2007
DOI: 10.1111/j.1365-246x.2007.03365.x
|View full text |Cite
|
Sign up to set email alerts
|

Inversion of time domain three-dimensional electromagnetic data

Abstract: S U M M A R YWe present a general formulation for inverting time domain electromagnetic data to recover a 3-D distribution of electrical conductivity. The forward problem is solved using finite volume methods in the spatial domain and an implicit method (Backward Euler) in the time domain. A modified Gauss-Newton strategy is employed to solve the inverse problem. The modifications include the use of a quasi-Newton method to generate a pre-conditioner for the perturbed system, and implementing an iterative Tikh… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
41
0

Year Published

2008
2008
2023
2023

Publication Types

Select...
7
2

Relationship

1
8

Authors

Journals

citations
Cited by 106 publications
(41 citation statements)
references
References 42 publications
(67 reference statements)
0
41
0
Order By: Relevance
“…To improve its efficiency, some modifications are necessary such as approximating only the Hessian related to the data misfit instead of the full Hessian (Haber 2005) or introducing an additional regularization (Avdeev and Avdeeva 2009). Often the approximate inverse Hessian of the QN method is used as a pre-conditioner in the CG solver in the GN method (Haber et al 2007) or in the non-linear conjugate gradient (NLCG) method described in the previous section (Newman and Boggs 2004). Because of its sophistication, I do not include the basic QN algorithm in this paper.…”
Section: The Quasi-newton (Qn) Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…To improve its efficiency, some modifications are necessary such as approximating only the Hessian related to the data misfit instead of the full Hessian (Haber 2005) or introducing an additional regularization (Avdeev and Avdeeva 2009). Often the approximate inverse Hessian of the QN method is used as a pre-conditioner in the CG solver in the GN method (Haber et al 2007) or in the non-linear conjugate gradient (NLCG) method described in the previous section (Newman and Boggs 2004). Because of its sophistication, I do not include the basic QN algorithm in this paper.…”
Section: The Quasi-newton (Qn) Methodsmentioning
confidence: 99%
“…If CG is applied to (3) or (6), the algorithm is referred to as the model space conjugate gradient method. This algorithm is used in Mackie and Madden (1993), Rodi and Mackie (2001), Haber et al (2004Haber et al ( , 2007, and Lin et al (2008). If CG is used to solve (5), the algorithm is the data space conjugate gradient method.…”
Section: The Gauss-newton With Conjugate Gradient (Gn-cg) Approachmentioning
confidence: 99%
“…Assuming that an appropriate finite volume or finite element mimicking discretization scheme has been applied in space [10,12,14,18] we can rewrite the resulting system as a large scale ODE in time u t = A(m)u (2) u(0) = u 0 1 where A(m) is a symmetric matrix which results from the discretization of the operator ∇× σ −1 ∇× , u is a discretization of the magnetic field and m = log(σ) is a discretization of a the log conductivity. Here, commonly to many other inverse conductivity formulations, we use the log conductivity because the conductivity may change over a few orders of magnitudes [22].…”
Section: Introductionmentioning
confidence: 99%
“…Examples of local 2 International Journal of Optics optimization methods include gradient methods and quasiNewton and Gauss-Newton techniques [17,18]. These methods are fast but often converge to local minima due to the nonlinear nature of the problem.…”
Section: Introductionmentioning
confidence: 99%