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应用地球物理  2015, Vol. 12 Issue (2): 212-220    DOI: 10.1007/s11770-015-0484-2
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地震低频信息缺失特征分析及拓频方法研究
张军华1,张彬彬1,张在金1,梁鸿贤2,葛大明2
1. 中国石油大学(华东)地学院,青岛 266580
2. 胜利油田物探研究院,东营 257022
Low-frequency data analysis and expansion
Zhang Jun-Hua1, Zhang Bin-Bin1, Zhang Zai-Jin1, Liang Hong-Xian2, and Ge Da-Ming2
1. School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China.
2. Geophysical Prospecting Research Institute of Shengli Oilfield, Dongying 257022, China.
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摘要 地震低频信息能够提高分辨率与成像精度,改善反演质量,甚至直接进行油气检测,需要对其进行有效保护与拓展。对于子波而言,缺失低频信息会导致主瓣幅度降低、第一旁瓣幅度增加,并出现次级旁瓣呈周期震荡衰减的现象;从合成地震记录和典型地质模型来看,低频缺失会产生假同相轴,造成分辨率提高的假象,且模型不同位置的特征存在一定差异;对缺失低频的模型数据进行波阻抗反演,会造成构造失真、岩性改变的假象,特别是高陡构造和薄互层。针对缺失低频的地震资料,本文还研究了基于压缩感知与稀疏约束的拓频方法,开发了相应的模块,并对实际CIP道集进行处理,取得了较好的应用效果。
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张军华
张彬彬
张在金
梁鸿贤
葛大明
关键词地震子波   正演模拟   低频拓展   压缩感知   稀疏约束     
Abstract: The use of low-frequency seismic data improves the seismic resolution, and the imaging and inversion quality. Furthermore, low-frequency data are applied in hydrocarbon exploration; thus, we need to better use low-frequency data. In seismic wavelets, the loss of low-frequency data decreases the main lobe amplitude and increases the first side lobe amplitude and results in the periodic shocking attenuation of the secondary side lobe. The loss of low frequencies likely produces pseudo-events and the false appearance of higher resolution. We use models to examine the removal of low-frequency data in seismic data processing. The results suggest that the removal of low frequencies create distortions, especially for steep structures and thin layers. We also perform low-frequency expansion using compressed sensing and sparse constraints and develop the corresponding module. Finally, we apply the proposed method to real common image point gathers with good results.
Key wordsseismic wavelet   forward modeling   low-frequency expansion   compressed sensing   sparse constraint   
收稿日期: 2015-01-30;
基金资助:

本研究由国家油气重大专项(编号:2011ZX05051)和胜利油田科技项目(编号:YKW1301)联合资助。

引用本文:   
张军华,张彬彬,张在金等. 地震低频信息缺失特征分析及拓频方法研究[J]. 应用地球物理, 2015, 12(2): 212-220.
Zhang Jun-Hua,Zhang Bin-Bin,Zhang Zai-Jin et al. Low-frequency data analysis and expansion[J]. APPLIED GEOPHYSICS, 2015, 12(2): 212-220.
 
[1] Beck, A., and Teboulle, M., 2009, A fast iterative shrinkage-thresholding algorithm for linear inverse problems: SIAM Journal on Imaging Sciences, 2(1), 183−202.
[2] Baeten, G., de Maag, J. W., Plessix, R. E., Klaassen, R., Qureshi, T., Kleemeyer, M., ten Kroode, F., and Zhang R. J., 2013, The use of low frequencies in a full-waveform inversion and impedance inversion land seismic case study: Geophysical Prospecting, 61(4), 701−711.
[3] Bai, L. S., Liu, Y. K., Lu, H. Y., Wang, Y, B., and Chang X., 2014, Curvelet-domain joint iterative seismic data reconstruction based on compressed sensing: Chinese J. Geophys. (in Chinese), 57(9), 2937−2945.
[4] Castagna, J. P., Sun, S. J., and Siegfried, R. W., 2003, Instantaneous spectral analysis: Detection of low-frequency shadows associated with hydrocarbons: The Leading Edge, 22(2), 120−127.
[5] Candès, E. J., Romberg, J. K., and Tao, T., 2006a, Stable signal recovery from incomplete and inaccurate measurements: Communications on Pure and Applied Mathematics, 59(8), 1207−1223.
[6] Candès, E. J., Romberg, J., and Tao, T., 2006b, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information: IEEE transaction on information theory, 52(2), 489−509.
[7] Chen, X. H., He, Z. H., Zhu, S. X., Liu, W., and Zhong W. L., 2012, Seismic low-frequency-based calculation of reservoir fluid mobility and its applications: Applied Geophysics, 9(3), 326−332.
[8] Donoho, D. L., 2006, Compressed sensing: IEEE Transactions on Information Theory, 52(4), 1289−1306.
[9] Guan, L. P., and Tang, Q. J., 1990, High/low frequency compensation of seismic signal: Geophysical Prospecting for Petroleum, 29(3), 35−45.
[10] Han, L. G., Zhang, Y., Han, L., and Yu, Q. L., 2012, Compressed sensing and sparse inversion based low-frequency information compensation of seismic data: Journal of JiLin University (Earth Science Edition), 42(3), 259−264.
[11] Herrmann, F. J., and Hennenfent, G., 2008, Non-parametric seismic data recovery with curvelet frames: Geophysical Journal International, 173(1), 233−248.
[12] Kallweit, R. S., and Wood, L. C., 1982, The limits of resolution of zero-phase wavelets: Geophysics, 47(7), 1035−1046.
[13] Kelly, S., Ramos-Martinez, J., and Tsimelzon, B., 2009, The effect of improved, low-frequency bandwidth in full-waveform inversion for velocity: 79th Ann. Internat. Mtg, Soc. Expl. Geophys., Expanded Abstracts, 3974−3977.
[14] Li, Z. C., and Zhang, J. H., 2004, Seismic data processing method: The Press of University of Petroleum.
[15] Li, G. L., Chen G., and Zhong J. Y., 2013, Analysis of geophone properties effects for land seismic data: Applied Geophysics, 6(1), 91−101.
[16] Sirgue, L., and Pratt, R. G., 2004, Efficient waveform inversion and imaging: A strategy for selecting temporal frequencies: Geophysics, 69(1), 231−248.
[17] Sun, J. H., Zhang, Y. S., Dong, J. G., and Han, Z. Y., 2012, Low frequency signal receiver technology: Petroleum Instrumenis, 26(6), 73−76.
[18] Tao, Z. F., Zhao, Y. L., and Ma, L., 2011, Low frequency seismic and low frequency vibrator: EGP, 21(2), 71−76
[19] ten Kroode, F., Bergler, S., Corsten, C., de Maag, J. W., Strijbos, F., and Tijhof, H., 2013, Broadband seismic data-The importance of low frequencies: Geophysics, 78(2), WA3−WA14.
[20] Whitcombe, D., Hodgson, L., 2007, Stabilizing the low frequencies: The Leading Edge, 26(1), 66−72.
[21] Woodburn, N., Hardwick, A., and Travis, T., 2011, Enhanced low frequency signal processing for sub-basalt imaging: 73rd EAGE Conference & Exhibition incorporating SPE EUROPEC, Vienna, Austria.
[22] Wang, X. W., and Wang, H. Z., 2014, A research of high-resolution plane-wave decomposition based on compressed sensing: Chinese J. Geophys.(in Chinese), 57(9), 2946−2960.
[23] Yuan, S. Y., Wang, S. X., Luo, C. M., and He, Y. X., 2015, Simultaneous multitrace impedance inversion with transform-domain sparsity promotion: Geophysics, 80(2), R71−R80.
[24] Ziolkowski, A., Hanssen, P., Gatliff R., Jakubowicz, H., Dobson, A., Hampson, G., Li, X. Y., and Liu E. R., 2003, Use of low frequencies for sub-basalt imaging: Geophysical Prospecting, 51(3), 169−182.
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