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Improved Estimation for Well-Logging Problems Based on Fusion of Four Types of Kalman Filters.

Authors :
Soltani, Sina
Kordestani, Mojtaba
Aghaee, Paknoosh Karim
Member, Graduate Student
Saif, Mehrdad
Member, Senior
Source :
IEEE Transactions on Geoscience & Remote Sensing; Feb2018, Vol. 56 Issue 2, p647-654, 8p
Publication Year :
2018

Abstract

The concept of information fusion has gained a widespread interest in many fields due to its complementary properties. It makes systems more robust against uncertainty. This paper presents a new approach for the well-logging estimation problem by using a fusion methodology. The natural gammaray tool (NGT) is considered as an important instrument in the well logging. The NGT detects changes in natural radioactivity emerging from the variations in concentrations of micronutrients as uranium (U), thorium (Th), and potassium (K). The main goal of this paper is to have precise estimation of the concentrations of U, Th, and K. Four types of Kalman filters are designed to estimate the elements using the NGT sensor. Then, a fusion of the Kalman filters is utilized into an integrated framework by an ordered weighted averaging (OWA) operator to enhance the quality of the estimations. A real covariance of the output error based on the innovation matrix is utilized to design weighting factors for the OWA operator. The simulation studies indicate not only a reliable performance of the proposed method compared with the individual Kalman filters but also a better response in contrast with previous fusion methodologies. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01962892
Volume :
56
Issue :
2
Database :
Complementary Index
Journal :
IEEE Transactions on Geoscience & Remote Sensing
Publication Type :
Academic Journal
Accession number :
128707782
Full Text :
https://doi.org/10.1109/TGRS.2017.2752460