Denoising of gamma-ray spectrum by optimized wavelet thresholding based on modified genetic algorithm in carbon/oxygen logging

Abstract

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.