Estudo e aplicação da transformada de Fourier na regularização de dados sísmicos na exploração de petróleo

In the oil prospection research seismic data are usually irregular and sparsely sampled along the spatial coordinates due to obstacles in placement of geophones. Fourier methods provide a way to make the regularization of seismic data which are efficient if the input data is sampled on a regular grid. However, when these methods are applied to a set of irregularly sampled data, the orthogonality among the Fourier components is broken and the energy of a Fourier component may "leak" to other components, a phenomenon called "spectral leakage". The objective of this research is to study the spectral representation of irregularly sampled data method. In particular, it will be presented the basic structure of representation of the NDFT (nonuniform discrete Fourier transform), study their properties and demonstrate its potential in the processing of the seismic signal. In this way we study the FFT (fast Fourier transform) and the NFFT (nonuniform fast Fourier transform) which rapidly calculate the DFT (discrete Fourier transform) and NDFT. We compare the recovery of the signal using the FFT, DFT and NFFT. We approach the interpolation of seismic trace using the ALFT (antileakage Fourier transform) to overcome the problem of spectral leakage caused by uneven sampling. Applications to synthetic and real data showed that ALFT method works well on complex geology seismic data and suffers little with irregular spatial sampling of the data and edge effects, in addition it is robust and stable with noisy data. However, it is not as efficient as the FFT and its reconstruction is not as good in the case of irregular filling with large holes in the acquisition.

Estudo e aplicação da transformada de Fourier na regularização de dados sísmicos na exploração de petróleo

In the oil prospection research seismic data are usually irregular and sparsely sampled along the spatial coordinates due to obstacles in placement of geophones. Fourier methods provide a way to make the regularization of seismic data which are efficient if the input data is sampled on a regular grid. However, when these methods are applied to a set of irregularly sampled data, the orthogonality among the Fourier components is broken and the energy of a Fourier component may "leak" to other components, a phenomenon called "spectral leakage". The objective of this research is to study the spectral representation of irregularly sampled data method. In particular, it will be presented the basic structure of representation of the NDFT (nonuniform discrete Fourier transform), study their properties and demonstrate its potential in the processing of the seismic signal. In this way we study the FFT (fast Fourier transform) and the NFFT (nonuniform fast Fourier transform) which rapidly calculate the DFT (discrete Fourier transform) and NDFT. We compare the recovery of the signal using the FFT, DFT and NFFT. We approach the interpolation of seismic trace using the ALFT (antileakage Fourier transform) to overcome the problem of spectral leakage caused by uneven sampling. Applications to synthetic and real data showed that ALFT method works well on complex geology seismic data and suffers little with irregular spatial sampling of the data and edge effects, in addition it is robust and stable with noisy data. However, it is not as efficient as the FFT and its reconstruction is not as good in the case of irregular filling with large holes in the acquisition.

Análise de superfícies seletivas de freqüência para aplicações em ondas milimétricas

This work consists in the development of a theoretical and numerical analysis for frequency selective surfaces (FSS) structures with conducting patch elements, such as rectangular patches, thin dipoles and cross dipoles, on anisotropic dielectric substrates. The analysis is developed for millimeter wave band applications. The analytical formulation is developed in the spectral domain, by using a rigorous technique known as equivalent transmission line method, or immitance approach. The numerical analysis is completed through the use of the Galerkin's technique in the Fourier transform domain, using entire-domain basis functions. In the last decades, several sophisticated analytical techniques have been developed for FSS structure applications. Within these applications, it can be emphasized the use of FSS structures on reflecting antennas and bandpass radomes. In the analysis, the scattered fields of the FSS geometry are related to the surface induced currents on the conducting patches. After the formulation of the scattering problem, the numerical solution is obtained by using the moment method. The choice of the basis functions plays a very important role in the numerical efficiency of the numerical method, once they should provide a very good approximation to the real current distributions on the FSS analyzed structure. Thereafter, the dyadic Green's function components are obtained in order to evaluate the basis functions unknown coefficients. To accomplish that, the Galerkin's numerical technique is used. Completing the formulation, the incident fields are determined through the incident potential, and as a consequence the FSS transmission and reflection characteristics are determined, as function of the resonant frequency and structural parameters. The main objective of this work was to analyze FSS structures with conducting patch elements, such as thin dipoles, cross dipoles and rectangular patches, on anisotropic dielectric substrates, for high frequency applications. Therefore, numerical results for the FSS structure main characteristics were obtained in the millimeter wave bando Some of these FSS characteristics are the resonant

Análise sísmica usando transformada de Curvelet

Oil prospecting is one of most complex and important features of oil industry Direct prospecting methods like drilling well logs are very expensive, in consequence indirect methods are preferred. Among the indirect prospecting techniques the seismic imaging is a relevant method. Seismic method is based on artificial seismic waves that are generated, go through the geologic medium suffering diffraction and reflexion and return to the surface where they are recorded and analyzed to construct seismograms. However, the seismogram contains not only actual geologic information, but also noise, and one of the main components of the noise is the ground roll. Noise attenuation is essential for a good geologic interpretation of the seismogram. It is common to study seismograms by using time-frequency transformations that map the seismic signal into a frequency space where it is easier to remove or attenuate noise. After that, data is reconstructed in the original space in such a way that geologic structures are shown in more detail. In addition, the curvelet transform is a new and effective spectral transformation that have been used in the analysis of complex data. In this work, we employ the curvelet transform to represent geologic data using basis functions that are directional in space. This particular basis can represent more effectively two dimensional objects with contours and lines. The curvelet analysis maps real space into frequencies scales and angular sectors in such way that we can distinguish in detail the sub-spaces where is the noise and remove the coefficients corresponding to the undesired data. In this work we develop and apply the denoising analysis to remove the ground roll of seismograms. We apply this technique to a artificial seismogram and to a real one. In both cases we obtain a good noise attenuation

Obtenção e Caracterização Físico-Química do Sistema Compósito PEG-TiO2

Hybrid systems formed from polymers and transition metals have now their physical and chemical properties extensively investigated for use in electronic devices. In this work, Titanium Dioxide (TiO2) from the precursor of titanium tetrabutoxide and the composite system Poly(Ethylene Glycol)-Titanium Dioxide (TiO2-PEG) were synthesized by sol-gel method. The PEG as acquired and TiO2 and composites powders were analyzed by X-Ray Diffraction (XRD), Spectroscopy in the Infrared region with Fourier transform (IRFT), Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM) and Electrochemical Impedance Spectroscopy (EIS). In the XRD analysis were observed in the TiO2 crystal faces of one of its polymorphs - anatase phase, crystal planes in Poly (Ethylene Glycol) with considerable intensity and in the composite systems the mixture of crystal faces of their precursors isolated and reduction of crystallinity. The TG / DTG suggested increasing the thermal instability of PEG in the composite powders as TiO2 is incorporated into the system. Spectral analysis presented in the infrared overlapping bands for the polymer and metal oxide, reducing the intensity of symmetric stretching of ligand groups in the main chain polymer and angular deformations; were observed using SEM micrographs of the morphological changes suffered by composite systems with the variation of the oxide concentration. Analyses by impedance spectroscopy indicated that the increased conductivity in composite occurs in line with the addition of the metal oxide concentration in the composite system

Classificador máquina de suporte vetorial com análise de Fourier aplicada em dados de EEG e EMG

The classifier support vector machine is used in several problems in various areas of knowledge. Basically the method used in this classier is to end the hyperplane that maximizes the distance between the groups, to increase the generalization of the classifier. In this work, we treated some problems of binary classification of data obtained by electroencephalography (EEG) and electromyography (EMG) using Support Vector Machine with some complementary techniques, such as: Principal Component Analysis to identify the active regions of the brain, the periodogram method which is obtained by Fourier analysis to help discriminate between groups and Simple Moving Average to eliminate some of the existing noise in the data. It was developed two functions in the software R, for the realization of training tasks and classification. Also, it was proposed two weights systems and a summarized measure to help on deciding in classification of groups. The application of these techniques, weights and the summarized measure in the classier, showed quite satisfactory results, where the best results were an average rate of 95.31% to visual stimuli data, 100% of correct classification for epilepsy data and rates of 91.22% and 96.89% to object motion data for two subjects.