Computational tools for microwave imaging -- [2pt] some ... · 3 Microwave imaging of the ... human...
Transcript of Computational tools for microwave imaging -- [2pt] some ... · 3 Microwave imaging of the ... human...
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Computational tools for microwave imaging –some finite element aspects
Christoph Schwarzbach, Eldad Haber
University of British Columbia, Vancouver, Canada
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Physical methods to investigate the . . .
human – medical imaging earth – geophysics
I ultrasound I seismicsI ultrawideband (UWB) mi-
crowave backscatteringI ground penetrating radar
(GPR)I microwave tomography I controlled source electro-
magnetics (CSEM)I electrical impedance to-
mography (EIT)I direct current (DC) resitivity
I magnetic resonance imag-ing (MRI)
I nuclear magnetic reso-nance (NMR)
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Microwave imaging of the . . .
human earth
length scale 0.1–1 cm 1–100 melectrical conductivity 0.01–1 S/m 0.01–1 S/mrelative electrical permittivity 1–81 1–81interrogation frequency 0.1–10 GHz 0.1–100 Hzskindepth 3.6–36 cm 0.036–3.6 kmwavelength 0.33–242 cm 0.32–32 km
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Image reconstruction as an optimisation problem
Ωe
Ωi
Γ
Consider a physical experiment that re-lates a constitutive parameter m, definedon Ω = Ωi ∪Ωe, to a physical observ-able d(m) on Γ ⊂ Ω. Given observeddata dobs on Γ ⊂ Ω and m on Ωe, weseek to reconstruct the spatial distribu-tion of m within Ωi such that d(m) matches dobs.
m∗ = arg minm
φ(m)
φ(m) =12
∥∥d(m)− dobs∥∥2 + αR(m)
1. forward problem d(m)2. regularisation operator R(m)3. optimisation of φ(m)4. choice of α
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1. Forward problem d(m)
d(m) = Qu = QA(m)−1f
f . . . sourcesA(m) . . . forward operatoru . . . physical state in ΩQ . . . observation operator Ω→ Γ
Here: microwave tomographyI A(m) is FE discretisation of time-harmonic Maxwell’s equationsI m electrical permittivity ε and conductivity σ
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Forward operator A(m) discretises∫Ω∇×Φ · µ−1∇× E dV
−∫
ΩΦ · iω(σ− iωε)E dV =
∫Ω
Φ · iωjs dV
FEM: Ω = ∪k Ωk ; E(x)→ ∑j uj Φj(x); Φ→ Φi
Assumption: µ, ε, σ elementwise constant
Au = f
Ai,j = ∑k
µ−1k
∫Ωk
∇×Φi · ∇ ×Φj dV
−∑k
iω(σk − iωεk )∫
Ωk
Φi ·Φj dV
fi =∫
ΩΦi · iωjs dV
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Note:
Ai,j = ∑k
µ−1k
∫Ωk
∇×Φi · ∇ ×Φj dV
−∑k
iω(σ− iωε)∫
Ωk
Φi ·Φj dV
leads to a sparse derivative matrix ∇m(A(m)ufix) since
∂Ai,j
∂µk= −µ−2
k
∫Ωk
∇×Φi · ∇ ×Φj dV
∂Ai,j
∂σk= −iω
∫Ωk
Φi ·Φj dV
∂Ai,j
∂εk= −ω2
∫Ωk
Φi ·Φj dV
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2. Regularisation operator R(m)Primal-dual finite element formulation for ‖∇(m−mref)‖2 withelementwise constant m,
minm
maxω
12
∥∥d(m)− dobs∥∥2 + αR(m, ω)
where ω ∈ H(div; Ω) and
R(m, ω) = −12‖ω‖2 − (m−mref,∇ ·ω)
RT0 discretisation of ω
R(m, w) = −12
w>Mw w − (m−mref)>Dw
∇w R(m, w) = 0
w = −M−1w D>(m−mref)
R(m) =12(m−mref)>DM−1
w D>(m−mref)
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2. Regularisation operator R(m)Primal-dual finite element formulation for ‖∇(m−mref)‖2 withelementwise constant m,
minm
maxω
12
∥∥d(m)− dobs∥∥2 + αR(m, ω)
where ω ∈ H(div; Ω) and
R(m, ω) = −12‖ω‖2 − (m−mref,∇ ·ω)
RT0 discretisation of ω
R(m, w) = −12
w>Mw w − (m−mref)>Dw
∇w R(m, w) = 0
w = −M−1w D>(m−mref)
R(m) =12(m−mref)>DM−1
w D>(m−mref)
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3. Optimisation of φ(m)
φ(m) =12
((d(m)− dobs)>(d(m)− dobs)
+ α(m−mref)>DM−1w D>(m−mref)
)Necessary condition g(m) := ∇mφ(m) = 0.
Solve by Gauss-Newton method.Given an initial model m0, iterate for ` = 0, 1, . . .
m`+1 = m` + γ`δm`
where δm` solves(J(m`)>J(m`) + αDM−1
w D>)δm` = −g(m`)
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Predicted data
d(m`) = Qu` = QA(m`)−1f
Gradient
g(m`) = J(m`)>(d(m`)− dobs) + αDM−1w D>(m` −mref)
Sensitivity matrix
J(m`) = −Q>A(m`)−1G(m`)
where G(m`) = ∇m`(A(m`)ufix
` ).
If(J(m`)>J(m`) + αDM−1
w D>)δm` = −g(m`) is solved iteratively,
the matrix-vector products involving J(m`) and J(m`)> only requireoperations with sparse matrices Q, A and G.
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4. Choice of αIterated Tikhonov regularisation:Set mref = m` at Gauss-Newton step ` and γ` = 1.(
J(m`)>J(m`) + αDM−1w D>
)(m`+1 −m`) =
− J(m`)>(d(m`)− dobs)
Let DM−1w D> = C>C, m = Cm, J(m) = J(m)C−1 and rename
α = 1/δt . Then
1δt
(m`+1 − m`) =
− J(m`)>(J(m`)(m`+1 − m`) + d(m`)− dobs)
⇒ semi-implicit time stepping
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Example
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mesh and true model
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mesh and true model
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true model
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predicted model
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true model
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predicted model
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Road to the future . . .
high resolution and high contrast?
multi-modal imaging joint inversion