Denoising of gamma-ray spectrum by optimized wavelet thresholding based on modified genetic algorithm in carbon/oxygen logging
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In order to reduce noise in gamma-ray spectrum measured by carbon/oxygen logging instrument, an improved wavelet thresholding algorithm was proposed in this paper. This algorithm established a thresholding function with an adjustable parameter, which could obtain various filtering performances by means of different parameters, and then a modified genetic algorithm combined with opposition-based learning theory was put forward to optimize the parameter and wavelet thresholds. By using Monte Carlo simulation, the objective function of the modified genetic algorithm was determined. Finally, the actual measured spectra processing results of the optimized wavelet thresholding algorithm was compared with traditional thresholding algorithms and other filtering algorithms, and the effectiveness of the proposed algorithm was verified based on signal-to-noise ratio index.
KeywordsSpectral noise reduction Wavelet thresholding Genetic algorithm Carbon/oxygen logging
This work was supported by the Scientific Research and Technology Development Project of CNPC (No. 2016D-3802) and the author thanks for the support by the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University.
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Conflict of interest
The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
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