A modified total variation regularization approach based on the Gauss-Newton algorithm and split Bregman iteration for magnetotelluric inversion

被引：0
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作者：
Feng, Deshan [1 ,2 ]
Su, Xuan [1 ,2 ]
Wang, Xun [1 ,2 ]
Wang, Xiangyu [1 ,2 ]
机构：
[1] School of Geosciences and Info-Physics, Central South University, Changsha,410083, China
[2] Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Changsha,410083, China
来源：
Journal of Applied Geophysics | 2020年 / 178卷
关键词：
The conductivity distribution of subsurface often contains segmented structure with sharp interfaces; although the magnetotelluric (MT) inversions based on Tikhonov regularization are usually stable; the smoothing interfaces tomography result from high-resistance artifacts and boundary blurring still post a great challenge for MT inversion. On the contrary; Total variation (TV) regularization can deal with the problem of high-impedance artifact and boundary blurring; but its stability is difficult to be guaranteed. In order to invert the sharp interface of conductivity distribution and improve the accuracy of MT inversion; this paper proposes an MT inversion method based on a new modified total variation (MTV) regularization. The improved method transforms the TV model constraint into two different sub-constraints: one is the standard MT inversion constraint with L2 norm; and the other is the standard L2-TV model constraint; then the Gauss-Newton algorithm and split Bregman iteration are employed to separately solve these two different sub-constraint problems. In the inversion experiments; we design a wedge-shaped model and an undulating surface model; the comparison results of accuracy and efficiency show that; among Tikhonov; TV and MTV regularization methods at the same iterations; the proposed regularization method can achieve the lowest reconstruction error and data misfit; thereby eliminating high-resistance artifacts and; in particular; can solve the problem of smoothing interfaces tomography. To this end; a field data testing is also presented to verify the effectiveness and the practicability of the new modified method. © 2020 Elsevier B.V;
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