Two-Dimensional Magnetotelluric Modeling Based on Stochastic Path Integral: TE Case

In this study, we investigate the application of the stochastic path integral (SPI) for 2-D magnetotelluric (MT) modeling. Our goal is to assess whether electromagnetic (EM) numerical algorithms can mechanistically adapt to parallel heterogeneous computing architectures and maximize the conversion o...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 63; s. 1 - 11
Hlavní autori:	Zhou, Hongyu, Li, Maokun, Yang, Fan, Xu, Shenheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Computational electromagnetics Computational modeling Conductivity Finite difference method Helmholtz equations Integral equations magnetotellurics (MTs) Mathematical models Modelling Monte Carlo (MC) simulation Monte Carlo methods numerical modeling Numerical models parallel computing Parallel processing Random walk Shape stochastic algorithm Stochastic processes Vectors
ISSN:	0196-2892, 1558-0644
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Abstract	In this study, we investigate the application of the stochastic path integral (SPI) for 2-D magnetotelluric (MT) modeling. Our goal is to assess whether electromagnetic (EM) numerical algorithms can mechanistically adapt to parallel heterogeneous computing architectures and maximize the conversion of computational power into efficient EM field simulations. Using the Feynman-Kac formula, we derive a path integral representation of the MT Helmholtz equation in a stochastic framework. We can then investigate the online-offline two-stage MT-SPI algorithm that includes Monte Carlo (MC) random walks, mapping matrix construction, and large-scale matrix-vector multiplication. Numerical experiments verify the correctness and stability of the proposed SPI algorithm. At the relative error of 1.2%, SPI simultaneously achieves over <inline-formula> <tex-math notation="LaTeX">550\times </tex-math></inline-formula> acceleration and approximately 30% memory reduction on GPU under a MATLAB-based implementation, compared with the finite difference method (FDM).
AbstractList	In this study, we investigate the application of the stochastic path integral (SPI) for 2-D magnetotelluric (MT) modeling. Our goal is to assess whether electromagnetic (EM) numerical algorithms can mechanistically adapt to parallel heterogeneous computing architectures and maximize the conversion of computational power into efficient EM field simulations. Using the Feynman–Kac formula, we derive a path integral representation of the MT Helmholtz equation in a stochastic framework. We can then investigate the online–offline two-stage MT-SPI algorithm that includes Monte Carlo (MC) random walks, mapping matrix construction, and large-scale matrix–vector multiplication. Numerical experiments verify the correctness and stability of the proposed SPI algorithm. At the relative error of 1.2%, SPI simultaneously achieves over [Formula Omitted] acceleration and approximately 30% memory reduction on GPU under a MATLAB-based implementation, compared with the finite difference method (FDM). In this study, we investigate the application of the stochastic path integral (SPI) for 2-D magnetotelluric (MT) modeling. Our goal is to assess whether electromagnetic (EM) numerical algorithms can mechanistically adapt to parallel heterogeneous computing architectures and maximize the conversion of computational power into efficient EM field simulations. Using the Feynman-Kac formula, we derive a path integral representation of the MT Helmholtz equation in a stochastic framework. We can then investigate the online-offline two-stage MT-SPI algorithm that includes Monte Carlo (MC) random walks, mapping matrix construction, and large-scale matrix-vector multiplication. Numerical experiments verify the correctness and stability of the proposed SPI algorithm. At the relative error of 1.2%, SPI simultaneously achieves over <inline-formula> <tex-math notation="LaTeX">550\times </tex-math></inline-formula> acceleration and approximately 30% memory reduction on GPU under a MATLAB-based implementation, compared with the finite difference method (FDM).
Author	Li, Maokun Xu, Shenheng Yang, Fan Zhou, Hongyu
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SubjectTerms	Algorithms Computational electromagnetics Computational modeling Conductivity Finite difference method Helmholtz equations Integral equations magnetotellurics (MTs) Mathematical models Modelling Monte Carlo (MC) simulation Monte Carlo methods numerical modeling Numerical models parallel computing Parallel processing Random walk Shape stochastic algorithm Stochastic processes Vectors
Title	Two-Dimensional Magnetotelluric Modeling Based on Stochastic Path Integral: TE Case
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