Regularized magnetotelluric inversion based on a minimum support gradient stabilizing functional

Xiang, Yang; Yu, Peng; Zhang, Luolei; Feng, Shaokong; Utada, Hisashi

doi:10.1186/s40623-017-0743-y

Cited by 26 publications

(11 citation statements)

References 23 publications

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“…The focusing parameter can be obtained by a trial‐and‐error method with the tuning of the focusing parameter (Xiang et al . ).…”

Section: Methodsmentioning

confidence: 97%

“…The focusing parameter can be obtained by a trial-and-error method with the tuning of the focusing parameter (Xiang et al 2017). The goal of this study is to introduce a new stabilizing function which generates a fast focusing solution.…”

Section: Sparse Inversion Of Magnetic Data With a Sigmoid Stabilizingmentioning

confidence: 99%

“…Last and Kubik ; Barbosa and Silva ; Portniaguine and Zhdanov ; Camacho, Montesinos and Vieira ; Bertete‐Aguirre, Cherkaev and Oristaglio ; Pilkington ; Zhao, Yu and Zhang ; Xiang et al . ; Meng ; Meng et al . ).…”

Section: Introductionmentioning

confidence: 99%

“…The second category of the stabilizing functions belongs to a class of functions that produce solutions with focused images (e.g. Last and Kubik 1983;Barbosa and Silva 1994;Portniaguine and Zhdanov 1999;Camacho, Montesinos and Vieira 2000;Bertete-Aguirre, Cherkaev and Oristaglio 2002;Pilkington 2009;Zhao, Yu and Zhang 2016;Xiang et al 2017;Meng 2018;Meng et al 2018). However, one of the most popular stabilizing functions for non-smooth inversion of geophysical data is minimum support (MS) stabilizing function which was used by different researchers (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…However, one of the most popular stabilizing functions for non-smooth inversion of geophysical data is minimum support (MS) stabilizing function which was used by different researchers (e.g. Cuma, Wilson and Zhdanov 2012;Zhdanov et al 2014;Glegola et al 2015;Hou et al 2015;Rezaie et al 2017a,b;Xiang et al 2017;Fei et al 2018;Ji et al 2019;Hu et al 2019). The MS stabilizing function was first introduced by Last and Kubik (1983).…”

Section: Introductionmentioning

confidence: 99%

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A sigmoid stabilizing function for fast sparse 3D inversion of magnetic data

Rezaie

2019

Near Surface Geophysics

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An interesting geological objective of quantitative interpretation of magnetic data is to find inverse models which can determine sharp geological interfaces below the surface. The stabilizing function in the Tikhonov parametric functional governs sparseness constraint in the recovered model. This paper introduces a novel stabilizer based on a sigmoid function which can provide non‐smooth models in the inversion of magnetic data efficiently. An inversion algorithm is developed based on the reweighted regularized conjugate gradient to get the solution of the inverse problem using this stabilizing function. The performance of the proposed algorithm is checked on two synthetic data sets and real aeromagnetic data from McFaulds Lake in Ontario, Canada, in comparison with the results of the minimum support stabilizing function. The inverse problem converges to the solution faster when the sigmoid stabilizing function is used instead of the minimum support stabilizing function.

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“…The focusing parameter can be obtained by a trial‐and‐error method with the tuning of the focusing parameter (Xiang et al . ).…”

Section: Methodsmentioning

confidence: 97%

Section: Sparse Inversion Of Magnetic Data With a Sigmoid Stabilizingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A sigmoid stabilizing function for fast sparse 3D inversion of magnetic data

Rezaie

2019

Near Surface Geophysics

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Robust inversion of 1D magnetotelluric data using the Huber loss function

Desifatma,

Djaja,

Pratomo

et al. 2024

Comput Geosci

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In this study, a robust 1D inversion was performed on magnetotelluric (MT) data by utilizing the Huber loss function to avoid misleading interpretation of results, which is caused by the presence of outliers in the data. The MT method utilizes the ratio between the electric field (E) and the magnetic field (H), which are perpendicular to each other, to obtain impedance values (Z). These values are used to extract subsurface information in the form of electrical resistivity variations with depth. In the study, forward modeling of the 1D MT responses was conducted by recursively calculating Z values. Robust inversion was performed using the Huber loss function as an objective function to minimize the difference between the calculated and observed data. The Huber loss function combines the squared loss and the absolute loss to anticipate the presence of outliers in MT observation data. The robust inversion was performed on synthetic data with additional noise and field data. The inversion process utilized 50 layers with a thickness of 500 m, a hyperparameter $$\varvec{\delta }= 5\times {10}^{-2}$$ δ = 5 × 10 - 2 , $$\lambda =0.01$$ λ = 0.01 , and $${\lambda }_{t}=100$$ λ t = 100 . The number of iterations used was 500 for inversions that used synthetic data and 3000 for inversions that used field data. The robust inversion scheme using the Huber loss function successfully overcame the presence of outliers and accurately estimated the actual model parameters, both for synthetic data and field data that contained outliers. The misfit was relatively small and close to zero, indicating that the inversion code works well.

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