Seismic attribute extraction based on HHT and its application in a marine carbonate area
Huang Ya-Ping1, Geng Jian-Hua1, Zhong Guang-Fa1, Guo Tong-Lou2, Pu Yong2, Ding Kong-Yun1, and Ma Ji-Qiang1
1. State Key Laboratory of Marine Geology, School of Ocean and Earth Science, Tongji University, Shanghai 200092, China.
2. Exploration Southern Company of China Petroleum and Chemical Corporation, Chengdu 610041, China.
Abstract:
The Hilbert-Huang transform (HHT) is a new analysis method suitable for nonlinear and non-stationary signals. It is very appropriate to seismic signals because they show both non-stationary and nonlinear characteristics. We first introduce the realization of HHT empirical mode decomposition (EMD) and then comparatively analyze three instantaneous frequency algorithms based on intrinsic mode functions (IMF) resulting from EMD, of which one uses the average instantaneous frequency of two sample intervals having higher resolution which can determine that the signal frequency components change with time. The method is used with 3-D poststack migrated seismic data of marine carbonate strata in southern China to effectively extract the three instantaneous attributes. The instantaneous phase attributes of the second intrinsic mode functions (IMF2) better describe the reef facies of the platform margin and the IMF2 instantaneous frequency attribute has better zoning.Combining analysis of the three IMF2 instantaneous seismic attributes and drilling data can identify the distribution of sedimentary facies well.
HUANG Ya-Ping,GENG Jian-Hua,ZHONG Guang-Fa et al. Seismic attribute extraction based on HHT and its application in a marine carbonate area[J]. APPLIED GEOPHYSICS, 2011, 8(2): 125-133.
[1]
Battista, B. M., Knapp, C., McGee, T., and Goebel, V., 2007, Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data: Geophysics, 72(2), H29 − H37.
[2]
Boashash, B., 1992, Estimating and interpreting the instantaneous frequency of a signal—Part 2:algorithms and applications: Proceedings of the IEEE, 80(4),540 − 568.
Huang, N., Shen, Z., Long, S. R., Wu, M. C., Shih,H. H., Zheng, Q., Yen, N.C., Tung, C. C., and Liu, H. H., 1998, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series: Proceedings of the Royal Society of London, A454, 903 − 995.
[6]
Hassan, H. H., and Peirce, J. W., 2005, Empirical Mode Decomposition (EMD) of potential field data: airborne gravity data as an example: 75th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 704 − 706.
Magrin-Chagnolleau, I., and Baranuik, R. G.., 1999, Empirical mode decomposition based time frequency attributes: 69th Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, 1949- 1952.
Taner, M., Koehler, F., and Sheriff, R. E., 1979, Complexseismic trace analysis: Geophysics, 44(6), 1041 −1063.
[11]
Wang, Z. W., Liu, J. H., Yue, C. W., Li, X. C., and Li, C. C., 2009,The filtering characteristics of HHT and its application in acoustic log waveform signal processing: Applied Geophysics, 6(1), 8 − 16.
[12]
Wen, X. T., He, Z. H., and Huang, D. J., 2009, Reservoir detection based on EMD and correlation dimension: Applied Geophysics, 6(1), 70 − 76.