Viscoacoustic least squares reverse-time migration using L1-2 norm sparsity constraint

IF 1.6 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Mingkun Zhang, Lingqian Wang, Hanming Chen, Hui Zhou, Peng Liu
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引用次数: 0

Abstract

Least-squares reverse-time migration (LSRTM) has become an advanced technique for complex structures imaging of the subsurface, as it can provide a higher resolution and more balanced amplitude migrated image than conventional reverse-time migration (RTM). However, the intrinsic attenuation of subsurface introduces amplitude attenuation and phase dispersion of seismic wavefield, which leads to the inverted image kinematically and dynamically inexactitude. Moreover, the imperfect geometry, limited bandwidth of seismic data, and inappropriate modeling kernel etc., would inevitably introduce two side-effects in migrated image, resulting in degradation of LSRTM imaging potential. To alleviate above issues, we present a data-domain sparsity constraint viscoacoustic least-squares reverse-time migration algorithm in this paper. In particular, we utilize the decoupled constant Q fractional Laplacians (DFLs) viscoacoustic wave equation as the modeling kernel to describe the attenuation effects of the subsurface, while a model constraint constructed in the misfit function via L1-2 norm is carried out to clear the migrated artefacts and boost the imaging resolution. Thanks to the excellent performance in sparsity, the drawbacks of unconstraint LSRTM can be effectively mitigated by the L1-2 norm-based regularization. In this paper, we adopt the alternating direction of multipliers method (ADMM) to iteratively address the constrained L1-2 minimization problem by implementing a proximal operator, and three synthetic examples are hired to evaluate the effectiveness and practicability of the proposed strategy. Migration results prove that the proposed scheme can effectively compensate the attenuation effects, improve the resolution, and suppress the migration artifacts of inverted images even in the complex imaging situations.
使用 L1-2 规范稀疏性约束的粘声最小二乘反向时间迁移
最小二乘反演(LSRTM)与传统的反演(RTM)相比,能提供分辨率更高、振幅更均衡的反演图像,因此已成为地下复杂结构成像的先进技术。然而,地表下的固有衰减会带来地震波场的振幅衰减和相位分散,从而导致反演图像在运动和动态上不精确。此外,不完美的几何形状、有限的地震数据带宽以及不恰当的建模内核等,都不可避免地会在迁移后的图像中引入两种副作用,导致 LSRTM 成像潜力下降。为了解决上述问题,我们在本文中提出了一种数据域稀疏性约束粘声最小二乘反演时间迁移算法。其中,我们利用解耦恒 Q 分数拉普拉斯(DFLs)粘声波方程作为建模核来描述地下的衰减效应,同时通过 L1-2 准则在误拟合函数中构建模型约束来清除迁移的伪影并提高成像分辨率。由于 LSRTM 在稀疏性方面的优异表现,基于 L1-2 规范的正则化可以有效缓解无约束 LSRTM 的缺点。本文采用交替乘数方向法(ADMM),通过实施近似算子来迭代解决受约束的 L1-2 最小化问题,并通过三个合成实例来评估所提策略的有效性和实用性。迁移结果证明,即使在复杂的成像情况下,所提出的方案也能有效补偿衰减效应、提高分辨率并抑制反转图像的迁移伪影。
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来源期刊
Journal of Geophysics and Engineering
Journal of Geophysics and Engineering 工程技术-地球化学与地球物理
CiteScore
2.50
自引率
21.40%
发文量
87
审稿时长
4 months
期刊介绍: Journal of Geophysics and Engineering aims to promote research and developments in geophysics and related areas of engineering. It has a predominantly applied science and engineering focus, but solicits and accepts high-quality contributions in all earth-physics disciplines, including geodynamics, natural and controlled-source seismology, oil, gas and mineral exploration, petrophysics and reservoir geophysics. The journal covers those aspects of engineering that are closely related to geophysics, or on the targets and problems that geophysics addresses. Typically, this is engineering focused on the subsurface, particularly petroleum engineering, rock mechanics, geophysical software engineering, drilling technology, remote sensing, instrumentation and sensor design.
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