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2. Joint Gravity and Magnetic Inversion Using CNNs' Deep Learning.

Author

Bai, Zhijing, Wang, Yanfei, Wang, Chenzhang, Yu, Caixia, Lukyanenko, Dmitry, Stepanova, Inna, and Yagola, Anatoly G.

Subjects
ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, INVERSION (Geophysics), DEEP learning, GRAVITY, MACHINE learning, MAGNETIC fields
Abstract

Enhancing the reliability of inversion results has always been a prominent issue in the field of geophysics. In recent years, data-driven inversion methods leveraging deep neural networks (DNNs) have gained prominence for their ability to address non-uniqueness issues and reduce computational costs compared to traditional physically model-driven methods. In this study, we propose a GMNet machine learning method, i.e., a CNN-based inversion method for gravity and magnetic field data. This method relies more on data-driven training, and in the prediction phase after the model is trained, it does not heavily depend on a priori assumptions, unlike traditional methods. By forward modeling gravity and magnetic fields, we obtain a substantial dataset to train the CNN model, enabling the direct mapping from field data to subsurface property distribution. Applying this method to synthetic data and one-field data yields promising inversion results. [ABSTRACT FROM AUTHOR]

Published
2024
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6. On the Unique Solvability of Inverse Problems of Magnetometry and Gravimetry.

Author

Stepanova, Inna, Lukyanenko, Dmitry, Kolotov, Igor, Shchepetilov, Alexey, and Yagola, Anatoly

Subjects
*INVERSION (Geophysics), *ALGEBRAIC equations, *INTEGRAL representations, *GRAVIMETRY, *INTEGRAL equations
Abstract

This article deals with the question of the unique solvability of systems of linear algebraic equations, to the solution of which many inverse problems of geophysics are reduced as a result of discretization when applying the methods of integral equations or integral representations. Examples are given of degenerate and nondegenerate systems of different dimensions that arise in the processing of magnetometric and gravimetric data from experimental observations. Conclusions are drawn about the methods for constructing the optimal grid of experimental observation points. [ABSTRACT FROM AUTHOR]

Published
2023
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15. О разрушении решений одного полного нелинейного уравнения ионно-звуковых волн в плазме с некоэрцитивными нелинейностями

Author

Корпусов, Максим Олегович, primary, Korpusov, Maxim Olegovich, primary, Лукьяненко, Дмитрий Витальевич, additional, Lukyanenko, Dmitry Vitalyevich, additional, Панин, Александр Анатольевич, additional, Panin, Alexander Anatolyevich, additional, Юшков, Егор Владиславович, additional, and Yushkov, Egor Vladislavovich, additional

Published
2018
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