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Joint Multi-Mode Dispersion Extraction in Frequency-Wavenumber and Space-Time Domains

机译:频域和空域中的联合多模式色散提取

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In this paper, we present a novel broadband approach for the extraction of dispersion curves of multiple time frequency overlapped dispersive modes from borehole acoustic data. The new approach works jointly in the frequency-wavenumber and space-time domains and, in contrast to existing methods it efficiently handles multiple signals with significant time frequency overlap. The proposed method begins by exploiting the slowness (phase and group) and time location estimates obtained by a broadband dispersion extraction method based on frequency-wavenumber domain sparsity penalization proposed in [A. Aeron, S. Bose, H.-P. Valero, and V. Saligrama, “Broadband dispersion extraction using simultaneous sparse penalization,” IEEE Trans. Signal Process., vol. 50, no. 10, pp. 4821–4837, 2011]. In this context, we first present a Cramér–Rao Bound (CRB) analysis for slowness estimation and show that for the domain broadband processing, group slowness estimates have more variance than the phase slowness and time location estimates. In order to improve the group slowness estimates, we exploit the time compactness property of the modes to effectively represent the data as a linear superposition of time compact space-time propagators parameterized by the phase and group slowness. A linear least squares estimation algorithm in the space-time domain is then used to obtain improved group slowness estimates. The performance of the method is demonstrated on real borehole acoustic data sets.
机译:在本文中,我们提出了一种新颖的宽带方法,用于从井眼声学数据中提取多个时间频率重叠的色散模的色散曲线。新方法在频波数域和时空域中共同工作,并且与现有方法相比,它可以有效处理具有明显时频重叠的多个信号。所提出的方法开始于利用[A.]中提出的基于频波数域稀疏性惩罚的宽带色散提取方法获得的慢度(相位和组)和时间位置估计。 Aeron,S. Bose,H.-P. Valero和V. Saligrama,“使用同时稀疏惩罚的宽带色散提取”,IEEE Trans。信号处理,第一卷50,不。 10,pp.4821-4837,2011]。在这种情况下,我们首先提出了Cramér-RaoBound(CRB)分析用于慢度估计,并表明对于域宽带处理,组慢度估计比相位慢度和时间位置估计具有更多的方差。为了改善组慢度估计,我们利用模式的时间紧度属性来有效地将数据表示为由相位和组慢度参数化的时间紧时空传播器的线性叠加。然后,使用时空域中的线性最小二乘估计算法来获得改进的组慢度估计。该方法的性能在真实的钻孔声波数据集上得到了证明。

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