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The Calculation of High-Order Vertical Derivative in Gravity Field by Tikhonov Regularization Iterative Method
Author(s) -
Wei Du,
Yangyang Zhang
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/8818552
Subject(s) - tikhonov regularization , regularization (linguistics) , backus–gilbert method , mathematics , zeta function regularization , inverse problem , iterative method , second derivative , regularization perspectives on support vector machines , derivative (finance) , mathematical analysis , mathematical optimization , algorithm , computer science , artificial intelligence , prime zeta function , arithmetic zeta function , financial economics , economics , riemann hypothesis
In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems, regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting. The Tikhonov regularization method is widely used to solve complex problems in engineering. The vertical derivative of gravity can highlight the local anomalies and separate the horizontal superimposed abnormal bodies. The higher the order of the vertical derivative is, the stronger the resolution is. However, it is generally considered that the conversion of the high-order vertical derivative is unstable. In this paper, based on Tikhonov regularization for solving the high-order vertical derivatives of gravity field and combining with the iterative method for successive approximation, the Tikhonov regularization method for calculating the vertical high-order derivative in gravity field is proposed. The recurrence formula of Tikhonov regularization iterative method is obtained. Through the analysis of the filtering characteristics of this method, the high-order vertical derivative of gravity field calculated by this method is stable. Model tests and practical data processing also show that the method is of important theoretical significance and practical value.

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