High-resolution channel identification method based on WVD-MEM
NIU Shuangchen1,2,3, CHENG Bingjie1,2
1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu, Sichuan 610059, China; 2. Key Laboratory of Earth Exploration and Information Techniques(Chengdu University of Technology), Ministry of Education, Chengdu, Sichuan 610059, China; 3. Sichuan Zhongcheng Institute of Coalfield Geophysical Engineering, Chengdu, Sichuan 610072, China
Abstract：As important oil and gas reservoirs, channel sandbody sedimentary has long been a hot research object in the field of oil and gas exploration and development. In recent years, as oil and gas exploration and development continue, channels at greater buried depth with smaller width and lesser thickness have gradually been brought into the focus of attention. However, restrained by factors such as buried depth, complex vertical and horizontal distribution, and low dominant frequency, narrow frequency band, and low spatial resolution of seismic data, it is difficult to identify the channels with high precision. Traditional identification methods including seismic attribute interpretation and time-frequency analysis are often not effective. In this paper,the non-stationary signal analysis method which combine the Wigner-Ville distribution (WVD) and the maximum entropy method (MEM) together (WVD-MEM method for short) was studied. The kernel function of the WVD was extended by the MEM, which not only eliminated the cross terms in WVD signal analysis but also delivered high time-frequency concentration and a high resolution. Furthermore, the maximum entropy power spectrum, instantaneous amplitude, and other seismic attributes of WVD were calculated, which was aimed at highlighting the channel response characteristics and resolution of seismic data from the perspective of improving the resolution of instantaneous amplitudes and power spectra. This method has achieved good results in the application of seismic data channel identification in western Sichuan area.
庞锐, 刘百红, 孙成龙.时频分析技术在地震勘探中的应用综述[J]. 岩性油气藏, 2013, 25(3):92-96.PANG Rui, LIU Baihong, SUN Chenglong. Review on time-frequency analysis technique and its application in seismic exploration[J]. Lithologic Reservoirs, 2013, 25(3):92-96.
[2]
黄德济, 赵宪生.时频域中主参数剖面的计算及应用[J]. 物探化探计算技术, 1994, 16(3):197-204.HUANG Deji, ZHAO Xiansheng. The calculation and applicatopn of seismic main characteristic sections in time frequency domain[J]. Computing Techniques for Geophysical and Geochemical Exploration, 1994, 16(3):197-204.
[3]
余厚全, 刘益成, 黄载禄.小波变换用于地震测井信号的多分辨率分析[J]. 石油地球物理勘探, 1994, 29(4):441-448.YU Houquan, LIU Yicheng, HUANG Zailu. Application of wavelet transform to multiresolution analysis of seismic and log signals[J]. Oil Geophysical Prospecting, 1994, 29(4):441-448.
杨道庆, 张永华, 罗家群, 等.时频分析技术识别泌阳凹陷毕店地区河道砂体[J]. 特种油气藏, 2015, 22(1):12-15.YANG Daoqing, ZHANG Yonghua, LUO Jiaqun, et al. Channel sandbodys identification by time-frequency analysis in Bidian of Biyang Sag[J]. Special Oil & Gas Reservoirs, 2015, 22(1):12-15.
Gabor D. Theory of communication[J]. Journal of the Institute of Electrical Engineers of Japan, 1946, 93:429-457.
[10]
黄昱丞, 郑晓东, 栾奕, 等.地震信号线性与非线性时频分析方法对比[J]. 石油地球物理勘探, 2018, 53(5):975-989.HUANG Yucheng, ZHENG Xiaodong, LUAN Yi, et al. Comparison of linear and nonlinear time-frequency analysis on seismic signals[J]. Oil Geophysical Prospecting, 2018, 53(5):975-989.
[11]
张贤达. 现代信号处理(第2版)[M]. 北京:清华大学出版社, 2002.
[12]
QIAN Shi'e.Introduction to Time-Frequency and Wavelet Transforms[M]. Beijing:China Machine Press, 2005.
[13]
Stockwell R G, Mansinha L, Lowe R P. Localization of the complex spectrum:the S transform[J]. IEEE Transactions on Signal Processing, 1996, 44(4):998-1001.
[14]
Pinnegar C R, Lalu M.The S-transform with windows of arbitrary and varying shape[J]. Geophysics, 2003, 68(1):381-385.
[15]
高静怀, 陈文超, 李幼铭, 等.广义S变换与薄互层地震响应分析[J]. 地球物理学报, 2003, 46(4):526-532.GAO Jinghuai, CHEN Wenchao, LI Youming, et al. Generalized S transform and seismic response analysis of thin interbeds[J]. Chinese Journal of Geophysics, 2003, 46(4):526-532.
[16]
陈学华, 贺振华, 黄德济.基于广义S变换的地震资料高效时频谱分解[J]. 石油地球物理勘探, 2008, 43(5):530-534.CHEN Xuehua, HE Zhenhua, HUANG Deji. High-efficient time-frequency spectrum decomposition of seismic data based on generalized S transform[J]. Oil Geophysical Prospecting, 2008, 43(5):530-534.
[17]
Wigner E P. On the Quantum Correction For Thermodynamic Equilibrium[J]. Physical Review, 1932, 40(5):749-759.
[18]
张贤达, 保铮. 非平稳信号分析与处理[M]. 北京:国防工业出版社, 1998.
[19]
Choi H I, Williams W J. Improved time-frequency representation of multicomponent signals using exponential kernels[J]. IEEE Transactions on Acoustics Speech & Signal Processing, 2002, 37(6):862-871.
[20]
海森堡H9N——提出"测不准原理"[J]. 光谱实验室, 2008, 25(1):112-113.
[21]
王见, 李金同, 卢华玲, 等. 采用STFT-Wigner变换抑制Wigner-Ville分布交叉项[J]. 重庆大学学报, 2013, 36(8):16-18.WANG Jian, LI Jintong, LU Hualing, et al. Using STFT-Wigner transform to suppress the cross terms in Wigner-Ville distribution[J]. Journal of Chongqing University, 2013, 36(8):16-18.
[22]
Zhong J, Huang Y. Time-frequency representation based on an adaptive short-time Fourier transform[J]. IEEE Transactions on Signal Processing, 2010, 58(10):5118-5128.
[23]
Pei S C, Huang S G. STFT with adaptive window width based on the chirp rate[J]. IEEE Transactions on Signal Processing, 2012, 60(8):4065-4080.
[24]
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.
[25]
Wu Z, Huang N E. Ensemble empirical mode decomposition:A noise assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1):1-41.
[26]
Torres M E, Colominas M A, Schlotthauer G, et al. A complete ensemble empirical mode decomposition with adaptive noise[C]. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2011.
[27]
Daubechies I, Lu J, Wu H T. Synchrosqueezed wavelet transforms:An empirical mode decomposition-like tool[J]. Applied and Computational Harmonic Analysis, 2011, 30(2):243-261.
[28]
Huang Z L, Zhang J, Zhao T H, et al. Synchrosquee-zing S-transform and its application in seismic spectral decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(2):817-825.
[29]
Thakur G, Wu H T. Synchrosqueezing-based reco-very of instantaneous frequency from nonuniform samples[J]. SIAM Journal on Mathematical Analysis, 2011, 43(5):2078-2095.
[30]
Stéphane G. Mallat, Member, et al. Matching pursuits with time-frequency dictionaries[J]. IEEE Tran-sactions on Signal Processing, 1993, 41(12):3397-3415.
[31]
刘希康, 丁志峰, 李媛, 等. STFT与WVD在地震波时频分析中的应用[J]. 地震地磁观测与研究, 2016, 37(1):15-21.LIU Xikang, DING Zhifeng, LI Yuan, et al. Application of short-time Fourier transform and Wigner-Ville distribution in time-frequency analysis of seismic wave[J]. Seismological and Geomagnetic Observation and Research, 2016, 37(1):15-21.
[32]
孔慧芳, 陶文益, 闫嘉鹏. 基于FRFT的Wigner-Ville分布交叉项抑制方法[J]. 测控技术, 2019, 38(10):15-19.KONG Huifang, TAO Wenyi, YAN Jiapeng. Wigner-Ville distribution cross terms suppression method based on FRFT[J]. Measurement and control technology, 2019, 38(10):15-19.
[33]
纪永祯, 张渝悦, 朱立华, 等. 基于SBL-WVD的地震高分辨率时频分析[J]. 石油物探, 2020, 59(1):80-86.JI Yongzhen, ZHANG Yuyue, ZHU Lihua, et al. High-resolution seismic time-frequency analysis based on sparse Bayesian learning combined with Wigner-Ville distribution[J]. Geophysical Prospecting for Petroleum, 2020, 59(1):80-86.
[34]
Zoukaneri I, Porsani M J.A combined Wigner-Ville and maximum entropy method for high-resolution time-frequency analysis of seismic data[J]. Geophy-sics, 2015, 80(6):O1-O11.
[35]
Burg J P. Maximum Entropy Spectrum Analysis[M]. 1975.
[36]
向道朴, 周东明, 何建国.微多普勒瞬时频率估计算法对噪声的适应性能研究[J]. 电路与系统学报, 2010, 15(6):90-101.XIANG Daopu, ZHOU Dongming, HE Jianguo. The adaptability study of micro-Doppler's instantaneous frequency estimation algorithm with noise[J]. Journal of Circuits and Systems, 2010, 15(6):90-101.
[37]
王宽.川西凹陷东坡沙溪庙组精细地震储层预测[D]. 北京:中国石油大学, 2017.WANG Kuan. Fine seismic reservoir prediction in Shaximiao formation on east slope of western Sichuan Depression[D]. China University of Petroleum, Beijing, 2017.
