An efficient plug-and-play regularization method for full waveform inversion

IF 1.6 3区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS Journal of Geophysics and Engineering Pub Date : 2023-09-12 DOI:10.1093/jge/gxad073
Hongsun Fu, Lu Yang, Xinyue Miao
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Abstract

Abstract Nonlinear inverse problems arise in various fields ranging from scientific computation to engineering technology. Inverse problems are intrinsically ill-posed, and effective regularization techniques are necessary. The core of a suitable regularization method is to introduce the prior information of the model via an explicit or implicit regularization function. Plug-and-play regularization is a flexible framework that integrates the most effective denoising priors into an iterative algorithm, and it has recently shown great potential in the solution of linear ill-posed problems. Unlike traditional regularization methods, plug-and-play regularization does not require an explicit regularization function to represent the prior information of the model. In this work, by using total variation, block-matching and three-dimensional filtering, and fast and flexible denoising convolutional neural network denoisers, we propose a novel iterative regularization algorithm based on the alternating direction method of multipliers method. The combination of total variation and block-matching three-dimensional filtering regularizers can take advantage of the sparsity and nonlocal similarity in the solution of inverse problems. When combined with traditional and novel regularizers, deep neural networks have been shown to be an effective regularization approach, which can achieve state-of-the-art performance. Finally, we apply the proposed algorithm to the full waveform inversion problem to show the effectiveness of our method. Numerical results demonstrate that the proposed algorithm outperforms existing inversion methods in terms of quantitative measures and visual perceptual quality.
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一种高效的全波形反演即插即用正则化方法
从科学计算到工程技术等各个领域都存在非线性逆问题。逆问题本质上是不适定的,有效的正则化技术是必要的。一种合适的正则化方法的核心是通过显式或隐式正则化函数引入模型的先验信息。即插即用正则化是一种灵活的框架,它将最有效的去噪先验集成到迭代算法中,并且最近在解决线性不适定问题方面显示出巨大的潜力。与传统的正则化方法不同,即插即用正则化不需要明确的正则化函数来表示模型的先验信息。本文利用全变分、分块匹配和三维滤波技术,结合快速灵活去噪的卷积神经网络去噪器,提出了一种基于乘法器交替方向法的迭代正则化算法。将全变分与块匹配三维滤波正则子相结合,可以利用逆问题解的稀疏性和非局部相似性。当与传统和新型正则化器相结合时,深度神经网络已被证明是一种有效的正则化方法,可以达到最先进的性能。最后,将该算法应用于全波形反演问题,验证了该方法的有效性。数值结果表明,该算法在定量度量和视觉感知质量方面优于现有的反演方法。
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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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