地震数据的盲谱反演

IF 1.8 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Geophysical Prospecting Pub Date : 2024-08-21 DOI:10.1111/1365-2478.13594
Yaoguang Sun, Siyuan Cao, Siyuan Chen, Yuxin Su
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引用次数: 0

摘要

反射率反演是储层预测的关键步骤。传统的稀疏-尖峰解卷积假定反射率(反射系数序列)是稀疏的,并通过 L1-正则反演过程求解反射系数。频谱反演是稀疏尖峰解卷积的替代方法,它基于奇偶分解算法,无需使用测井数据、地层解释或反射率初始模型的约束条件,就能准确识别薄层并减少小波调谐效应。在地震处理过程中,由于地质结构复杂,小波提取存在误差,导致解卷积和反演精度较低。盲解卷积是解决上述问题的有效方法,它包括地震小波和反射率序列,假定影响某些地震数据子集的小波大致相同。因此,我们将盲解卷与频谱反演相结合,提出了盲频谱反演。在给定初始小波的情况下,我们可以根据频谱反演计算反射率,并为下一次迭代更新小波。在更新处理过程中,我们加入了小波振幅谱的平滑性作为正则化项,从而减少了小波在时域的振荡,增加了反演小波与初始小波之间的相似性,提高了解的稳定性。盲频谱反演方法继承了盲解卷的小波鲁棒性和频谱反演的高分辨率,适用于反射率反演。在合成和野外地震数据集上的应用表明,即使在小波提取存在误差的情况下,盲频谱反演方法也能准确计算反射率。
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Blind spectral inversion of seismic data

Reflectivity inversion is a key step in reservoir prediction. Conventional sparse-spike deconvolution assumes that the reflectivity (reflection coefficient series) is sparse and solves for the reflection coefficients by an L1-norm inversion process. Spectral inversion is an alternative to sparse-spike deconvolution, which is based on the odd–even decomposition algorithm and can accurately identify thin layers and reduce the wavelet tuning effect without using constraints from logging data, from horizon interpretations or from an initial model of the reflectivity. In seismic processing, an error exists in wavelet extraction because of complex geological structures, resulting in the low accuracy of deconvolution and inversion. Blind deconvolution is an effective method for solving the problem mentioned above, which comprises seismic wavelet and reflectivity sequence, assuming that the wavelets that affect some subsets of the seismic data are approximately the same. Therefore, we combined blind deconvolution with spectral inversion to propose blind spectral inversion. Given an initial wavelet, we can calculate the reflectivity based on spectral inversion and update the wavelet for the next iteration. During the update processing, we add the smoothness of the wavelet amplitude spectrum as a regularization term, thus reducing the wavelet oscillation in the time domain, increasing the similarity between inverted and initial wavelets, and improving the stability of the solution. The blind spectral inversion method inherits the wavelet robustness of blind deconvolution and high resolution of spectral inversion, which is suitable for reflectivity inversion. Applications to synthetic and field seismic datasets demonstrate that the blind spectral inversion method can accurately calculate the reflectivity even when there is an error in wavelet extraction.

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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.
期刊最新文献
Issue Information Simultaneous inversion of four physical parameters of hydrate reservoir for high accuracy porosity estimation A mollifier approach to seismic data representation Analytic solutions for effective elastic moduli of isotropic solids containing oblate spheroid pores with critical porosity An efficient pseudoelastic pure P-mode wave equation and the implementation of the free surface boundary condition
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