Seismic data random noise reduction using a method based on improved complementary ensemble EMD and adaptive interval threshold
Liu Jicheng, Ya Gu, Yongxin Chou, Jianfei Gu
Abstract
Random noise attenuation is an important step in seismic signal processing. This paper develops a seismic denoising method which combines the improved complementary ensemble empirical mode decomposition (ICEEMD) and adaptive interval threshold. The seismic data are decomposed into intrinsic mode functions (IMFs) by ICEEMD, which can overcome the problem of uncertain number of modes when adding different random noise as well as the problems of spurious modes and the residual noise from using the ensemble empirical mode decomposition (EEMD) and the complementary ensemble empirical mode decomposition (CEEMD). After the decomposition, the noise in IMFs is filtered out by the adaptive interval threshold. The de-noised data are reconstructed by stacking the filtered IMFs. The proposed approach is validated via the synthetic and field data. The results demonstrate that the approach can effectively improve the de-noising performance.