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Magnetic field interpretation using singular value decomposition method based on correlation coefficient of eigenimages

Ata Eshaghzadeh, Roghayeh Alsadat Kalantari

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J. Bio. Env. Sci.9(1), 185-193, July 2016


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Magnetic investigations can yield important information about geological structures. Singular Value Decomposition (SVD) is a very powerful tool for analysis of the geophysical data set chiefly potential fields. In this paper, a new technique for demonstration of near-subsurface features with short wavelength using magnetic eigenvectors and eigenimage is proposed which separate the residual anomalies from magnetic map background (total magnetic field). Also, is exhibited a new method based on correlation coefficient between eigenimages for threshold determination. Using the SVD, a matrix of magnetic data set can be decomposed to a series of eigenimages. Finally, the SVD method eventuate two layers of singular value images that the layer reconstructed of threshold value to last eigenimages show local magnetic anomalies. The results obtained from the synthetic data set, with and without random noise, have been discussed. The method is demonstrated on real magnetic data set surveyed from Iran. The results show the good performance of the proposed method.


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Magnetic field interpretation using singular value decomposition method based on correlation coefficient of eigenimages

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