Discrete wavelet transform-based multiresolution analysis and spectral enhancement to characterize heavy oil reservoirs in the southern Gulf of Mexico region

2016 ◽  
Vol 4 (4) ◽  
pp. T497-T505 ◽  
Author(s):  
Erik Camacho-Ramírez ◽  
Ernesto Gonzalez-Flores ◽  
José Oscar Campos-Enriquez
Keyword(s):  
Wavelet Transform ◽  
Gulf Of Mexico ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Heavy Oil ◽  
Resolution Enhancement ◽  
Discrete Wavelet ◽  
Seismic Resolution ◽  
Spectral Enhancement ◽  
Southern Gulf Of Mexico

In Neogene geologic settings, seismic resolution plays an important role in the static characterization of large stratigraphically complex reservoirs. Attenuation of high frequencies due to absorption and scattering of waves propagating through the subsurface contributes to the loss of seismic resolution, resulting, in particular, to a poor delineation of thin beds. This case history comprised the resolution enhancement of seismic data from one petroleum field located in the southern Gulf of Mexico. We applied discrete wavelet transform-based multiresolution analysis to identify frequency components in the data that required spectral enhancement. The resulting enhanced power spectrum contains a higher frequency content, which made it possible to identify new heavy oil (11°–23° API) reservoirs located in thin sandstones of deltaic sedimentary environments. The methodology used here helped us to cope with the attenuation problem.

scholarly journals Wavelet Transform Based Fault Identification and Reconfiguration for a Reduced Switch Multilevel Inverter Fed Induction Motor Drive

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1023
Author(s):  
Arigela Satya Veerendra ◽  
Akeel A. Shah ◽  
Mohd Rusllim Mohamed ◽  
Chavali Punya Sekhar ◽  
Puiki Leung
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Short Circuit ◽  
Open Circuit ◽  
Multilevel Inverter ◽  
Fault Identification ◽  
Discrete Wavelet ◽  
The Novel ◽  
Inverter Topology

The multilevel inverter-based drive system is greatly affected by several faults occurring on switching elements. A faulty switch in the inverter can potentially lead to more losses, extensive downtime and reduced reliability. In this paper, a novel fault identification and reconfiguration process is proposed by using discrete wavelet transform and auxiliary switching cells. Here, the discrete wavelet transform exploits a multiresolution analysis with a feature extraction methodology for fault identification and subsequently for reconfiguration. For increasing the reliability, auxiliary switching cells are integrated to replace faulty cells in a proposed reduced-switch 5-level multilevel inverter topology. The novel reconfiguration scheme compensates open circuit and short circuit faults. The complexity of the proposed system is lower relative to existing methods. This proposed technique effectively identifies and classifies faults using the multiresolution analysis. Furthermore, the measured current and voltage values during fault reconfiguration are close to those under healthy conditions. The performance is verified using the MATLAB/Simulink platform and a hardware model.

scholarly journals IMAGE RESOLUTION ENHANCEMENT OF SATELLITE IMAGES USING DYADIC INTEGER COEFFICIENT WAVELET FILTER FOR WAVELET TRANSFORM

2021 ◽  
Vol 9 (1) ◽  
pp. 971-975
Author(s):  
P.B. Chopade, P. N. Kota
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Satellite Images ◽  
Resolution Enhancement ◽  
Image Resolution ◽  
Integer Coefficient ◽  
Discrete Wavelet ◽  
Wavelet Filter ◽  
Hardware Complexity ◽  
Integer Coefficients

In this paper, image resolution enhancements for satellite images are proposed using dyadic integer coefficients based wavelet filter (DICWF). We proposes a technique in which discrete wavelet transform and stationary wavelet transform using DICWF which is used to obtain a high resolution image and this image is derived from frequency subbands. The satellite images play a very vital role now days in the development of technical aspects which needs to be enhanced. These satellite images are superresolved with the help of dyadic integer coefficient-based wavelet filters, which reduces the hardware complexity and computational difficulties due to the rational and integer coefficients of these filter banks. The value of the peak signal-to-noise ratio (PSNR) of the proposed method and the resultant visual images of the proposed method show the effectiveness of this algorithm over other existing algorithms using discrete wavelet transform. Noise can be minimized by applying thresholding on different frequency subbands which obtained by the application of DICWF to the noisy, blurred input images.

Multi-Resolution Transforms Based Robust Image Enhancement for High Frequency Colour Images

2014 ◽  
Vol 933 ◽  
pp. 762-767
Author(s):  
T. Menakadevi ◽  
J. Arivudainambi ◽  
M. Sulochana
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Frequency ◽  
Resolution Enhancement ◽  
Input Image ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Stationary Wavelet Transform ◽  
Colour Image

An Image Resolution Enhancement Technique based on Interpolation of the high frequency sub-band of colour images obtained by Discrete Wavelet Transform and the input colour image is proposed in this paper. Interpolation determines the intermediate values on the basis of observed values. One of the commonly used interpolation technique is Bicubic Interpolation. The edges are enhanced by introducing an intermediate stage by using Stationary Wavelet Transform. It is designed to overcome the lack of Translation-Invariance of Discrete Wavelet Transform. This is widely used in Signal Denoising and Pattern Recognition. Discrete Wavelet Transform is applied in order to decompose an input colour image into different sub-bands. Then the high frequency sub-bands as well as the input colour image are interpolated separately. The interpolated high frequency sub-bands and the Stationary Wavelet Transform high frequency sub-bands have the same size which means they can be added with each other. The new corrected high frequency sub-bands can be interpolated further for higher enlargement. Then all these sub-bands are combined with interpolated input image for new high resolution image by using Inverse Discrete Wavelet Transform. This has been done by MATLAB. The Peak Signal-Noise Ratio was obtained upto 5dB greater than the conventional and state-of-art image resolution enhancement techniques.

scholarly journals Eliminated aliasing effect on wavelet transform based multiresolution analysis

2016 ◽  
Vol 55 (3) ◽  
Author(s):  
Ernesto González-Flores ◽  
José Oscar Campos-Enríquez ◽  
Erick Camacho-Ramírez ◽  
David Ernesto Rivera-Recillas
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multiresolution Analysis ◽  
Synthetic Data ◽  
Seismic Signal ◽  
Discrete Wavelet ◽  
Frequency Bands ◽  
Analysis Technique ◽  
The Time Domain ◽  
Seismic Signal Processing

Multiresolution analysis, based on the discrete wavelet transform, is here incorporated in seismic signal processing. This analysis technique enables decomposing a seismic signal, in different frequency bands, and thus to analyze the information contained in these frequency bands. Multiresolution analysis allows visualizing in the time domain the information contained in the frequency bands. Wavelets commonly used in the discrete wavelet transform present an overlay between scales, this constitutes an aliasing effect that gives rise to spurious effects. Vaidyanathan wavelet minimizes the overlay between scales. We applied this wavelet to synthetic data and to a 3D seismic cube. Accordingly, spurious effects from aliasing generated by overlay between scales are minimized with the Vaidyanathan wavelet.

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