Neural Network Recognition of the Type of Parameterization Scheme for Magnetotelluric Data

Isaev, Igor; Obornev, Eugeny; Obornev, Ivan; Shimelevich, M. I.; Доленко, С. А.

doi:10.1007/978-3-030-01328-8_19

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…The upper half-space (z < 0) is filled with non-conducting air with a permittivity of ε 0 . The magnetic permeability µ is constant and equal to one in all areas of the model [8,19].…”

Section: Resultsmentioning

confidence: 99%

“…Using the tabular formulas of the QFT, transformation of the operator ∆ from [9], and the multiplication rules (23) for Equation (19), the following was obtained:…”

Section: Application Of Qft For a Non-stationary Problemmentioning

confidence: 99%

“…This information can either be entered directly into the algorithm, or used indirectly by taking it into account when forming a training sample. An approach based on the indirect use of additional information, based on alternative measurement methods or structural assumptions, is shown in [19]. The method, which includes the direct introduction of additional information, is considered in the work [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Solution of the Optimization Problem of Magnetotelluric Sounding in Quaternions by the Differential Evolution Method

Kasenov,

Demeubayeva,

Temirbekov

et al. 2024

Computation

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The article discusses the application of quaternion Fourier transforms and quaternion algebra to transform Maxwell’s equations. This makes it possible to present the problem of magnetotelluric sensing (MTS) in a more convenient form for research. Studies of the inverse MTS problem for multi-layer regions are presented using the differential evolution method, which demonstrates high convergence. For single-layer regions, a new method for solving inverse problems based on minimizing the quadratic functional using conjugate optimization methods is considered. Numerical results obtained using special Python libraries are presented, with analysis and conclusions.

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“…The upper half-space (z < 0) is filled with non-conducting air with a permittivity of ε 0 . The magnetic permeability µ is constant and equal to one in all areas of the model [8,19].…”

Section: Resultsmentioning

confidence: 99%

“…Using the tabular formulas of the QFT, transformation of the operator ∆ from [9], and the multiplication rules (23) for Equation (19), the following was obtained:…”

Section: Application Of Qft For a Non-stationary Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solution of the Optimization Problem of Magnetotelluric Sounding in Quaternions by the Differential Evolution Method

Kasenov,

Demeubayeva,

Temirbekov

et al. 2024

Computation

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show abstract

“…In this case, additional information can either fed to the input of the algorithm directly, or be used indirectly, by taking it into account when forming the training sample. An example of an approach associated with the indirect use of additional information can be the use of parameterization schemes with a rigidly defined spatial structure (the so-called "class-generating models" [1][2][3][4]), which is built on the basis of alternative measurement methods or on assumptions about the structure of the specific area. The disadvantage of this approach is the need to develop its own individual solution for each problem, as well as the need to have a priori information about the spatial structure of the defined parameterization.…”

Section: Introductionmentioning

confidence: 99%

Neural network recovery of missing data of one geophysical method from known data of another one in solving inverse problems of exploration geophysics

Isaev¹,

Obornev²,

Obornev³

et al. 2022

Proceedings of the 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)

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This study is devoted to the inverse problems of exploration geophysics, which consist in reconstructing the spatial distribution of the properties of the medium in the Earth's thickness from the geophysical fields measured on its surface. We consider the methods of gravimetry, magnetometry, and magnetotelluric sounding, as well as their integration, i.e. simultaneous use of data from several geophysical methods to solve the inverse problem. In their previous studies, the authors have shown that the integration of geophysical methods allows improving the quality of the solution of the inverse problem in comparison with the individual use of each of them. One of the obstacles to using the integration of geophysical methods can be the situation when for some measurement points there is no data from one of the geophysical methods used. At the same time, the data spaces of different integrated geophysical methods are interconnected, and the values of the observed quantities (fields) for one of the methods can be possibly recovered from the known values of the observed quantities of another geophysical method by constructing a preliminary adaptive mapping of one of the spaces to another. In this study, we investigate the neural network recovery of missing data of one geophysical method from the known data of another one and compare the quality of the solution of the inverse problem on full and on recovered data.

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“…At the same time, when solving the RG problem, the ML methods can be used at various stages of its solution: in data preprocessing, e.g., for noise removal [7,8]; as an independent optimization method [2,3,9] or as a component of optimization methods used to solve the EG problem [10]; as an independent inversion method [1,[11][12][13][14][15][16][17][18][19]; in solving the classification problem to select a class of geological media [20]. In this study, we considered neural networks as an independent inversion method.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Solution of Inverse Problems of Geological Prospecting with Discrete Output

Isaev¹,

Obornev²,

Obornev³

et al. 2021

Proceedings of the 5th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2021)

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The inverse problems of exploration geophysics are to reconstruct the spatial distribution of the properties of the medium in the Earth's thickness from the geophysical fields measured on its surface. In particular, this paper deals with the problems of gravimetry, magnetometry, and magnetotelluric sounding, as well as their integration, i.e., the simultaneous use of several geophysical fields to restore the desired distribution. To implement the integration, a 4-layer 2D model was used, where the inverse problem was to determine the lower boundary of the layers, and each layer was characterized by variable values of the depth of the lower boundary along the section and fixed values of density, magnetization, and resistivity, both for the layer and for the entire data set. To implement the neural network solution of the inverse problem, a data set was generated by solving the direct problem, where for each pattern, the distribution of layer depth values was set randomly in a given range and with a given step, i.e. it took discrete values from a certain set. In this paper, we consider an approach involving the use of neural networks to solve the problem of multiclass classification, where class labels correspond to discrete values of the determined layer depths. The results of the solution are compared with the results of the solution of the same inverse problem in the formulation of the regression problem, in terms of the error in determining the depth of the layers.

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Neural Network Recognition of the Type of Parameterization Scheme for Magnetotelluric Data

Cited by 11 publications

References 11 publications

Solution of the Optimization Problem of Magnetotelluric Sounding in Quaternions by the Differential Evolution Method

Solution of the Optimization Problem of Magnetotelluric Sounding in Quaternions by the Differential Evolution Method

Neural network recovery of missing data of one geophysical method from known data of another one in solving inverse problems of exploration geophysics

Neural Network Solution of Inverse Problems of Geological Prospecting with Discrete Output

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