3 resultados para eigenvalues and eigenfunctions
em Universidade Federal do Rio Grande do Norte(UFRN)
Resumo:
In geophysics there are several steps in the study of the Earth, one of them is the processing of seismic records. These records are obtained through observations made on the earth surface and are useful for information about the structure and composition of the inaccessible parts in great depths. Most of the tools and techniques developed for such studies has been applied in academic projects. The big problem is that the seismic processing power unwanted, recorded by receivers that do not bring any kind of information related to the reflectors can mask the information and/or generate erroneous information from the subsurface. This energy is known as unwanted seismic noise. To reduce the noise and improve a signal indicating a reflection, without losing desirable signals is sometimes a problem of difficult solution. The project aims to get rid of the ground roll noise, which shows a pattern characterized by low frequency, low rate of decay, low velocity and high amplituds. The Karhunen-Loève Transform is a great tool for identification of patterns based on the eigenvalues and eigenvectors. Together with the Karhunen-Loève Transform we will be using the Singular Value Decomposition, since it is a great mathematical technique for manipulating data
Resumo:
This research work aims to make a study of the algebraic theory of matrix monic polynomials, as well as the definitions, concepts and properties with respect to block eigenvalues, block eigenvectors and solvents of P(X). We investigte the main relations between the matrix polynomial and the Companion and Vandermonde matrices. We study the construction of matrix polynomials with certain solvents and the extention of the Power Method, to calculate block eigenvalues and solvents of P(X). Through the relationship between the dominant block eigenvalue of the Companion matrix and the dominant solvent of P(X) it is possible to obtain the convergence of the algorithm for the dominant solvent of the matrix polynomial. We illustrate with numerical examples for diferent cases of convergence.
Resumo:
In geophysics there are several steps in the study of the Earth, one of them is the processing of seismic records. These records are obtained through observations made on the earth surface and are useful for information about the structure and composition of the inaccessible parts in great depths. Most of the tools and techniques developed for such studies has been applied in academic projects. The big problem is that the seismic processing power unwanted, recorded by receivers that do not bring any kind of information related to the reflectors can mask the information and/or generate erroneous information from the subsurface. This energy is known as unwanted seismic noise. To reduce the noise and improve a signal indicating a reflection, without losing desirable signals is sometimes a problem of difficult solution. The project aims to get rid of the ground roll noise, which shows a pattern characterized by low frequency, low rate of decay, low velocity and high amplituds. The Karhunen-Loève Transform is a great tool for identification of patterns based on the eigenvalues and eigenvectors. Together with the Karhunen-Loève Transform we will be using the Singular Value Decomposition, since it is a great mathematical technique for manipulating data