基于优化小波阈值的碳氧比能谱处理方法
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摘要
由于放射性统计涨落以及仪器自身稳定性的影响,碳氧比测井伽马能谱中会包含一定量的噪声,影响碳氧比值的计算精度,在资料解释时需要对能谱进行滤波。传统小波阈值算法进行信号滤波处理时采用的软硬阈值函数存在均方根误差偏大与伪吉布斯效应等问题。提出了一种具有调节参数的改进阈值函数,根据不同的参数取值实现不同的滤波效果。为了获得改进阈值函数中的最佳参数,提出了基于反向学习策略的遗传算法,并用于对阈值函数中的参数进行优化。与经典遗传算法相比,改进遗传算法具有更好的收敛性与寻优精度。采用优化后的阈值函数对能谱进行滤波处理得到的数据和软硬阈值函数的处理结果相比具有更小的均方根误差,能谱中的噪声也得到了有效压制,表明所提算法具有更好的适用性。
Due to the fluctuation of radioactivity statistics and the influence of the stability of the instrument, the gamma energy spectrum measured by the C/O logging instrument would contain a certain amount of noise, which can affect the calculation accuracy of the C/O, so it is necessary to filter the energy spectrum during the process of data interpretation. The traditional wavelet threshold algorithm used the soft and hard threshold function for signal filtering, but the function has the problems of large mean square root error and pseudo-Gibbs effect. In order to solve these problems, an improved threshold function which can adjust parameters was proposed in this paper, which can be used according to different parameters to realize different filtering effects. Meanwhile, to obtain the best parameters in the improved threshold function, this paper also proposed a genetic algorithm based on backward learning strategy, which was used to optimize the parameters in the threshold function. Compared with the classical genetic algorithm, the improved genetic algorithm has better convergence and optimization accuracy. By comparison, it was found that compared with the processing results of the soft and hard threshold function, the data obtained by the optimized threshold function had a smaller mean square root error, and the noise in the energy spectrum was effectively suppressed, which indicated that the proposed algorithm has better applicability.
Due to the fluctuation of radioactivity statistics and the influence of the stability of the instrument, the gamma energy spectrum measured by the C/O logging instrument would contain a certain amount of noise, which can affect the calculation accuracy of the C/O, so it is necessary to filter the energy spectrum during the process of data interpretation. The traditional wavelet threshold algorithm used the soft and hard threshold function for signal filtering, but the function has the problems of large mean square root error and pseudo-Gibbs effect. In order to solve these problems, an improved threshold function which can adjust parameters was proposed in this paper, which can be used according to different parameters to realize different filtering effects. Meanwhile, to obtain the best parameters in the improved threshold function, this paper also proposed a genetic algorithm based on backward learning strategy, which was used to optimize the parameters in the threshold function. Compared with the classical genetic algorithm, the improved genetic algorithm has better convergence and optimization accuracy. By comparison, it was found that compared with the processing results of the soft and hard threshold function, the data obtained by the optimized threshold function had a smaller mean square root error, and the noise in the energy spectrum was effectively suppressed, which indicated that the proposed algorithm has better applicability.
引文
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[2] JR W G,PRATI E,PEMPER R,et al.Introduction of a new through-tubing multifunction pulsed neutron instrument [C]//SPE56803,SPE ATCE,1999.
[3] 李伦辉,何剑锋,王芹,等.改进小波阈值方法对γ能谱去噪的研究 [J].原子能科学技术,2016,50(7):1279-1283.
[4] DONOHO D,JOHNSTONE I.Adapting to unknown smoothness via wavelet shrinkage [J].Publications of the American Statistical Association,1995,90(432):1200-1224.
[5] 潘泉,孟晋丽,张磊,等.小波滤波方法及应用 [J].电子与信息学报,2007,29(1):236-242.
[6] 杨祎罡,王汝赡,李元景.利用小波滤波方法对γ能谱进行处理 [J].核技术,2002,25(4):241-246.
[7] DONOHO D L.De-noising by soft-thresholding [J].IEEE Transactions on Information Theory,2002,41(3):613-627.
[8] VARGAS R N.Seismic trace noise reduction by wavelets and double threshold estimation [J].Iet Signal Processing,2018,11(9):1069-1075.
[9] ZHANG X P,DESAI M D.Adaptive de-noising based on SURE risk [J].IEEE Signal Processing Letters,2002,5(10):265-267.
[10] 关新平,刘冬,唐英干,等.基于局部方差的模糊小波阈值图像去噪 [J].系统工程与电子技术,2006,28(5):650-653.
[11] 王尧葵,于翔,王珩,等.利用小波阈值法提高地震勘探信号的信噪比 [J].地学前缘,2017,24(3):319-324.
[12] TIZHOOSH H R.Opposition-based learning:a new scheme for machine intelligence [C]//International Conference on Computational Intelligence for Modelling,Control and Automation and International Conference on Intelligent Agents,Web Technologies and Internet Commerce Vol.IEEE Computer Society,2005:695-701.
[13] 夏学文,刘经南,高柯夫,等.具备反向学习和局部学习能力的粒子群算法 [J].计算机学报,2015(7):1397-1407.
[14] 谢承旺,许雷,赵怀瑞,等.应用精英反向学习的多目标烟花爆炸算法 [J].电子学报,2016,44(5):1180-1188.
[15] WANG H.Opposition-based barebones particle swarm for constrained nonlinear optimization problems [J].Mathematical Problems in Engineering,2012:1-12.
[16] HOLLAND J H.Adaptation in natural and artificial systems:an introductory analysis with applications to biology,control,and artificial intelligence [J].Ann Arbor,1975,6(2):126-137.
[17] 赵军,祁兴中,宋帆,等.遗传算法在测井识别凝析气藏中的应用 [J].测井技术,2006,30(4):313-316.
[18] 方中于,王丽萍,杜家元,等.基于混合智能优化算法的非线性AVO反演 [J].石油地球物理勘探,2017,52(4):797-804.
[19] 陶珂,朱建军.小波去噪质量评价方法的对比研究 [J].大地测量与地球动力学,2012,32(2):128-133.
[20] 苏睿,王仲奇.γ能谱小波降噪控制研究 [J].原子能科学技术,2014,48(7):1309-1313.
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