基于CEEMD和自适应中值滤波的大地电磁数据去噪研究

为提高大地电磁数据的信噪比,笔者提出基于互补总体经验模式分解(CEEMD)和自适应中值滤波的去噪方法,利用CEEMD将大地电磁时间序列数据分解成多个固有模态函数(IMF)及趋势项,依据噪声的高低频特征有选择地利用自适应中值滤波对固有模态函数(IMF)进行去噪,再进行数据重构。对实测数据进行处理,该方法能较好地抑制大地电磁数据中、低频部分的噪声干扰,抑制突变点,提高数据的信噪比。

Research on magnetotelluric signal denoising based on CEEMD and self-adaptive median filtering

In order to improve the signal-to-noise ratio of the magnetotelluric data, the authors put forward a new denoising method for removing the noises in magnetotelluric data based on the theory of complementary ensemble empirical mode decomposition(CEEMD) and self-adaptive median filtering. By CEEMD decomposing magnetotelluric time-series into different intrinsic mode functions (IMFs) and trend items, and according to the frequency of noise,the authors selectively used self-adaptive median filter to denoise each IMF component to extract useful data from IMFs,then reconstructed the data for signal-noise separation. This method was applied to the measured data, and it is indicated that the method can suppress medium and low frequency noises in magnetotelluric data and inhibit mutation, which improve the signal-to-noise ratio effectively.