| Citation: |
LIU Gang, GONG Yuquan, ZHANG He, LIANG Haibo. Infrared Spectral Noise Reduction Algorithm Based on Wavelet Transform Optimized EEMD Combined with SG[J]. Infrared Technology , 2024, 46(12): 1453-1458.
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Infrared spectral gas analysis technology has gradually become the main analytical method for gas logging owing to its advantages of non-pollution, high detection efficiency, and accurate analysis. However, because of factors, such as numerous types of hydrocarbon gases in the formation fluid and a large concentration range span, the measured spectral data are complicated. Therefore, the pre-processing of the spectral data is crucial as it directly impacts the accuracy of the measurement results. Noise is a significant interference factor, and improving the noise reduction process for the spectral data is crucial. To solve this problem, this study proposes a wavelet transform optimized ensemble empirical mode decomposition (EEMD) combined with Savitzky-Golay filtering (S-G) for the infrared spectral noise reduction algorithm. This algorithm first uses EEMD to decompose the signal to obtain a set of IMF components. It then uses wavelet transform for wavelet threshold denoising on the IMF components. Finally, the denoised IMF components are reconstructed, followed by S-G. The experimental results show that the algorithm can not only remove the Gaussian white noise and impulse noise in the absorption spectrum but also improve the smoothness index of the absorption spectrum and enhance the accuracy of logging gas detection.
| [1] |
荆文峰, 阎荣辉, 陈中普, 等. 红外光谱录井技术在长庆油田的创新应用[J]. 录井工程, 2019, 30(3): 124-130.
JING Wenfeng, YAN Ronghui, CHEN Zhongpu, et al. Innovative application of infrared spectroscopy logging technology in the Changqing oilfield[J]. Well Logging Engineering, 2019, 30(3): 124-130.
|
| [2] |
刘志宏, 邓波, 周玉荣, 等. 红外光谱预处理中去噪的研究[J]. 光谱实验室, 2006(4): 815-819. DOI: 10.3969/j.issn.1004-8138.2006.04.041
LIU Zhihong, DENG Bo, ZHOU Yurong, et al. Research on denoising in infrared spectroscopy preprocessing [J]. Chinese Journal of Spectroscopy Laboratory, 2006(4): 815-819. DOI: 10.3969/j.issn.1004-8138.2006.04.041
|
| [3] |
罗方. 基于小波变换的信号去噪研究[J]. 科技风, 2012(16): 67. DOI: 10.19392/j.cnki.1671-7341.2012.16.049.
LUO Fang. Research on signal denoising based on wavelet transform[J]. Science and Technology Wind, 2012(16): 67. DOI: 10.19392/j.cnki.1671-7341.2012.16.049.
|
| [4] |
王书涛, 曾秋菊, 宋浩兵, 等. 基于SVM滤波器的吸收式甲烷检测的信号去噪方法[J]. 中国激光, 2014, 41(9): 271-275.
WANG Shutao, ZENG Qiuju, SONG Haobing, et al. Signal denoising method for absorption-based methane detection using SVM filters[J]. Chinese Journal of Lasers, 2014, 41(9): 271-275.
|
| [5] |
HUANG N E, ZHENG S, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1998: 903-995.
|
| [6] |
WU Z H, HUANG N E. Ensemble empirical mode decomposition: a noise assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41. DOI: 10.1142/S1793536909000047
|
| [7] |
刘铭华. 基于空芯光子带隙光纤的全光纤甲烷检测系统研究[D]. 秦皇岛: 燕山大学, 2015.
LIU Minghua. Research on All-Fiber Methane Detection System Based on Hollow-Core Photonic Band Gap Fiber[D]. Qinhuangdao: Yanshan University, 2015.
|
| [8] |
王书涛, 车先阁, 王志芳, 等. 基于小波优化EEMD的甲烷浓度检测信号研究[J]. 光学技术, 2019, 45(3): 269-274.
WANG Shutao, CHE Xiange, WANG Zhifang, et al. Study on methane concentration detection signals based on wavelet-optimized EEMD[J]. Optical Techniques, 2019, 45(3): 269-274.
|
| [9] |
秦亚辉, 冯景辉, 陈立定. 基于小波变换的信号去噪方法研究[J]. 信息技术, 2010, 34(1): 53-57.
QIN Yahui, FENG Jinghui, CHEN Liding. Study on signal denoising methods based on wavelet transform [J]. Information Technology, 2010, 34(1): 53-57.
|
| [10] |
郭飞, 王玉兰. 小波变换与匹配滤波耦合的激光雷达弱信号处理[J]. 激光杂志, 2006(4): 51-52.
GUO Fei, WANG Yulan. Weak signal processing of Lidar based on coupling of wavelet transform and matched filtering [J]. Laser Journal, 2006(4): 51-52.
|
| [11] |
秦瑞霞, 黄毅. 基于阈值函数的小波去噪的研究[J]. 信息通信, 2016(12): 1-3.
QIN Ruixia, HUANG Yi. Research on wavelet denoising based on threshold function[J]. Information Communication, 2016(12): 1-3.
|
| [12] |
赵肖宇. 基于EMD和EEMD的自适应光谱预处理方法及其应用研究[D]. 秦皇岛: 燕山大学, 2015.
ZHAO Xiaoyu. Adaptive Spectral Preprocessing Methods Based on EMD and EEMD and Their Applications[D]. Qinhuangdao: Yanshan University, 2015.
|
| [13] |
ZHOU Y, TAO T, MEI X, et al. Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method[J]. Robotics and Computer-Integrated Manufacturing, 2011, 27(4): 785-793 http://www.xueshufan.com/publication/2052675069
|
| [14] |
王玉静, 康守强, 张云, 等. 基于集合经验模态分解敏感固有模态函数选择算法的滚动轴承状态识别方法[J]. 电子与信息学报, 2014, 36(3): 595-600.
WANG Yujing, KANG Shouqiang, ZHANG Yun, et al. Rolling bearing condition recognition method based on sensitive intrinsic mode function selection algorithm using ensemble empirical mode decomposition [J]. Journal of Electronics & Information Technology, 2014, 36(3): 595-600.
|
| [15] |
杨帆, 王鹏, 张宁超, 等. 一种基于小波变换的改进滤波算法及其在光谱去噪方面的应用[J]. 国外电子测量技术, 2020, 39(8): 98-104.
YANG Fan, WANG Peng, ZHANG Ningchao, et al. An improved filtering algorithm based on wavelet transform and its application in spectral denoising [J]. Foreign Electronic Measurement Technology, 2020, 39(8): 98-104.
|
| [16] |
李亢, 杨绍清. 基于Savitzky-Golay算法的图像平滑去噪[J]. 数据采集与处理, 2010, 25(S1): 72-74.
LI Kang, YANG Shaoqing. Image smoothing and denoising based on the Savitzky-Golay algorithm[J]. Data Acquisition and Processing, 2010, 25(S1): 72-74.
|
| [17] |
雷林平. 基于Savitzky-Golay算法的曲线平滑去噪[J]. 电脑与信息技术, 2014, 22(5): 30-31.
LEI Linping. Curve smoothing and denoising based on the Savitzky-Golay algorithm [J]. Computer and Information Technology, 2014, 22(5): 30-31.
|
| [18] |
蔡天净, 唐瀚. Savitzky-Golay平滑滤波器的最小二乘拟合原理综述[J]. 数字通信, 2011, 38(1): 63-68, 82.
CAI Tianjing, TANG Han. A review of the least squares fitting principle of the Savitzky-Golay smoothing filter[J]. Digital Communication, 2011, 38(1): 63-68, 82.
|
| [19] |
张林, 张志杰, 张华. 小波变换在压力传感器输出信号去噪中的应用[J]. 仪表技术与传感器, 2018(4): 10-13.
ZHANG Lin, ZHANG Zhijie, ZHANG Hua. Application of wavelet transform in denoising of pressure sensor output signals [J]. Instrument Technology and Sensors, 2018(4): 10-13.
|
| [20] |
王志芳, 王书涛, 王贵川, 等. 基于小波优化EEMD的二氧化硫检测[J]. 计量学报, 2020, 41(6): 752-758.
WANG Zhifang, WANG Shutao, WANG Guichuan, et al. Sulfur dioxide detection based on wavelet-optimized EEMD[J]. Acta Metrologica Sinica, 2020, 41(6): 752-758.
|
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