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Previous issue date: 2011-04-05 === Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior === In the Hydrocarbon exploration activities, the great enigma is the location of the deposits. Great
efforts are undertaken in an attempt to better identify them, locate them and at the same time,
enhance cost-effectiveness relationship of extraction of oil. Seismic methods are the most widely
used because they are indirect, i.e., probing the subsurface layers without invading them. Seismogram
is the representation of the Earth s interior and its structures through a conveniently
disposed arrangement of the data obtained by seismic reflection. A major problem in this representation
is the intensity and variety of present noise in the seismogram, as the surface bearing
noise that contaminates the relevant signals, and may mask the desired information, brought by
waves scattered in deeper regions of the geological layers. It was developed a tool to suppress
these noises based on wavelet transform 1D and 2D. The Java language program makes the
separation of seismic images considering the directions (horizontal, vertical, mixed or local) and
bands of wavelengths that form these images, using the Daubechies Wavelets, Auto-resolution
and Tensor Product of wavelet bases. Besides, it was developed the option in a single image,
using the tensor product of two-dimensional wavelets or one-wavelet tensor product by identities.
In the latter case, we have the wavelet decomposition in a two dimensional signal in a single
direction. This decomposition has allowed to lengthen a certain direction the two-dimensional
Wavelets, correcting the effects of scales by applying Auto-resolutions. In other words, it has been
improved the treatment of a seismic image using 1D wavelet and 2D wavelet at different stages of
Auto-resolution. It was also implemented improvements in the display of images associated with
breakdowns in each Auto-resolution, facilitating the choices of images with the signals of interest
for image reconstruction without noise. The program was tested with real data and the results
were good === Na atividade explorat?ria de hidrocarbonetos a grande inc?gnita ? a localiza??o das jazidas.
Grandes esfor?os s?o empreendidos na tentativa de melhor identific?-las, localiz?-las e, ao mesmo
tempo, otimizar a rela??o custo-benef?cio da extra??o de Petr?leo. Os m?todos s?smicos s?o os
mais utilizados pelo fato de serem indiretos, isto ?, sondam as camadas de subsuperf?cie sem
invadi-las.
O sismograma ? a representa??o do interior da Terra e de suas estruturas atrav?s de um arranjo
convenientemente disposto dos dados obtidos por meio da s?smica de reflex?o. Um grande
problema nessa representa??o ? a intensidade e variedade de ru?dos presentes no sismograma,
como o ru?do de rolamento superficial que contamina os sinais relevantes e pode mascarar as
informa??es desejadas, trazidas por ondas espalhadas em regi?es mais profundas das camadas
geol?gicas.
Desenvolvemos uma ferramenta para suprimir estes ru?dos que usa transformadas Wavelets 1D
e 2D. O programa, em linguagem Java, faz a separa??o das imagens S?smicas considerando as
dire??es (horizontal, vertical e mistas ou locais) e faixas de comprimentos de ondas que formam
essas imagens, usando Wavelets de Daubechies, Autoresolu??o que duplica o comprimento das
ondas e Produto Tensorial das bases de Wavelets.
Desenvolvemos a op??o, em uma mesma imagem, de usar o produto tensorial de Wavelets de
dimens?o 2 ou produto tensorial de Wavelets de dimens?o 1 pelas identidades. Neste ?ltimo
caso, temos a Decomposi??o em Wavelets de um sinal bidimensional em uma ?nica dire??o. Esta
decomposi??o permite alongar numa determinada dire??o as Wavelets bidimensionais, corrigindo
efeitos de escalas ao aplicarmos Autoresolu??es. Em outras palavras, aperfei?oamos o tratamento
de uma imagem s?smica, usandoWavelet 1D eWavelet 2D em etapas diferentes de Autoresolu??es.
Tamb?m implementamos melhorias na visualiza??o das imagens associadas ?s decomposi??es
em cada Autoresolu??o, facilitando as escolhas das imagens com os sinais de interesse para
reconstru??o da imagem sem os ru?dos. O programa foi testado com dados reais e os resultados
obtidos foram de boa qualidade
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