Residual separation of magnetic fields using a Cellular Neural Network approach

--In this paper, a Cellular Neural Network (CNN) has been applied to a magnetic regional/residual anomaly separation problem. CNN is an analog parallel computing paradigm defined in space and characterized by the locality of connections between processing neurons. The behavior of the CNN is defined...

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Published inPure and applied geophysics Vol. 158; no. 9-10; pp. 1797 - 1818
Main Authors ALBORA, A. M, ÖZMEN, A, UCAN, O. N
Format Journal Article
LanguageEnglish
Published Basel Springer 01.09.2001
Springer Nature B.V
Subjects
Algorithms
Cellular
Cellular communication
Chromite
Earth sciences
Earth, ocean, space
Exact sciences and technology
Geophysics
Geophysics: general, magnetic, electric and thermic methods and properties
Internal geophysics
Magnetic fields
Mathematical analysis
Metal geology
Metallic and non-metallic deposits
Neural networks
Separation
Middle East
Turkey
spectra
magnetic field
oxides
maps
regional anomalies
neural networks
residual anomalies
spinel
depth
Asia
chromite
magnetic susceptibility
magnetic anomalies
distortion
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Summary:--In this paper, a Cellular Neural Network (CNN) has been applied to a magnetic regional/residual anomaly separation problem. CNN is an analog parallel computing paradigm defined in space and characterized by the locality of connections between processing neurons. The behavior of the CNN is defined by the template matrices A, B and the template vector I. We have optimized weight coefficients of these templates using Recurrent Perceptron Learning Algorithm (RPLA). The advantages of CNN as a real-time stochastic method are that it introduces little distortion to the shape of the original image and that it is not effected significantly by factors such as the overlap of power spectra of residual fields. The proposed method is tested using synthetic examples and the average depth of the buried objects has been estimated by power spectrum analysis. Next the CNN approach is applied to magnetic data over the Golalan chromite mine in Elazig which lies East of Turkey. This area is among the largest and richest chromite masses of the world. We compared the performance of CNN to classical derivative approaches.[PUBLICATION ABSTRACT]
Bibliography:ObjectType-Article-2
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ISSN:0033-4553
1420-9136
DOI:10.1007/PL00001244