Spectral decomposition method of adaptive matching pursuit based on empirical mode decomposition dictionary and its application in oil and gas detection
PAN Hui1,2, YIN Xingyao1,2, LI Kun1,2, PEI Song1,2
1. School of Geosciences, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. Laboratory for Marine Mineral Resources, National Laboratory for Marine Science and Techno-logy(Qingdao), Qingdao, Shandong 266071, China
Abstract:When the instantaneous properties of seismic signals are used to determine the sweep range of the atomic dynamic time-frequency parameters, the obtained instantaneous frequency has poor stability and negative values. The calculation of instanta-neous frequency with the help of continuous phase is vulnerable to noise and abnormal frequencies can be found. For these reasons, the empirical mode decomposition (EMD) is introduced into the ma-tching pursuit (MP) algorithm for the first time, and a spectral decomposition method of adaptive MP based on the EMD dictionary is proposed. EMD is regarded as a sparse decomposition method on the basis of an over-complete time-frequency dictionary. In the process of calculating the dominant frequency of the matching atom, the continuous phase-based damped least squares inversion is employed to solve the instantaneous frequency, and a shaping regularization operator is introduced for data smoothing. This method avoids abnormal frequencies, and the obtained instantaneous frequency curve is smoother and more realistic with more prominent high-frequency components. The fast MP method based on the EMD dictionary is applied to the prediction of oil and gas content of the reservoir. In the comparison of instantaneous spectrum profiles of different scales, the low-frequency shadow phenomenon is obvious, and the decomposition efficiency is improved, which further verify the feasibility of the method.
潘辉, 印兴耀, 李坤, 裴松. 基于经验模态分解字典的自适应匹配追踪谱分解方法及其在油气检测中的应用[J]. 石油地球物理勘探, 2021, 56(5): 1117-1129.
PAN Hui, YIN Xingyao, LI Kun, PEI Song. Spectral decomposition method of adaptive matching pursuit based on empirical mode decomposition dictionary and its application in oil and gas detection. Oil Geophysical Prospecting, 2021, 56(5): 1117-1129.
Huang N E, Shen Z, Long S R, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings Mathematical Physical & Engineering Sciences, 1998, 454(1971):903-995.
[2]
杨培杰, 印兴耀, 张广智.希尔伯特-黄变换地震信号时频分析与属性提取[J]. 地球物理学进展, 2007, 22(5):1585-1590.YANG Peijie, YIN Xingyao, ZHANG Guangzhi.Seismic signals time-frequency analysis and attribute extraction based on HHT[J]. Progress in Geophysics, 2007, 22(5):1585-1590.
[3]
Liu D, Chen X.Image denoising based on improved bidimensional empirical mode decomposition thresholding technology[J]. Multimedia Tools and Applications, 2018, 78(6):1-37.
[4]
Huang H, He Y, Yang S, et al.Chaotic image encryption based on bidimensional empirical mode decomposition and double random phase encoding[J]. Multimedia Tools and Applications, 2020, 79(37):28065-28078.
[5]
赵谦, 钱渠, 任志奇. BEMD分解的矿下图像增强算法[J]. 西安科技大学学报, 2020, 40(3):484-491.ZHAO Qian, QIAN Qu, REN Zhiqi.Undermine image enhancement algorithm based on BEMD decomposition[J]. Journal of Xi'an University of Science and Technology, 2020, 40(3):484-491.
[6]
Castagna J P, Sun S, Siegfried R W.Instantaneous spectral analysis:Detection of low frequency shadows associated with hydrocarbons[J]. The Leading Edge, 2003, 22(2):120-127.
[7]
Wang Y H.Seismic time-frequency spectral decomposition by matching pursuit[J]. Geophysics, 2007, 72(1):V13-V20.
李坤, 印兴耀, 宗兆云, 等.基于快速匹配追踪的混合域地震稀疏反演方法[J]. 中国石油大学学报(自然科学版), 2018, 42(1):50-59.LI Kun, YIN Xingyao, ZONG Zhaoyun, et al.Seismic sparse inversion in mixed-domain utilizing fast match-ing pursuit algorithm[J]. Journal of China University of Petroleum(Edition of Natural Seismic), 2018, 42(1):50-59.
[10]
许璐, 吴笑荷, 张明振, 等.基于局部频率约束的动态匹配追踪强反射识别与分离方法[J]. 石油地球物理勘探, 2019, 54(3):587-593.XU Lu, WU Xiaohe, ZHANG Mingzhen, et al.Strong reflection identification and separation based on the local-frequency-constrained dynamic matching pursuit[J]. Oil Geophysical Prospecting, 2019, 54(3):587-593.
[11]
张繁昌, 李传辉.三角洲砂岩尖灭线的地震匹配追踪瞬时谱识别方法[J]. 石油地球物理勘探, 2012, 47(1):82-88.ZHANG Fanchang, LI Chuanhui.Detla fringe line recognition based on seismic matching pursuit instantaneous spectral characteristic[J]. Oil Geophysical Prospecting, 2012, 47(1):82-88.
[12]
Partyka G, Gridley J, Lopez J.Interpretational applications of spectral decomposition in reservoir characterization[J]. The Leading Edge, 1999, 18(3):173-184.
[13]
Mallat S G, Zhang Z. Matching-pursuit with time frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12):3397-3415.
[14]
Liu J, Wu Y, Han D, et al.Time-frequency decomposition based on Ricker wavelet[C]. SEG Technical Program Expanded Abstracts, 2004, 23:1937-1940.
[15]
Liu J, Marfurt K J.Matching pursuit decomposition using Morlet wavelets[C]. SEG Technical Program Expanded Abstracts, 2005, 24:786-789.
[16]
张繁昌, 李传辉, 印兴耀.基于动态匹配子波库的地震数据快速匹配追踪[J]. 石油地球物理勘探, 2010, 45(5):667-673.ZHANG Fanchang, LI Chuanghui, YIN Xingyao.Seismic data fast matching pursuit based on dynamic matching wavelet library[J]. Oil Geophysical Prospecting, 2010, 45(5):667-673.
Boashash B.Estimating and interpreting the instantaneous frequency of a signal. Ⅱ. Algorithms and Applications[J]. IEEE Proceedings, 1992, 80(4):540-568.
[19]
刘汉卿, 张繁昌, 代荣获, 等.动态匹配追踪中利用连续相位求取瞬时频率[J]. 物探与化探, 2015, 39(1):211-216.LIU Hanqing, ZHANG Fanchang, DAI Ronghuo, et al.The calculation of the instantaneous frequency using the continuous phase in dynamic matching pursuit algorithm[J]. Geophysicial and Geochemical Exploration, 2015, 39(1):211-216.
[20]
印兴耀, 许璐, 宗兆云, 等.基于局部频率约束的动态快速匹配追踪方法[J]. 中国石油大学学报(自然科学版), 2018, 42(6):64-71.YIN Xingyao, XU Lu, ZONG Zhaoyun, et al. Dynamic and fast matching pursuit method based on local frequency constraint[J]. Journal of China University of Petroleum(Natural Science Edition), 2018, 42(6):64-71.
[21]
Hou T Y, Shi Z.Data-driven time-frequency analysis[J]. Applied and Computational Harmonic Analysis, 2013, 35(2):284-308.
Mcgowan R, Kuc R.A direct relation between a signal time series and its unwrapped phase[J]. IEEE Transactions on Acoustics Speech & Signal Processing, 2003, 30(5):719-726.
[24]
Taner M T, Koehler F, Sheriff R E.Complex seismic trace analysis[J]. Geophysics, 1979, 44(6):1041-1063.
[25]
高静怀, 吴茜, 陈文超, 等.小波变换域地震资料瞬时频率分析方法[J]. 石油物探, 2007, 46(6):534-540.GAO Jinghuai, WU Qian, CHEN Wenchao, et al. Instantaneous frequency analysis of seismic data in wavelet transform domain[J]. Geophysical Prospecting for Petroleum, 2007, 46(6):534-540.
[26]
尹继尧, 吴宝成, 王维, 等.基于TK能量的峰值瞬时频率在薄互层预测中的应用[J]. 石油地球物理勘探, 2015, 50(3):516-522.YIN Jiyao, WU Baocheng, WANG Wei, et al. Thin interbed thickness prediction using peak instantaneous frequency of time-frequency Teager-Kaiser energy[J]. Oil Geophysical Prospecting, 2015, 50(3):516-522.
[27]
Fomel S. Shaping regularization in geophysical-estimation problems[J]. Geophysics, 2007, 72(2):R29-R36.
[28]
Vincent P, Bengio Y.Kernel matching pursuit[J]. Machine Learning, 2002, 48(1-3):165-187.
[29]
Durka P J, Matysiak A, Eduardo M M, et al.Multichannel matching pursuit and EEG inverse solutions[J]. Journal of Neuroence Methods, 2005, 148(1):49-59.
[30]
Gribonval R.Fast matching pursuit with a multiscale dictionary of Gaussian chirps[J]. IEEE Transactions on Signal Processing, 2011, 49(5):994-1001.