Nonlinear Noise Reduction for the Airborne Transient Electromagnetic Method based on Kernel Minimum Noise Fraction

Abstract

The airborne transient electromagnetic method has become a powerful tool to explore deep resource and tectonic structures. However, aircraft vibrations and flight environments produce very strong and complex nonlinear noise and result in poor data quality compared to ground transient electromagnetic methods. Consequently, the reduction of airborne electromagnetic noises is of vital importance to data inversion and imaging. To suppress and remove the nonlinear noise, we propose using kernel minimum noise fraction (KMNF), which is a nonlinear generalized method of minimum noise fraction. First, an adaptive variable window-width filtering algorithm is used to evaluate the noises and perform the preliminary denoising. Then, we adopt the two filter methods, which are minimum noise fraction (MNF) and KMNF to suppress the noise. The results show that these two methods can both suppress noise and make the decay curves smooth, but kernel MNF is more effective for the nonlinear characteristics of noise and it does not weaken the anomaly. Finally, field data from the Qinling mine area is processed, using the MNF and KMNF methods. The results show that nonlinear noise is suppressed by both methods but the results of KMNF are better than those of the linear MNF method.