Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Introduction to 3D MT inversion code x3Di
Anna Avdeeva ([email protected]), Dmitry Avdeevand Marion Jegen
31 March 2011
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Outline
1 Essential Parts of 3D MT Inversion Code X3DIOptimization MethodCalculation of the GradientsParametrizationRegularization
2 Salt Dome Overhang Detectability Study with x3Di
3 Conclusions
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Outline
1 Essential Parts of 3D MT Inversion Code X3DIOptimization MethodCalculation of the GradientsParametrizationRegularization
2 Salt Dome Overhang Detectability Study with x3Di
3 Conclusions
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
How 3D inverse problem is commonly solved
Traditionally solution is sought as a stationary point of a penaltyfunction
ϕ(m, λ) = ϕd(m) + λϕs(m) −→m
min (1)
ϕd(m) data misfit
ϕs(m) Tikhonov-type stabilizer
λ regularization parameter
m vector of model parameters (conductivities)
ϕd(m) =1
2
∥∥dobs − F (m)∥∥2 (2)
F (m) is a forward problem mapping
ϕs(m) =1
2
∥∥W(m−mref )∥∥2 (3)
mref vector of model parameters, for some reference model,which usually include some a priori information.
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Where are the differences between 3D inversioncodes?
1 model parameters (σ, ρ, log ρ, log σ etc.)
2 forward problem solver (FD, FE or IE)
- X3D
3 optimization method (GN, QN, LMQN, NLCG etc.)
4 form of the data misfit ϕd
5 form of the stabilizer ϕs
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Where are the differences between 3D inversioncodes?
1 model parameters (σ, ρ, log ρ, log σ etc.)
2 forward problem solver (FD, FE or IE)
- X3D
3 optimization method (GN, QN, LMQN, NLCG etc.)
4 form of the data misfit ϕd
5 form of the stabilizer ϕs
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Where are the differences between 3D inversioncodes?
1 model parameters (σ, ρ, log ρ, log σ etc.)
2 forward problem solver (FD, FE or IE) - X3D
3 optimization method (GN, QN, LMQN, NLCG etc.)
4 form of the data misfit ϕd
5 form of the stabilizer ϕs
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Where are the differences between 3D inversioncodes?
1 model parameters (σ, ρ, log ρ, log σ etc.)
2 forward problem solver (FD, FE or IE) - X3D
3 optimization method (GN, QN, LMQN, NLCG etc.)
4 form of the data misfit ϕd
5 form of the stabilizer ϕs
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Optimization method
All iterative methods work at the same manner. At each iteration lthey find:
1 a search direction vector p(l)
2 a step length α(l) using an inexact line search along p(l)
3 the next, improved model given by m(l+1) = m(l) + α(l)p(l)
The difference is in how a specific method finds the search directionand in what price is paid for this.
NLCG p(l) = −g(l) + γ(l)p(l−1), where γ(l) = g(l)·g(l)g(l−1)·g(l−1)
Newton, GN H(l)p(l) = −g(l)
QN m(i), g(i) : i = 1, ..., l → p(l)
LMQN m(i), g(i) : i = l − ncp, ..., l → p(l)
H(l)ij = ∂2ϕ
∂mi∂mj(m(l)), g
(l)i = ∂ϕ
∂mi(m(l))
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Optimization method
All iterative methods work at the same manner. At each iteration lthey find:
1 a search direction vector p(l)
2 a step length α(l) using an inexact line search along p(l)
3 the next, improved model given by m(l+1) = m(l) + α(l)p(l)
The difference is in how a specific method finds the search directionand in what price is paid for this.
NLCG p(l) = −g(l) + γ(l)p(l−1), where γ(l) = g(l)·g(l)g(l−1)·g(l−1)
Newton, GN H(l)p(l) = −g(l)
QN m(i), g(i) : i = 1, ..., l → p(l)
LMQN m(i), g(i) : i = l − ncp, ..., l → p(l)
H(l)ij = ∂2ϕ
∂mi∂mj(m(l)), g
(l)i = ∂ϕ
∂mi(m(l))
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Adjoint approach
Calculation of gradients requires 2 forward modellings ateach frequency
Important: Straightforward calculation of the gradientswould require N + 1 forward modellings, i.e. in ≈ N/2times more
Example: number of model parameters = 3000
Single forward modelling requires ≈ 4 min on PC⇒ Straightforward calculation of the single gradient requires 8 daysUsually ≈ 200 iterations(gradients) is needed⇒ Total inversion time is ≈ 4 years.
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
3D data misfit
ϕd(σ) =1
2
NS∑i=1
NT∑j=1
βij tr[A
Tij (σ)Aij(σ)
](4)
σ = (σ1, ..., σN)T the vector of the electrical conductivities ofthe cells, N number of cells
NS number of MT sites ri = (xi , yi , z = 0)
NT number of frequencies ωj
Aij = Zij −Dij 2× 2 matrices
Zij complex-valued predicted Z(ri , ωj) impedance
Dij complex-valued observed D(ri , ωj) impedance
βij some positive weights
tr[A
TA]
= A11A11 + A12A12 + A21A21 + A22A22
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Gradients: Adjoint approach
∂ϕd
∂σk= Re
NT∑j=1
2∑p=1
∫Vk
(u(p)x E(p)
x + u(p)y E(p)y + u(p)z E(p)
z
)dV
(5)
Maxwell’s equation
∇×∇× E(p)j −
√−1ωjµσ(r)E
(p)j =
√−1ωjµJ
(p)j (6)
Adjoint Maxwell’s equation
∇×∇× u(p)j −
√−1ωjµσ(r)u
(p)j =
√−1ωjµ
(j(p)j +∇× h
(p)j
)(7)
j(p)j and h
(p)j - horizontal electric and magnetic dipoles at the MT sites
p = 1, 2 - polarization
The forward modelling is performed with X3D by (Avdeev et al.,
2002).
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Grid and MT sites
N= 20× 20× 9 cellsdx = dy = 4 kmNS= 80 MT sitesNT=3 periods:100, 300, 1000 snoise - 1%initial guess model:50 Ωm halfspace
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Logarithmic parametrization vs conductivities:inversion results
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Logarithmic parametrization vs conductivities:convergence curves
0 100 200 300 400 500 600
−2
−1
0
1
2
3
Mis
fit
nfg
−2−10123456789
log 10
(λ)
without additional regularization
m= σ m=log
10 σ
Target misfit
0 100 200 300 400 500 600
−2
−1
0
1
2
3
Mis
fit
nfg
−2−10123456789
log 10
(λ)
with additional regularization
m= σ m=log
10 σ
Target misfit
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Regularization
Tikhonov-type stabilizers:
Laplace
ϕs = dxdy∑αβγ
[∂2m
∂x2+∂2m
∂y2+∂2m
∂z2
]2αβγ
dzγ (8)
Gradient
ϕs = dxdy∑αβγ
[(∂m
∂x
)2
+
(∂m
∂y
)2
+
(∂m
∂z
)2]αβγ
dzγ (9)
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Gradient vs Laplace: inversion results
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Gradient vs Laplace: inversion results
Laplace
Gradient
True model
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Gradient vs Laplace: convergence curves
0 100 200 300 400 50010
−2
10−1
100
101
102
103
104
Mis
fit
nfg
0 100 200 300 400 500
−8
−7
−6
−5
−4
−3
−2
−1
0
1
2
3
4
log 10
(λ)
Laplace regul.Gradient regul.Target misfit
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Outline
1 Essential Parts of 3D MT Inversion Code X3DIOptimization MethodCalculation of the GradientsParametrizationRegularization
2 Salt Dome Overhang Detectability Study with x3Di
3 Conclusions
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
3D Model of a salt wall
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
1995 MT sites
N= 129× 69× 13 cellsdx = dy = 0.25 kmNS=1995 MT sitesNT=5 frequencies:10−3 - 101 Hznoise - 5%initial guess model:11 Ωm halfspace
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Inversion result for model 2 with overhang.1995 MT sites. Horizontal slices
0.002−0.05 km
y (
km
)
−30
−20
−10 y
(k
m)
x (km)−15−10 −5 0
−30
−20
−10
0.05−0.275 km
x (km)−15−10 −5 0
0.95−1.2 km
x (km)−15−10 −5 0
1.7−2.2 km
x (km)−15−10 −5 0
2.7−3.7 km
x (km)
Model 2: with overhang
−15−10 −5 0
Resistivity (Ωm)10
−110
010
110
2
z :
Inversion result
0.002−0.05 km 0.05−0.275 km 0.95−1.2 km 1.7−2.2 km 2.7−3.7 kmz :
−15−10 −5 0 −15−10 −5 0 −15−10 −5 0 −15−10 −5 0 −15−10 −5 0
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Inversion result: model 1 vs model 2.1995 MT sites. Vertical slices
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
105 MT sites along three profiles
N= 129× 69× 13 cellsdx = dy = 0.25 kmNS=105 MT sitesNT=5 frequencies:10−3 - 101 Hznoise - 5%initial guess model:11 Ωm halfspace
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Inversion result for model 2 with overhang.105 MT sites. Horizontal slices
y (
km
)
−30
−20
−10 y
(k
m)
x (km)
−30
−20
−10
x (km) x (km) x (km)
C
B
A
x (km)
C
B
A
Resistivity (Ωm)10
−110
010
110
2
0.002−0.05 km
−15−10 −5 0
0.05−0.275 km
−15−10 −5 0
0.95−1.2 km
−15−10 −5 0
1.7−2.2 km
−15−10 −5 0
2.7−3.7 km
Model 2: with overhang
−15−10 −5 0
z :
Inversion result
0.002−0.05 km 0.05−0.275 km 0.95−1.2 km 1.7−2.2 km 2.7−3.7 kmz :
−15−10 −5 0 −15−10 −5 0 −15−10 −5 0 −15−10 −5 0 −15−10 −5 0
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Inversion result: model 1 vs model 2.105 MT sites. Vertical slices
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Outline
1 Essential Parts of 3D MT Inversion Code X3DIOptimization MethodCalculation of the GradientsParametrizationRegularization
2 Salt Dome Overhang Detectability Study with x3Di
3 Conclusions
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Conclusions
Logarithmic parametrization is beneficial in terms ofinversion result and computational time
Regularization based on Gradient suppresses spatialresistivity gradients
The x3Di code produces encouraging results for salt domeoverhang detectability
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Acknowledgements
We would like to thank
Wintershall Holding AG, who funded the inversion studyand 20 ocean bottom MT instruments
Introductionto 3D MT
inversion codex3Di
A. Avdeeva,D. Avdeev,M. Jegen
X3DI
OptimizationMethod
Calculation ofthe Gradients
Parametrization
Regularization
Salt OverhangDetectability
Conclusions
References
Thanks for your attention
Avdeev, D. B. and Avdeeva, A. D. (2009). Three-dimensional magnetotelluricinversion using a limited-memory QN optimization. Geophysics, 74(3),F45–F57.
Avdeev, D. B., Kuvshinov, A. V., Pankratov, O. V., and Newman, G. A. (2002).Three-dimensional induction logging problems, Part I: An integral equationsolution and model comparisons. Geophysics, 67, 413–426.
Avdeeva, A. D. and Avdeev, D. B. (2006). A limited-memory quasi-newtoninversion for 1D magnetotellurics. Geophysics, 71, G191–G196.
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