This article is cited in 1 scientific paper (total in 1 paper)
Gradient methods for solving inverse gravimetry and magnetometry problems on the Uran supercomputer
E. N. Akimova, V. E. Misilov, A. F. Skurydina, A. I. Tret'yakov
Institute of Mathematics and Mechanics, Ural Branch of the Russian Academy of Sciences, Ekaterinburg
A modified linearized steepest descent method with variable weight factors is proposed to solve three-dimensional structural inverse gravimetry and magnetometry problems of finding the interfaces between constant density or magnetization layers in a multilayer medium. A linearized conjugate gradient method and its modified version with weight factors for solving the gravimetry and magnetometry problems in a multilayer medium is constructed. On the basis of the modified gradient-type methods, a number of efficient parallel algorithms are numerically implemented on an Intel multi-core processor and NVIDIA GPUs. The developed parallel iterative algorithms are compared for a model problem in terms of the relative error, the number of iterations, and the execution time.
inverse gravimetry and magnetometry problems, parallel algorithms, gradient-type methods, multi-core and graphics processors.
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E. N. Akimova, V. E. Misilov, A. F. Skurydina, A. I. Tret'yakov, “Gradient methods for solving inverse gravimetry and magnetometry problems on the Uran supercomputer”, Num. Meth. Prog., 16:1 (2015), 155–164
Citation in format AMSBIB
\by E.~N.~Akimova, V.~E.~Misilov, A.~F.~Skurydina, A.~I.~Tret'yakov
\paper Gradient methods for solving inverse gravimetry and magnetometry problems on the Uran supercomputer
\jour Num. Meth. Prog.
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V. V. Vasin, A. F. Skurydina, “A two-stage method of construction of regularizing algorithms for nonlinear ill-posed problems”, Proc. Steklov Inst. Math. (Suppl.), 301, suppl. 1 (2018), 173–190
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