Deblending and interpolation of subsampled blended seismic data based on damped randomized singular spectrum analysis

IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Zhuowei Li, Tongtong Mo, Jiawen Song, Benfeng Wang
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引用次数: 0

Abstract

When compared to traditional seismic data acquisition, irregular blended acquisition significantly promotes the acquisition efficiency. Yet, the blending noise of subsampled blended data introduces new obstacles for the subsequent processing of seismic data. Due to the predictability of linear events in the frequency–space domain, the constructed Hankel matrices exhibit low-rank characteristics. However, the blending noise of subsampled blended data increases the rank, so deblending and interpolation can be implemented via rank-reduction algorithms such as the singular spectrum analysis. The significant computing cost of the singular value decomposition, however, makes the traditional singular spectrum analysis inefficient. An alternative algorithm, known as the randomized singular spectrum analysis, employs the randomized singular value decomposition instead of the traditional singular value decomposition for rank-reduction. The randomized singular spectrum analysis significantly enhances the efficiency of the decomposition process, particularly when dealing with large Hankel matrices. There still remains some random noise when using the singular spectrum analysis or randomized singular spectrum analysis for subsampled blended data, because the noise subspace and signal subspace are coupled together. Thus, we incorporate a damping operator into the randomized singular value decomposition and propose a novel damped randomized singular spectrum analysis method. The damped randomized singular spectrum analysis combines the advantages of the randomized singular value decomposition and the damping operator to enhance the computational efficiency and suppress the remaining noise. Moreover, an iterative projected gradient descent strategy is introduced to achieve deblended and interpolated seismic data for subsequent processing. Examples from synthetic data and field data are used to demonstrate the effectiveness and superiority of the proposed damped randomized singular spectrum analysis method, which enhances the accuracy and efficiency during simultaneous deblending and interpolation.

基于阻尼随机奇异谱分析的子采样混合地震数据去模和插值
与传统的地震数据采集相比,不规则混合采集大大提高了采集效率。然而,子采样混合数据的混合噪声为地震数据的后续处理带来了新的障碍。由于线性事件在频域的可预测性,构建的 Hankel 矩阵呈现低秩特征。然而,子采样混合数据的混合噪声会增加秩,因此可以通过秩还原算法(如奇异谱分析)来实现去秩和插值。然而,奇异值分解的计算成本很高,使得传统的奇异频谱分析效率低下。一种被称为随机奇异谱分析的替代算法采用随机奇异值分解代替传统奇异值分解进行秩还原。随机奇异谱分析大大提高了分解过程的效率,尤其是在处理大型汉克尔矩阵时。由于噪声子空间和信号子空间耦合在一起,因此在使用奇异谱分析或随机奇异谱分析处理子采样混合数据时,仍会存在一些随机噪声。因此,我们在随机奇异值分解中加入了阻尼算子,并提出了一种新型的阻尼随机奇异谱分析方法。阻尼随机奇异谱分析结合了随机奇异值分解和阻尼算子的优点,提高了计算效率并抑制了剩余噪声。此外,该方法还引入了迭代投影梯度下降策略,以实现对地震数据的除杂和插值,以便进行后续处理。通过合成数据和野外数据的实例,证明了所提出的阻尼随机奇异频谱分析方法的有效性和优越性,该方法提高了同时除杂和插值的精度和效率。
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来源期刊
Geophysical Prospecting
Geophysical Prospecting 地学-地球化学与地球物理
CiteScore
4.90
自引率
11.50%
发文量
118
审稿时长
4.5 months
期刊介绍: Geophysical Prospecting publishes the best in primary research on the science of geophysics as it applies to the exploration, evaluation and extraction of earth resources. Drawing heavily on contributions from researchers in the oil and mineral exploration industries, the journal has a very practical slant. Although the journal provides a valuable forum for communication among workers in these fields, it is also ideally suited to researchers in academic geophysics.
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