Inverse source estimation problems in magnetostatics

Transcription

1 Inverse source estimation problems in magnetostatics ERNSI Workshop, Lyon, 2017 Juliette Leblond INRIA Sophia Antipolis (France), Team APICS Joint work with: Laurent Baratchart, Sylvain Chevillard, Doug Hardin, Eduardo Lima, Jean-Paul Marmorat, Dmitry Ponomarev

2 Context APICS team, Inria Sophia Antipolis, France Earth, Atmospheric & Planetary Sciences Dep., MIT Cambridge, Massachusetts, USA Center for Constructive Approximation Vanderbilt University, Nashville, Tennessee, USA Planetary sciences, paleomagnetism, remanent magnetization of ancient rocks history and future of Earth magnetic field Magnetization not measurable measures of generated magnetic field inverse problems, non destructive inspection

3 How do rocks acquire magnetization? Igneous rocks (from Earth volcanoes, lava, magma; basalts) Thermoremanent magnetization, ferromagnetic particles (small magnets) follow the magnetic field:

4 How do rocks acquire magnetization? Igneous rocks (from Earth volcanoes, lava, magma; basalts) Thermoremanent magnetization, ferromagnetic particles (small magnets) follow the magnetic field:

5 How do rocks acquire magnetization? Igneous rocks (from Earth volcanoes, lava, magma; basalts) Thermoremanent magnetization, ferromagnetic particles (small magnets) follow the magnetic field:

6 How do rocks acquire magnetization? Igneous rocks (from Earth volcanoes, lava, magma; basalts) Thermoremanent magnetization, ferromagnetic particles (small magnets) follow the magnetic field:

7 How do rocks acquire magnetization? Igneous rocks (from Earth volcanoes, lava, magma; basalts) Thermoremanent magnetization, ferromagnetic particles (small magnets) follow the magnetic field:

8 How do rocks acquire magnetization? Igneous rocks (from Earth volcanoes, lava, magma; basalts) Thermoremanent magnetization, ferromagnetic particles (small magnets) follow the magnetic field: Can be subsequently altered, under high pressure or temperature

9 SQUID microscope Scanning magnetic microscopes: for weakly magnetized small samples (MIT, EAPS) pedestal + sensor sapphire window map of the vertical component of the (tiny) magnetic field on a rectangular region Q above the sample S

10 General scheme Thin sample planar (rectangle) S R 2 M = m = (m1, m 2, m 3 ) t support of unknown magnetization (source term) m B 3 on Q m on S?

11 General scheme Thin sample planar (rectangle) S R 2 M = m = (m1, m 2, m 3 ) t support of unknown magnetization (source term) m B 3 on Q m on S?

12 General scheme Thin sample planar (rectangle) S R 2 M = m = (m1, m 2, m 3 ) t support of unknown magnetization (source term) m B 3 on Q m on S?

13 Hawaiian Basalt example picture of thin sample magnetization recovery measures (nt ) of B3 on Q {h} estimated magnetization (Am2 ) m on S {0} knowing direction of m & after re-magnetization (MIT, EAPS) not always feasible...

14 Problem setting, B 3 = B 3 [m] m generates a magnetic field at x S: B[m](x) = µ 0 U[m](x) where U[m](x) = 1 m(y), x y 4π x y 3 dy S U scalar magnetic potential, µ 0 magnetic constant, 3D gradient

15 Problem setting, B 3 = B 3 [m] m generates a magnetic field at x S: B[m](x) = µ 0 U[m](x) where U[m](x) = 1 m(y), x y 4π x y 3 dy S U scalar magnetic potential, µ 0 magnetic constant, 3D gradient measurements b 3 [m] of vertical component B 3 [m] = µ 0 x3 U[m] performed on square Q R 2 at height h (incomplete data) xi partial derivative w.r.t. x i

16 Problem setting, B 3 = B 3 [m] m generates a magnetic field at x S: B[m](x) = µ 0 U[m](x) where U[m](x) = 1 m(y), x y 4π x y 3 dy S U scalar magnetic potential, µ 0 magnetic constant, 3D gradient measurements b 3 [m] of vertical component B 3 [m] = µ 0 x3 U[m] performed on square Q R 2 at height h (incomplete data) xi partial derivative w.r.t. x i inverse problems: from pointwise values of b 3 [m] on Q recover magnetization m or net moment m on S m 1 m = m 2, m i = m 3 S m i (y) dy average, m i L 2 (S) L 1 (S), i = 1, 2, 3

17 Operators Put X = (x 1, x 2, x 3 ) R 3 grad = = x1 x2 x3 with xi = xi div =, curl = Laplacian = = div (grad) = = 2 x x x3 2

18 Inverse recovery problems Poisson-Laplace equation with source term in divergence form: Maxwell, magnetostatics, time-harmonic, macroscopic U[m] = m in R 3, supp m S {0} B 3 = B 3 [m] = x3 U[m] U[m] and B 3 [m] harmonic functions in {x 3 > 0} b 3 [m] = B 3 [m] Q {h} on Q {h} m or m on S {0}? m = m(y) d y R 3, m L 2 (S, R 3 ), b 3 [m] L 2 (Q) S Integral expression for (x 1, x 2 ) Q (also in terms of Poisson and Riesz transforms) b 3 [m] (x 1, x 2 ) = µ 0 4π m 1 (y) (x 1 y 1 ) + m 2 (y) (x 2 y 2 ) + m 3 (y) x 3 x3 ( S (x1 y 1 ) 2 + (x 2 y 2 ) 2 + x3 2 ) dy 3/2 x 3 =h y = (y 1, y 2 )

19 Inverse recovery problems Data: b 3 [m] on Q net moment m i estimation: uniqueness, unstability regularization (BEP) preliminary step for magnetization recovery (non-uniqueness): mean values m i of m i on S m direction of m

20 Inverse recovery problems Linear map: b 3 : { L 2 (S, R 3 ) L 2 (Q) m B 3 [m] Q {h} = b 3 [m] Magnetization recovery (full inversion): recover m from b 3 [m] ill-posed (non uniqueness) because Ker b 3 {0}, silent sources

21 Inverse recovery problems Linear map: b 3 : { L 2 (S, R 3 ) L 2 (Q) m B 3 [m] Q {h} = b 3 [m] Magnetization recovery (full inversion): recover m from b 3 [m] ill-posed (non uniqueness) because Ker b 3 {0}, silent sources Moment recovery: well-defined i {1, 2, 3} { Ran b3 R b 3 [m] m i

22 Inverse recovery problems Linear map: b 3 : { L 2 (S, R 3 ) L 2 (Q) m B 3 [m] Q {h} = b 3 [m] Magnetization recovery (full inversion): recover m from b 3 [m] ill-posed (non uniqueness) because Ker b 3 {0}, silent sources Moment recovery: well-defined i {1, 2, 3} { Ran b3 R b 3 [m] m i Strategy: linear estimator m of given norm, once and for all find test functions φ i L 2 (Q) such that φ i, b 3 [m] L 2 (Q) m i best constrained approximation problems (BEP) Direct inversion of associated discrete problem: heavy, unstable and asymptotic formulas, for large measurement area Q

23 Strategy for net moment recovery Determine φ i L 2 (Q) such that b 3 [m], φ i Q m i = m, e i S.,. L 2 (Q),.,. L 2 (Q,R 2 ).,. Q,. Q,.,. S.,. S,. S e 1 = (1, 0, 0), e 2 = (0, 1, 0), e 3 = (0, 0, 1) on S b 3 [m], φ i Q = m, b 3 [φ i ] S with adjoint operator b 3 : L2 (Q) L 2 (S, R 3 ) to b 3 linear estimator for moment m i given bound on m S trade-off between prescribed accuracy and norm of φ i in Sobolev space, L 2 norm of gradient of φ stability w.r.t. measurement errors, robustness

24 Tools for analysis Properties of Hilbert Sobolev spaces W 1,2 0 (Ω) L 2 (Ω) Ω R 2 Integral expression of b 3 [m] in terms of Poisson and Riesz transforms Properties of operator b 3 and its adjoint b 3 e i Ker b 3 : silent sources possess vanishing net moments uniqueness of m from b 3 [m] Ran b 3 dense in L 2 (Q)

25 Net moment recovery, strategy no hope to find φ L 2 (Q) such that b 3 [m], φ Q m i = 0 m L 2 (S, R 3 ) because e i Ran b3 and b 3 [m], φ Q m i = m, b3 [φ] e i S however, density result for m L 2 (S, R 3 ) φ n W 1,2 0 (Q) s.t. b 3 [m], φ n Q m i 0 as n inf 1,2 b3 [m], φ Q m i = 0 φ W (Q) 0 but it satisfies 2 φ n Q unstability for m L 2 (S, R 3 ), we can find φ W 1,2 0 (Q) L 2 (Q) s.t. b 3 [m], φ Q m i arbitrarily small at the expense of arbitrarily large 2 φ Q needs for regularization, trade-off (error / constraint)

26 Bounded extremal problem b 3 [m], φ Q m i = m, b 3 [φ] e i S m S b 3 [φ] e i S Best constrained approximation, optimization issue M > 0 unique solution φ W 1,2 0 (Q) to (BEP) b 3 [φ ] e i S = min φ W 1,2 0 (Q), 2 φ Q M b 3 [φ] e i S it satisfies the critical point equation (CPE) and 2 φ Q = M Tykhonov type regularization b 3 b 3 [φ ] λ 2 φ = b 3 [e i ] on Q for unique λ > 0 s.t. 2 φ Q = M also, φ C 1/2 ( Q) Lagrange parameter λ 0 iff constraint M & criterion error 0 resolution algorithm: iterate in λ

27 Ongoing, solving (CPE), preliminary numerics m = (m 1, m 2, m 3 ) on S b 3 [m] on Q synthetic data (matlab) simulated m, b 3 [m] R = 2.55, s = 1.97, h = 0.27 mm, Q / S 1.3, S /h overlapping squares, finite elements P 0 on S Q 1 on Q, for W 1,2 0 (Q) basis functions ψ (CPE) Dirichlet problems for elliptic 2D PDE: φ W 1,2 0 (Q), φ Q = 0 b 3 [φ ], b 3 [ψ] S + λ 2 φ, 2 ψ Q = b 3 [e i ], ψ Q

28 Solving (CPE), preliminary numerics towards a numerical magnetometer, φ = φ ei, i = 1, 2, kb $ 3 [?e1(6)]! e1k L 2 (S;R 3 ) ke1k L 2 (S;R 3 ) kb $ 3 [?e3(6)]! e3k L 2 (S;R 3 ) ke3k L 2 (S;R 3 ) kr?e1 (6)k L 2 (Q;R 2 ) kr?e3 (6)k L 2 (Q;R 2 ) log 10(6) log 10(6) criterion error (cost) on S constraint M on Q b 3 [φ e i ] e i S 2 φ ei Q for i = 1, 3, w.r.t. log 10 (λ)

29 Solving (CPE), preliminary numerics kb $ 3 [?e1(6)]! e1k L 2 (S;R 3 ) 0.45 kb $ 3 [?e3(6)]! e1k L 2 (S;R 3 ) = 10!4 ke1k L 2 (S;R 3 ) = 10!4 ke3k L 2 (S;R 3 ) = 10! = 10! = 10! = 10! = 10! = 10! = 10!8 6 = 10! = 10!10 6 = 10!11 6 = 10! M = kr?e1 (6)k L 2 (Q;R 2 ) = 10! = 10!9 6 = 10! = 10!11 6 = 10! M = kr?e3 (6)k L 2 (Q;R 2 ) L-curves, i = 1 (left), i = 3 (right): criterion error on S w.r.t. constraint M on Q, at values of λ b3 [φe i ] e Q i w.r.t. 2 φ ei S trade-off between error and constraint

30 Solving (CPE), preliminary numerics φ ei (λ 1 ) on Q, for i = 1, 2, 3 λ 1 = φ ei (λ 2 ) on Q, for i = 1, 2, 3 λ 2 = 10 8

31 Solving (CPE), preliminary numerics errors ɛ k on component k = 1, 2, 3 of b 3 [ ] φ ei (λ 1 ) e i on S for i = 1, 2, 3 (rows) λ 1 = 10 11

32 Solving (CPE), preliminary numerics 1st row: actual values of components m i, i = 1, 2, e e e-05 λ = e e e % e e e % e e e % e e e % e e e % e e e % e e e % next rows: estimated values values b 3 [m], φ ei [λ] Q last column: relative errors on moment m

33 Ongoing, next Resolution schemes, numerical analysis (ctn), test noisy & actual data & other resolution schemes: iterative, φ n φ = φ ei n λ ϱ 2 φ n + φ n = ϱ b 3 b3 [ ] φn 1 + φn 1 + ϱ b 3 [e i ] and φ n Q = 0, n 1 in Fourier basis asymptotic estimations product of sin of separated variables Q > S or R > s Local moments determined by b 3 [m] if m of minimal L 2 (S) norm (BEP) for general e Ran b 3 \ Ran b 3 higher order moments Use m for magnetization m estimation

34 References L. Baratchart, D. Hardin, E.A. Lima, E.B. Saff, B.P. Weiss, Characterizing kernels of operators related to thin-plate magnetizations via generalizations of Hodge decompositions, Inverse Problems, 2013 L. Baratchart, S. Chevillard, J. Leblond, Silent and equivalent magnetic distributions on thin plates, Theta Series in Advanced Mathematics, to appear, hal Also, in planetary sciences, paleomagnetism: magnetizations m in L 1 (S, R 3 ) or distributions for 3D samples S, pointwise dipolar sources chondrules, also Moon rocks,... In brain imaging (medical engineering), electroencephalography (EEG), source and conductivity inverse problems, spherical geometry, software FindSources3D ( + MEG)