NDLI: 3D multiplicative regularized Gauss-Newton inversion

Content Provider	IEEE Xplore Digital Library
Author	Abubakar, A. Jianguo Liu Habashy, T. Zaslavsky, M. Druskin, V. Guangdong Pan
Copyright Year	2009
Description	Author affiliation: Schlumberger-Doll Research, Cambridge, MA, USA (Abubakar, A.; Jianguo Liu; Habashy, T.; Zaslavsky, M.; Druskin, V.; Guangdong Pan)
Abstract	The marine controlled-source electromagnetic (CSEM) technology has the potential of providing useful information in applications such as off-shore oil exploration. With a horizontal electric dipole as a transmitter towed by a ship and multi-component electromagnetic receivers on the seafloor, this method has been applied in several field surveys. The high contrast in resistivity between saline-filled rocks and hydrocarbons, makes this method well-suited for detecting oil reservoirs (see [3]). A more rigorous approach to address this type of application is the full nonlinear inversion approach, for example see [1,2,5]. In such an approach the investigation domain is subdivided into pixels and through inversion, the location, the shape and the conductivity of the reservoir are reconstructed. We present a rigorous three-dimensional (3D) inversion algorithm. Unlike in [2,5], where the minimization approaches are based on non-linear conjugate gradient (CG) or quasi-Newton techniques, we employed a Gauss-Newton minimization approach as described in [4] using a multiplicative cost function [6]. The Gauss-Newton approach is well-known to have higher convergence rates than non-linear CG or quasi-Newton methods. On the other hand, the Gauss-Newton method can be more expensive because it requires the computation and inversion of a Hessian matrix. Furthermore by using the multiplicative cost function we do not need to determine the so-called regularization parameter in the optimization process; hence, the algorithm is fully automated. Further, the algorithm is equipped with two different regularization functions to produce either a smooth (using a standard L2-norm function) or a blocky (using a weighted L2-norm function) conductivity distribution; see [6]. Moreover, in order to enhance the robustness of the algorithm, we incorporated a non-linear transformation for constraining the minimum and maximum values of the conductivity distribution (see [4]). A line-search procedure for enforcing the error reduction in the cost function in the optimization process is also employed.
Starting Page	1
Ending Page	4
File Size	673218
Page Count	4
File Format	PDF
ISBN	9781424436477
ISSN	15223965
DOI	10.1109/APS.2009.5171690
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Newton method Least squares methods Recursive estimation Conductivity Cost function Equations Petroleum Character generation Inverse problems H infinity control
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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