Abstract:

In order to avoid the loss of effective signal or incomplete suppression of random noise because of single threshold selection in the curvelet threshold denoising method, a new curvelet threshold denoising method based on empirical mode decomposition (EMD) was proposed. Firstly, noise signal was decomposed into a series of intrinsic mode functions (IMF) by EMD. Then according to the distributing level of noise in each IMF, different thresholds were chosen to process the IMFs with noise. Finally, denoised signal was obtained by reconstructing the denoised IMFs and the IMFs without noise. Thanks to the introduction of EMD, different threshould can be chosen to apply to IMFs with various degree noise. In this way, the method  can overcome the shortcomings of single threshold selection of Curvelet threshold denoising method. Applying this method to synthetical and real field data sets indicate that it can improve the signaltonoise ratio, meanwhile, also greatly maintain the effective signal. The method is proved to be an effective and preservedamplitude denoising way.

Key words: empirical mode decomposition, curvelet threshold denoising, intrinsic mode function, random noise, signal to noise ratio, preserved-amplitude denoising