2D discrete wavelet transform for denoising magnetic data

Discrete wavelet transform (DWT) is a valuable tool in signal and imaging processing, in particular for denoising. Its performance in denoising potential-field data has been proven to be superior to that of traditional techniques. We analyze the most common thresholding techniques: soft and hard with cycle spinning, for denoising magnetic data. To certify the efficiency of denoising and improvement of the filtered data we use qualitative and quantitative analysis. Tests on noise-corrupted Bishop model prove that the soft thresholding changes the amplitude of the data while hard thresholding with cycle spinning generates better results. We show the quality of hard thresholding with cycle spinning applying it to real aeromagnetic anomaly over the Goiás Alkaline Province, Brazil, and quantifying the improvement of the denoised data.