New approach of solving Euler deconvolution relation for the automatic interpretation of magnetic data

The conventional Euler deconvolution has 5 unknown parameters to be solve which are the location of source (x_0, y_0, and z_0), the background field and the structural index (SI). Among these 5 unknowns, the SI is to be manually selected by the interpreter. The manual input of SI into the Euler equa...

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Bibliographic Details
Published in:TAO : Terrestrial, atmospheric, and oceanic sciences atmospheric, and oceanic sciences, 2018-06, Vol.29 (3), p.243-259
Main Authors: Usman, Nuraddeen, Abdullah, Khiruddin, Nawawi, Mohd, Khalil, Amin Esmail
Format: Article
Language:English
Subjects:
Automation
Convolution
Data
Data interpretation
Deconvolution
Error analysis
Euler-Lagrange equation
Magnetic data
Regression analysis
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Summary:The conventional Euler deconvolution has 5 unknown parameters to be solve which are the location of source (x_0, y_0, and z_0), the background field and the structural index (SI). Among these 5 unknowns, the SI is to be manually selected by the interpreter. The manual input of SI into the Euler equation makes the technique to be semiautomated. A new technique based on Euler deconvolution that estimate background, horizontal coordinate (x_0 and y_0), depth and structural index (SI) of gridded magnetic data is presented. The theoretical and field model study over magnetic sources demonstrates the ability of the method to solve for the source location and nature of the target, the technique does not depend on magnetic latitude. An integrated automated filter which is based on convolution window, SI deviation, regression error and analytic signal is prepared and used for selecting valid solution. Finally, the technique is applied to real magnetic data of Sebarang Jaya, the clustered depth solutions coincided with the high amplitude/values of analytic signal and these are the possible positions of the target being sought. The technique is fast means of magnetic data interpretation and easy to implement as it involves first order derivatives of the field.
ISSN:1017-0839
2311-7680
DOI:10.3319/TAO.2017.10.05.01