Combining the adaptive shrinkage genetic algorithm in the feasible region with the imaging of apparent vertical conductance differential, we have inverted the TEM conductive thin layer. The result of the inversion dem...Combining the adaptive shrinkage genetic algorithm in the feasible region with the imaging of apparent vertical conductance differential, we have inverted the TEM conductive thin layer. The result of the inversion demonstrates that by adaptive shrinkage in the feasible region, the calculation speed accelerates and the calculation precision improves. To a certain extent, in this method we surmount the transient electromagnetic sounding equivalence and reduced equivalence scope. Comparison of the inverted result with the forward curve clearly shows that we can image the conductive thin layer.展开更多
Most current prestack AVA joint inversion methods are based on the exact Zoeppritz equation and its various approximations. However, these equations only reflect the relation between reflection coefficients, incidence...Most current prestack AVA joint inversion methods are based on the exact Zoeppritz equation and its various approximations. However, these equations only reflect the relation between reflection coefficients, incidence angles, and elastic parameters on either side of the interface, which means that wave-propagation effects, such as spherical spreading, attenuation, transmission loss, multiples, and event mismatching of P-and S-waves, are not considered and cannot accurately describe the true propagation characteristics of seismic waves. Conventional AVA inversion methods require that these wave-propagation effects have been fully corrected or attenuated before inversion but these requirements can hardly be satisfied in practice. Using a one-dimensional(1 D) earth model, the reflectivity method can simulate the full wavefield response of seismic waves. Therefore, we propose a nonlinear multicomponent prestack AVA joint inversion method based on the vectorized reflectivity method, which uses a fast nondominated sorting genetic algorithm(NSGA II) to optimize the nonlinear multiobjective function to estimate multiple parameters, such as P-wave velocity, S-wave velocity, and density. This approach is robust because it can simultaneously cope with more than one objective function without introducing weight coefficients. Model tests prove the effectiveness of the proposed inversion method. Based on the inversion results, we find that the nonlinear prestack AVA joint inversion using the reflectivity method yields more accurate inversion results than the inversion by using the exact Zoeppritz equation when the wave-propagation effects of transmission loss and internal multiples are not completely corrected.展开更多
The inverse problem in geophysics is to infer the vertical structure from the observed data. The crucial assumption in deriving inversion algorithms obtained for different elementary layer structures. Much of the prev...The inverse problem in geophysics is to infer the vertical structure from the observed data. The crucial assumption in deriving inversion algorithms obtained for different elementary layer structures. Much of the previous work on this problem for the case of plane wave at normal incidence has consisted of deriving a Schrodinger equation from the basic acoustic and stress-strain equations, and then reconstructing the potential appearing in this equation by using the Gelfand-Levitan procedure. We shall be concerned with structures in which the unknown coefficient has jump discontinuities. Here the unknown potential in the corresponding Gelfand-Levitan framework is highly singular, so much so that the theory breaks down. In this paper it is presented an inversion algorithm based on the Riccati equation,which avoids to solve the problem of singularity. The model experiment was conducted in a sewage pool of a factory. The final result of inversion agrecd well with the direct experimental observation.展开更多
Surface wave methods have received much attention due to their efficient, flexible and convenient characteristics. However, there are still critical issues regarding a key step in surface wave inversion. In most exist...Surface wave methods have received much attention due to their efficient, flexible and convenient characteristics. However, there are still critical issues regarding a key step in surface wave inversion. In most existing methods, the number of layers is assumed to be known prior to the process of inversion. However, improper assignment of this parameter leads to erroneous inversion results. A Bayesian nonparametric method for Rayleigh wave inversion is proposed herein to address this problem. In this method, each model class represents a particular number of layers with unknown S-wave velocity and thickness of each layer. As a result, determination of the number of layers is equivalent to selection of the most applicable model class. Regarding each model class, the optimization search of S-wave velocity and thickness of each layer is implemented by using a genetic algorithm. Then, each model class is assessed in view of its efficiency under the Bayesian framework and the most efficient class is selected. Simulated and actual examples verify that the proposed Bayesian nonparametric approach is reliable and efficient for Rayleigh wave inversion, especially for its capability to determine the number of layers.展开更多
文摘Combining the adaptive shrinkage genetic algorithm in the feasible region with the imaging of apparent vertical conductance differential, we have inverted the TEM conductive thin layer. The result of the inversion demonstrates that by adaptive shrinkage in the feasible region, the calculation speed accelerates and the calculation precision improves. To a certain extent, in this method we surmount the transient electromagnetic sounding equivalence and reduced equivalence scope. Comparison of the inverted result with the forward curve clearly shows that we can image the conductive thin layer.
基金supported by the National Science and Technology Major Project(No.2016ZX05003-003)
文摘Most current prestack AVA joint inversion methods are based on the exact Zoeppritz equation and its various approximations. However, these equations only reflect the relation between reflection coefficients, incidence angles, and elastic parameters on either side of the interface, which means that wave-propagation effects, such as spherical spreading, attenuation, transmission loss, multiples, and event mismatching of P-and S-waves, are not considered and cannot accurately describe the true propagation characteristics of seismic waves. Conventional AVA inversion methods require that these wave-propagation effects have been fully corrected or attenuated before inversion but these requirements can hardly be satisfied in practice. Using a one-dimensional(1 D) earth model, the reflectivity method can simulate the full wavefield response of seismic waves. Therefore, we propose a nonlinear multicomponent prestack AVA joint inversion method based on the vectorized reflectivity method, which uses a fast nondominated sorting genetic algorithm(NSGA II) to optimize the nonlinear multiobjective function to estimate multiple parameters, such as P-wave velocity, S-wave velocity, and density. This approach is robust because it can simultaneously cope with more than one objective function without introducing weight coefficients. Model tests prove the effectiveness of the proposed inversion method. Based on the inversion results, we find that the nonlinear prestack AVA joint inversion using the reflectivity method yields more accurate inversion results than the inversion by using the exact Zoeppritz equation when the wave-propagation effects of transmission loss and internal multiples are not completely corrected.
文摘The inverse problem in geophysics is to infer the vertical structure from the observed data. The crucial assumption in deriving inversion algorithms obtained for different elementary layer structures. Much of the previous work on this problem for the case of plane wave at normal incidence has consisted of deriving a Schrodinger equation from the basic acoustic and stress-strain equations, and then reconstructing the potential appearing in this equation by using the Gelfand-Levitan procedure. We shall be concerned with structures in which the unknown coefficient has jump discontinuities. Here the unknown potential in the corresponding Gelfand-Levitan framework is highly singular, so much so that the theory breaks down. In this paper it is presented an inversion algorithm based on the Riccati equation,which avoids to solve the problem of singularity. The model experiment was conducted in a sewage pool of a factory. The final result of inversion agrecd well with the direct experimental observation.
基金Science and Technology Development Fund of the Macao SAR under research grant SKL-IOTSC-2018-2020the Research Committee of University of Macao under Research Grant MYRG2016-00029-FST。
文摘Surface wave methods have received much attention due to their efficient, flexible and convenient characteristics. However, there are still critical issues regarding a key step in surface wave inversion. In most existing methods, the number of layers is assumed to be known prior to the process of inversion. However, improper assignment of this parameter leads to erroneous inversion results. A Bayesian nonparametric method for Rayleigh wave inversion is proposed herein to address this problem. In this method, each model class represents a particular number of layers with unknown S-wave velocity and thickness of each layer. As a result, determination of the number of layers is equivalent to selection of the most applicable model class. Regarding each model class, the optimization search of S-wave velocity and thickness of each layer is implemented by using a genetic algorithm. Then, each model class is assessed in view of its efficiency under the Bayesian framework and the most efficient class is selected. Simulated and actual examples verify that the proposed Bayesian nonparametric approach is reliable and efficient for Rayleigh wave inversion, especially for its capability to determine the number of layers.
