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1

An effective estimate for selecting the regularization parameter in the 3D inversion of magnetotelluric data

Ghaedrahmati Reza, Moradzadeh Ali, Moradpouri Farzad

Acta Geophysica

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2022

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Vol. 70, no 2

609--621

EN

The magnetotelluric (MT) inverse problem is a nonlinear and strongly ill-posed problem. Therefore, to avoid the problem of non-uniqueness of response, this problem is mainly solved by Tikhonov regularization method. The purpose of this study is to present a suitable method for selecting the regularization parameters in the 3D MT inverse problem, with regard to the accuracy and speed of the inversion. In this research, the regularization parameter is simply estimated in each iteration of inversion as the ratio of the data misfit to sum of the data misfit and model norm in the pre-iteration. This scheme is applied in the well-known 3D inversion algorithm, WSInv3DMT, instead of the discrepancy principle method. The accuracy of this scheme is assessed by performing the inversion on synthetic models and real data. Results from the inversion for the synthetic and real data indicate that the data misfit and the model norm are reduced with an acceptable rate during the inversion operation. The inverse model has been smoothly converged to an appropriate model and that unrealistic structures have not been included in the model. The results also show that estimation of the regularization parameter by the discrepancy principle method and continuing the inversion to achieve the target data misfit may lead to the production of a model with non-realistic structures, while in the proposed scheme the inversion has not encountered this problem and it converges to an appropriate model after fewer iterations of inversion. In addition, the results show that the time consumed for the inversion of a set of real data with 41 stations and 16 measurement frequencies would decrease up to 27 percent compared to the time devoted for inverting the same set of data by the discrepancy principle method. Also the inversion does not deviate toward unrealistic models and it closely converges to the model of real geological structures.

2

Machine learning based prediction of regularization parameters for seismic inverse problems

Liu Shihuan, Zhang Jiashu

Acta Geophysica

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2021

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Vol. 69, no. 3

809--820

EN

Regularization parameter selection (RPS) is one of the most important tasks in solving inverse problems. The most common approaches seek the optimal regularization parameter (ORP) from a sequence of candidate values. However, these methods are often time-consuming because they need to conduct the estimation process on all candidate values, and they are always restricted to solve certain problem types. In this paper, we propose a novel machine learning-based prediction framework (MLBP) for the RPS problem. The MLBP frst generates a large number of synthetic data by varying the inputs with diferent noise conditions. Then, MLBP extracts some pre-defned features to represent the input data and computes the ORP of each synthetic example by using true models. The pairs of ORP and extracted features construct a training set, which is used to train a regression model to describe the relationship between the ORP and input data. Therefore, for newly practical inverse problems, MLBP can predict their ORPs directly with the pre-trained regression model, avoiding wasting computational resources on improper regularization parameters. The numerical results also show that MLBP requires signifcantly less computing time and provides more accurate solutions for diferent tasks than traditional methods. Especially, even though the MLBP trains the regression model on synthetic data, it can also achieve satisfying performance when directly applied to feld data.

3

An Efficient Interpolation Approach for Exploring the Parameter Space of Regularized Tomography Algorithms

Lagerwerf Marinus J., Palenstijn Willem Jan, Bleichrodt Folkert, Batenburg K. Joost

Fundamenta Informaticae

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2020

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Vol. 172, nr 2

143--167

EN

Choosing a regularization parameter for tomographic reconstruction algorithms is often a cumbersome task of trial-and-error. Although several automatic and objective criteria have been proposed, each of them yields a different “optimal” value, which may or may not correspond to the actual implicit image quality metrics one would like to optimize for. Exploration of the space of regularization parameters is computationally expensive, as it requires many reconstructions to be computed. In this paper we propose an algorithmic approach for computationally efficient exploration of the regularization parameter space, based on a pixel-wise interpolation scheme. Once a relatively small number of reconstructions have been computed for a sparse sampling of the parameters, an approximation of the reconstructed image for other parameter values can be computed instantly, thereby allowing both manual and automated selection of the most preferable parameters based on a variety of image quality metrics. We demonstrate that for three common variational reconstruction methods, our approach results in accurate approximations of the reconstructed image and that it can be used in combination with existing approaches for choosing optimal regularization parameters.

4

Algorithms of Sustainable Estimation of Unknown Input Signals in Control Systems

Yusupbekov Nadirbek, Igamberdiev Husan, Mamirov Uktam

Journal of Automation Mobile Robotics and Intelligent Systems

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2018

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Vol. 12, No. 4

83--86

EN

The problem of estimating unknown input effects in control systems based on the methods of the theory of optimal dynamic filtering and the principle of expansion of mathematical models is considered. Equations of dynamics and observations of an extended dynamical system are obtained. Algorithms for estimating input signals based on regularization and singular expansion methods are given. The above estimation algorithms provide a certain roughness of the filter parameters to various violations of the conditions of model problems, i.e. are not very sensitive to changes in the a priori data.

5

Modern regularization techniques for inverse modelling: a comparative study

Krawczyk-Stańdo D., Rudnicki M., Stańdo J.

Journal of Applied Computer Science

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2011

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Vol. 19, nr 1

51-63

EN

Regularization techniques are used for computing stable solutions to ill-posed problems. The well-known form of regularization is that of Tikhonov in which the regularized solution is searched as a minimiser of the weighted combination of the residual norm and a side constraint-controlled by the regularization parameter. For the practical choice of regularization parameter we can use the L-curve approach, U-curve criterion introduced by us [1] and empirical risk method [2]. We present a comparative study of different strategies for the regularization parameter choice on examples of function approximation by radial basis neural networks. Such networks are universal approximators and can learn any nonlinear mapping. e.g. representing an magnetic inverse problem. Some integral equations of the first kind are considered as well.

6

Regularization parameter selection in discrete ill-posed problems—The use of the U-curve

Krawczyk-Stańdo D., Rudnicki M.

International Journal of Applied Mathematics and Computer Science

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2007

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Vol. 17, no 2

157-164

EN

To obtain smooth solutions to ill-posed problems, the standard Tikhonov regularization method is most often used. For the practical choice of the regularization parameter \alfa we can then employ the well-known L-curve criterion, based on the L-curve which is a plot of the norm of the regularized solution versus the norm of the corresponding residual for all valid regularization parameters. This paper proposes a new criterion for choosing the regularization parameter \alfa, based on the so-called U-curve. A comparison of the two methods made on numerical examples is additionally included.