A method for simultaneous determination of mixed model parameters,which have different physical dimensions or different responses to data,is presented.Mixed parameter estimation from observed data within a single mode...A method for simultaneous determination of mixed model parameters,which have different physical dimensions or different responses to data,is presented.Mixed parameter estimation from observed data within a single model space shows instabilities and trade-offs of the solutions. We separate the model space into N-subspaces based on their physical properties or computational convenience and solve the N-subspaces systems by damped least-squares and singular-value decomposition. Since the condition number of each subsystem is smaller than that of the single global system,the approach can greatly increase the stability of the inversion. We also introduce different damping factors into the subsystems to reduce the tradeoffs between the different parameters. The damping factors depend on the conditioning of the subsystems and may be adequately chosen in a range from 0.1 % to 10 % of the largest singular value. We illustrate the method with an example of simultaneous determination of source history,source geometry,and hypocentral location from regional seismograms,although it is applicable to any geophysical inversion.展开更多
In this paper, by using the fast iterative method of mode decomposition[12], source range-depth localization performance of MMP for three kinds of vertical array (short, sparse and short-sparse arrays) in shallow wate...In this paper, by using the fast iterative method of mode decomposition[12], source range-depth localization performance of MMP for three kinds of vertical array (short, sparse and short-sparse arrays) in shallow water with a downward refraction sound-speed profile in the surnmertime is discussed; the accuracy of mode decomposition is measured by its rootmean-square error, RMS. The numerical results illustrate that the accuracy of source range and depth estimation are raised and the sidelobes are effectively suppressed. The short-sparse vertical array not only has shorter length and fewer hydrophones, but also can be applied to the different sea areas with various depth, so it is a practical type of vertical arrny in the engineering project of the passive source localization.展开更多
基金supported by Innovation Project of Chinese Academy of Sciences
文摘A method for simultaneous determination of mixed model parameters,which have different physical dimensions or different responses to data,is presented.Mixed parameter estimation from observed data within a single model space shows instabilities and trade-offs of the solutions. We separate the model space into N-subspaces based on their physical properties or computational convenience and solve the N-subspaces systems by damped least-squares and singular-value decomposition. Since the condition number of each subsystem is smaller than that of the single global system,the approach can greatly increase the stability of the inversion. We also introduce different damping factors into the subsystems to reduce the tradeoffs between the different parameters. The damping factors depend on the conditioning of the subsystems and may be adequately chosen in a range from 0.1 % to 10 % of the largest singular value. We illustrate the method with an example of simultaneous determination of source history,source geometry,and hypocentral location from regional seismograms,although it is applicable to any geophysical inversion.
文摘In this paper, by using the fast iterative method of mode decomposition[12], source range-depth localization performance of MMP for three kinds of vertical array (short, sparse and short-sparse arrays) in shallow water with a downward refraction sound-speed profile in the surnmertime is discussed; the accuracy of mode decomposition is measured by its rootmean-square error, RMS. The numerical results illustrate that the accuracy of source range and depth estimation are raised and the sidelobes are effectively suppressed. The short-sparse vertical array not only has shorter length and fewer hydrophones, but also can be applied to the different sea areas with various depth, so it is a practical type of vertical arrny in the engineering project of the passive source localization.
