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A robust method for analyzing the instantaneous attributes of seismic data: The instantaneous frequency estimation based on ensemble empirical mode decomposition

机译:一种可靠的地震数据瞬时属性分析方法:基于整体经验模态分解的瞬时频率估计

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The Hilbert-Huang transform (HHT) includes two procedures. First, empirical mode decomposition (EMD) is used to decompose signals into several intrinsic mode functions (IMFs) before the Hilbert transform (HT) of these IMFs are calculated. Compared to the conventional Hilbert transform (HT), HI-IT is more sensitive to thickness variations of seismic beds. However, random noise in seismic signal may cause the mixture of the modes from HHT. The recent ensemble empirical mode decomposition (EEMD) presents the advantages in decreasing mode mixture and has the promising potential in seismic signal analysis. Currently, EEMD is mainly used in seismic spectral decomposition and noise attenuation. We extend the application of EEMD based instantaneous frequency to the analysis of bed thickness. The tests on complex Marmousi2 model and a 2D field data show that EEMD is more effective in weakening mode mixtures contained in the IMFs, compared with that calculated by EMD. Furthermore, the EEMD based instantaneous frequency is more sensitive to the seismic thickness variation than that based on EMD and more consistent with the stratigraphic structure, which means that E-IFPs are more advantageous in characterizing reservoirs. (C) 2014 Elsevier B.V. All rights reserved.
机译:Hilbert-Huang变换(HHT)包括两个过程。首先,在计算这些IMF的希尔伯特变换(HT)之前,使用经验模式分解(EMD)将信号分解为几个固有模式函数(IMF)。与传统的希尔伯特变换(HT)相比,HI-IT对地震层的厚度变化更为敏感。但是,地震信号中的随机噪声可能会导致HHT模式的混合。最近的整体经验模态分解(EEMD)在减少模态混合方面具有优势,在地震信号分析中具有广阔的应用前景。目前,EEMD主要用于地震频谱分解和噪声衰减。我们将基于EEMD的瞬时频率的应用扩展到床层厚度的分析中。对复杂Marmousi2模型和2D现场数据进行的测试表明,与EMD计算相比,EEMD在减弱IMF中包含的弱模式混合气方面更为有效。此外,基于EEMD的瞬时频率比基于EMD的瞬时频率对地震厚度变化更敏感,并且与地层结构更加一致,这意味着E-IFP在表征储层方面更具优势。 (C)2014 Elsevier B.V.保留所有权利。

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