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Abstract
Residual noise remains in airborne time domain electromagnetic profiles after preprocessing the data, and this noise affects the exploration of targets. An approach to reduce this noise based on the minimum noise fraction has been proposed. The minimum noise fraction uses a rotation matrix to transform noise-contaminated electromagnetic data into the minimum noise fraction components ordered by signal-to-noise ratio. The rotation matrix is formed based on the use of noise covariance estimation and the data covariance. Noise can be effectively removed when we reconstruct the electromagnetic data using the low-order minimum noise fraction components whose signal-to-noise ratios are sufficiently high. In this work, we discuss the de-noising process based on the minimum noise fraction for two earth models and field data from Ontario Airborne Geophysical Surveys over the Nestor Falls area, Canada. Example applications to synthetic and field data are used to demonstrate the excellent performance of the proposed method.
Residual noise remains in airborne time domain electromagnetic profiles after preprocessing the data, and this noise affects the exploration of targets. An approach to reduce this noise based on the minimum noise fraction has been proposed in this paper.
Acknowledgements
The would like to thank Jean M. Legault, Chief Geophysicist of Geotech Ltd, for kindly providing us with access to Geophysical Data Set 1076 for airborne electromagnetic field data. This work is financially supported by the National Natural Science Foundation of China (41274076, 41674108), and Projects on the Development of the Key Equipment of Chinese Academy of Sciences (ZDYZ2012–1-03, ZDYZ2012–05–04), and National High Technology Research and Development Program of China (2013AA063904). We want to thank all reviewers and editors for their significant comments and suggestions that helped to clarify this paper.