Magnetic moment estimation and bounded extremal problems

Laurent Baratchart; Sylvain Chevillard; Douglas Hardin; Juliette Leblond; Eduardo Andrade Lima; Jean-Paul Marmorat

doi:10.3934/ipi.2019003

Article Contents

2019, Volume 13, Issue 1: 39-67. Doi: 10.3934/ipi.2019003

This issue Previous Article Inverse problems for the heat equation with memory Next Article A partial inverse problem for the Sturm-Liouville operator on the lasso-graph

Magnetic moment estimation and bounded extremal problems

1.
Team Factas, Inria, 2004, route des Lucioles - BP 93, 06902 Sophia Antipolis Cedex, France
2.
Department of Mathematics, Vanderbilt University, Nashville, TN 37240, USA
3.
Department of Earth, Atmospheric and Planetary Sciences - MIT, Cambridge, MA 02139, USA
4.
Center of Applied Mathematics, École des Mines ParisTech - CS 10 207, 06904 Sophia Antipolis Cedex, France

* Corresponding author: laurent.baratchart@inria.fr
* Corresponding author: laurent.baratchart@inria.fr

Received: September 30, 2017

Published: December 2018

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Abstract

We consider the inverse problem in magnetostatics for recovering the moment of a planar magnetization from measurements of the normal component of the magnetic field at a distance from the support. Such issues arise in studies of magnetic material in general and in paleomagnetism in particular. Assuming the magnetization is a measure with L²-density, we construct linear forms to be applied on the data in order to estimate the moment. These forms are obtained as solutions to certain extremal problems in Sobolev classes of functions, and their computation reduces to solving an elliptic differential-integral equation, for which synthetic numerical experiments are presented.

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Mathematics Subject Classification: Primary: 35J15, 35R30, 35A35, 42B37, 86A22; Secondary: 45K05, 46F12, 47N20.

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Figure 2. Approximation error of $b_3^*[\phi_{\mathit{\boldsymbol{e}}_k}(\lambda)]$ with respect to $\mathit{\boldsymbol{e}}_k$ when $\lambda$ varies (the scale for $\lambda$ is logarithmic). The solid line corresponds to the case when $k = 1$, while the dashed line corresponds to the case when $k = 3$. As expected from Lemma 4.1, on the one hand, when $\lambda$ goes to $0$ (i.e., $\log_{10}(\lambda) \to -\infty$), the error tends to $0$. On the other hand, when $\lambda$ goes large, the constraint $M(\lambda)$ goes to $0$, meaning that ${\phi _{{\rm{opt}}}}$ is forced to go to $0$, whence the relative error tends to $1$

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Figure 3. Constraint $M(\lambda) = \nabla \phi_{\mathit{\boldsymbol{e}}_k}(\lambda)$ as a function of $\lambda$ (the scale for $\lambda$ is logarithmic). The solid line corresponds to the case when $k = 1$, while the dashed line corresponds to the case when $k = 3$. As expected from Lemma 4.1, these are strictly decreasing smooth functions tending to $+\infty$ when $\lambda \to 0$ (i.e., $\log_{10}(\lambda) \to -\infty$) and tending to $0$ when $\lambda \to +\infty$

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Figure 1. The mesh on $Q$, here with $P = 4$. The points of coordinates $(\kappa_p, \kappa_q)$ ($1 \le p, q \le P$) are represented by bullets. The elementary squares $Q_{p, q}$ overlap, as shown in the diagram

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Figure 4. "L-curves" showing the approximation error $\|b_3^*[\phi_{\mathit{\boldsymbol{e}}_k}(\lambda)]-\mathit{\boldsymbol{e}}_k\|_{L^2(S, \mathbb{R}^3)}/\|\mathit{\boldsymbol{e}}_k\|_{L^2(S, \mathbb{R}^3)}$ as a function of the constraint $M = \|\nabla \phi_{\mathit{\boldsymbol{e}}_k}(\lambda)\|_{L^2(Q, \mathbb{R}^2)}$. The upper plot corresponds to the case when $k = 1$, and the lower one to the case when $k = 3$. As expected, the error decreases and tends to $0$ as the constraint is relaxed

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Figure 5. $\phi_{\mathit{\boldsymbol{e}}_1}(\lambda)$ (top) and $\phi_{\mathit{\boldsymbol{e}}_3}(\lambda)$ (bottom) for $\lambda = 10^{-21}$. On both plots, the rectangles $Q$ (red) and $S$ (blue) are drawn together on the bottom layer to help visualize their respective positions

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Figure 6. Synthetic magnetization (from left to right) $m_1$, $m_2$ and $m_3$ on $S$

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Figure 7. Field $b_3[\mathit{\boldsymbol{m}}]$ corresponding to the synthetic magnetization shown on Figure 6 and an additive Gaussian white noise generated on the same $P \times P$ mesh on $Q$, with $P = 100$. The computed values are used to approximate the field and the noise as functions of $\text{Span}\{\psi_{p, q}\}_{1 \le p, q \le P}$. Note that the color scales are not the same on both pictures

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Table 1. The components $\langle m_k \rangle$ ($k = 1, 2, 3$) of the net moment $\langle \mathit{\boldsymbol{m}} \rangle$ are approximated thanks to the linear estimator as $\mu_k = \langle b, \, \phi_{\mathit{\boldsymbol{e}}_k}(\lambda) \rangle_{L^2(Q)}$ for several values of $\lambda$ and with $b$ being either the exact synthetic field $b_3[\mathit{\boldsymbol{m}}]$ or the exact field plus some noise.
The quantities $\delta_k$ are the relative errors $\delta_k = \big(\mu_k - \langle m_k \rangle\big)/\langle m_k \rangle$. The quantity $\delta_r$ is the relative error of the amplitude of $\mathit{\boldsymbol{\mu}} = (\mu_1, \mu_2, \mu_3)$ as a vector approximation of $\langle \mathit{\boldsymbol{m}}\rangle$, i.e., $\delta_r = \big(\|\mathit{\boldsymbol{\mu}}\|-\|\langle \mathit{\boldsymbol{m}} \rangle\|\big)/\|\langle \mathit{\boldsymbol{m}} \rangle\|$, where $\|\cdot\|$ denotes the Euclidean norm. Finally, $\theta$ is the angle between the vectors $\mathit{\boldsymbol{\mu}}$ and $\langle \mathit{\boldsymbol{m}} \rangle$, i.e., $\theta = \frac{360}{2\pi}\, \arccos\big(\frac{\mathit{\boldsymbol{\mu}}}{\|\mathit{\boldsymbol{\mu}}\|}.\frac{\langle \mathit{\boldsymbol{m}} \rangle}{\|\langle \mathit{\boldsymbol{m}} \rangle\|}\big)$

	No noise					With noise
$\lambda$	$\delta_1$ (%)	$\delta_2$ (%)	$\delta_3$ (%)	$\delta_r$ (%)	$\theta$ (°)	$\delta_r$ (%)	$\theta$ (°)
$10^{-18}$	$ 12.56$	$ 14.77$	$ 3.02$	$-13.10$	$ 2.13$	$-12.93$	$ 2.28$
$10^{-19}$	$ 7.41$	$ 9.15$	$ 2.08$	$ -8.05$	$ 1.23$	$ -7.73$	$ 1.33$
$10^{-20}$	$ 4.91$	$ 5.52$	$ 1.61$	$ -5.01$	$ 0.65$	$ -4.44$	$ 0.70$
$10^{-21}$	$ 3.50$	$ 3.17$	$ 1.25$	$ -3.10$	$ 0.34$	$ -0.41$	$ 1.03$
$10^{-22}$	$ 2.50$	$ 1.71$	$ 0.95$	$ -1.86$	$ 0.26$	$ 6.66$	$ 2.37$
$10^{-23}$	$ 1.71$	$ 0.86$	$ 0.73$	$ -1.08$	$ 0.23$	$ 17.87$	$ 4.34$
$10^{-24}$	$ 1.11$	$ 0.38$	$ 0.53$	$ -0.59$	$ 0.18$	$ 31.97$	$ 5.76$

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