derivation of S-wave velocity profiles either using inversion algorithms or manually error and trial forward modeling. The first two steps have been successfully achieved by several techniques. However, the third step inversion) needs more improvements. An accurate and automatic inversion method is needed to generate shallow S-wave velocity profiles. With the achievement of a fast forward modeling method, this study focuses on the inversion of phase velocity dispersion curves of surface waves contained in ambient seismic noise for a one dimensional, flat-layered S-wave velocity structure. For the forward modeling, we present a new more efficient algorithm, called the fast generalized R/T reflection and transmission) coefficient method, to calculate the phase velocity of surface waves for a layered earth model. The fast method is based on but is more efficient than the traditional ones. The improvements by this study include 1) computation of the generalized reflection and transmission coefficients without calculation of the modified reflection and transmission coefficients； 2) presenting an analytic solution for the inverse of the 4X4 layer matrix E. Compared with traditional R/T methods, the fast generalized R/T coefficient method, when applied on Rayleigh waves, significantly improves the speed of computation, cutting the computational time at least by half while keeping the stability of the traditional R/T method. On inversion study, the dissertation explored a linear inversion technique, a non-linear inversion method, and a joint method on the dispersion data of surface waves. Chapter 3 explores the Occams linear inversion technique with a higher-order Tikhonov regulization. The blind tests on a suite of nine synthetic models and two field data sets show that the final model is heavily influenced by a) the initial model in terms of the number of layers and the initial S-wave velocity of each layer)； b) the minimum and the maximum depth of profiles； c) the number of dispersion picks； d) the frequency density of dispersion picks； and e) other noise. To minimize this initial-model-dependence of the Occams inversion, the nonlinear simulated annealing SA) inversion technique is proposed in Chapter 4. Following previous developments I modified the SA inversion yielding one-dimensional shallow S-wave velocity profiles from high frequency fundamental-mode Rayleigh dispersion curves and validated the inversion with blind tests. Unlike previous applications of SA, this study draws random numbers from a standard Gaussian distribution. The numbers simultaneously perturb both S-wave velocities and layer thickness of models. The annealing temperature is gradually decreased following a polynomial-time cooling schedule. Phase velocities are calculated using the reflectivity-transmission method. The reliability of the model resulting from our implementation is evaluated by statistically calculating the expected values of model parameters and their covariance matrices. Blind tests on the same data sets as these in Chapter 3 show that the SA implementation works well for S-wave velocity inversion of dispersion curves from high-frequency fundamental-mode Rayleigh waves. Blind estimates of layer S-wave velocities fall within one standard deviation of the velocities of the original synthetic models in 78% of cases. A hybrid method is also explored in Chapter 4. The hybrid idea is that the models obtained by the SA can used as input to the Occams inversion. Tests show that the hybrid method does not always provide better results. Dispersion curves of fundamental mode Rayleigh waves alone do not contain sufficient information to uniquely determine a model. The velocity-depth trade-off gives rise to model non-uniqueness. A joint SA inversion method is proposed in Chapter 5 using the fundamental-mode Love wave dispersion curves to constrain the Rayleigh wave inversion by the SA optimization. The SA technique described in Chapter 4 is applied on the dispersion data of both fundamental-mode Love and Rayleigh waves with equal weighting factor. Three synthetic tests show that Love wave constraints result in significant improvement of inverted model in terms of resolution of low velocity zones and high velocity contrasts. Prospecting ; Seismic prospecting. Geophysical methods. Electronic dissertations local. aInternet resource. aCN b010001 http://pqdt.bjzhongke.com.cn/Detail.aspx?pid=9hf8iGjXGUQ%3d 010001 Bs2765 rCNY371.35 ; h1 bs1312