Complex Curvelet Research Articles

Blended noise suppression using a hybrid median filter, normal moveout and complex curvelet transform approach

The high-density acquisition technique can improve subsurface imaging accuracy. However, it increases production cost rapidly and limits the wide application in practice. To solve this issue, the high productivity blending acquisition technology has emerged as a promising way to significantly increase the efficiency of seismic acquisition and reduce production cost. The great challenge of the blending acquisition technology lies in the severe interference noise of simultaneous sources. Therefore, the success of the blending acquisition technology relies heavily on the effectiveness of separating effective energy from the blended noise. We propose a blended noise suppression approach by using a hybrid median filter, normal moveout (NMO), and complex curvelet transform (CCT) approach. First, median filter is applied to original data after NMO correction. Second, the CCT-based thresholding denoising method is used to extract the remained effective energy from the data after median filtering to get the preliminary de-blended result. Next, the updated data are obtained by subtracting the pseudo-de-blended data of the de-blended result from the original data, and the process iterates. Last, the final de-blended result is obtained by adding the retrieved energy at each iteration until the signal-to-noise ratio satisfies the desired level. We demonstrate the effectiveness of the proposed approach on simulated synthetic and field data examples.

Signal enhancement based on complex curvelet transform and complementary ensemble empirical mode decomposition

Signal enhancement is a necessary step in seismic data processing. In this paper we utilize the complementary ensemble empirical mode decomposition (CEEMD) and complex curvelet transform (CCT) methods to separate signal from random noise further to improve the signal to noise (S/N) ratio. Firstly, the original data with noise is decomposed into a series of intrinsic mode function (IMF) profiles with the aid of CEEMD. Then the IMFs with noise are transformed into CCT domain. By choosing different thresholds which are based on the noise level difference of each IMF profile, the noise in original data can be suppressed. Finally, we illustrate the effectiveness of the approach by simulated and field datasets.