A level-set algorithm for the inverse problem of full magnetic gradient tensor data

Wenbin Li; Jianliang Qian

doi:10.1016/j.aml.2020.106416

A level-set algorithm for the inverse problem of full magnetic gradient tensor data

Wenbin Li
, Jianliang Qian^*

^*Corresponding author for this work

Harbin Institute of Technology
Michigan State University

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

We propose a level-set approach for recovering the susceptibility distribution from five-component full magnetic gradient tensor data. Given a value of the average susceptibility, the level-set inversion inverts for the susceptibility distribution so as to depict the shape of the underlying magnetic source. Numerical examples illustrate that the method is able to recover multiple magnetic sources from noisy data.

Original language	English
Article number	106416
Journal	Applied Mathematics Letters
Volume	107
DOIs	https://doi.org/10.1016/j.aml.2020.106416
State	Published - Sep 2020
Externally published	Yes

Keywords

Inverse problem
Level set method
Magnetic gradient tensor

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10.1016/j.aml.2020.106416

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title = "A level-set algorithm for the inverse problem of full magnetic gradient tensor data",

abstract = "We propose a level-set approach for recovering the susceptibility distribution from five-component full magnetic gradient tensor data. Given a value of the average susceptibility, the level-set inversion inverts for the susceptibility distribution so as to depict the shape of the underlying magnetic source. Numerical examples illustrate that the method is able to recover multiple magnetic sources from noisy data.",

keywords = "Inverse problem, Level set method, Magnetic gradient tensor",

author = "Wenbin Li and Jianliang Qian",

note = "Publisher Copyright: {\textcopyright} 2020 Elsevier Ltd",

year = "2020",

month = sep,

doi = "10.1016/j.aml.2020.106416",

language = "英语",

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T1 - A level-set algorithm for the inverse problem of full magnetic gradient tensor data

AU - Li, Wenbin

AU - Qian, Jianliang

PY - 2020/9

Y1 - 2020/9

N2 - We propose a level-set approach for recovering the susceptibility distribution from five-component full magnetic gradient tensor data. Given a value of the average susceptibility, the level-set inversion inverts for the susceptibility distribution so as to depict the shape of the underlying magnetic source. Numerical examples illustrate that the method is able to recover multiple magnetic sources from noisy data.

AB - We propose a level-set approach for recovering the susceptibility distribution from five-component full magnetic gradient tensor data. Given a value of the average susceptibility, the level-set inversion inverts for the susceptibility distribution so as to depict the shape of the underlying magnetic source. Numerical examples illustrate that the method is able to recover multiple magnetic sources from noisy data.

KW - Inverse problem

KW - Level set method

KW - Magnetic gradient tensor

UR - https://www.scopus.com/pages/publications/85083818004

U2 - 10.1016/j.aml.2020.106416

DO - 10.1016/j.aml.2020.106416

M3 - 文章

AN - SCOPUS:85083818004

SN - 0893-9659

VL - 107

JO - Applied Mathematics Letters

JF - Applied Mathematics Letters

M1 - 106416

ER -

A level-set algorithm for the inverse problem of full magnetic gradient tensor data

Abstract

Keywords

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