1. Key Laboratory of Exploration Technologies for Oil and Gas Resources in Ministry of Education,Yangtze university,Wuhan 430100,China 2. Hubei Cooperation Innovation Center of Unconventional Oil and Gas,Wuhan 430100,China 3. School of Information and Mathematics,Yangtze university,Jingzhou 434023,China
In the process of measuring electric field and magnetic field components,due to interference from geological factors,power frequency,human activities,and other external factors as well as the observation system,there often exist many kinds of noises in actual data,which affect the computing of apparent resistivity and phase and the subsequent forward and inversion work.In order to improve the processing effect,the authors applied the TI wavelet denoising method based on improved threshold to the denoising of magnetotelluric signals on the basis of the existing fast algorithm.By using the denoising method,not only the noise can be removed effectively,but also the original shape of signal can be preserved at a large extent.After dealing with the measured magnetotelluric signal data by this method,the authors detected that the denoising method can make the mutation phenomenon under effective control and decrease,and furthermore it improves significantly the quality of time series,apparent resistivity curve and phase curve,thus it has a good denoising effect for magnetotelluric signal.
Coifman R R, Donoho D L . Translation-invariant de-noising[J]. Neuroimage, 1995,103(2):125-150.
Lang M, Guo H, Odegard J E , et al. Noise reduction using an undecimated discrete wavelet transform[J]. IEEE Signal Processing Letters, 1996,3(1):10-12.
Nason G P, Silverman B W . The stationary wavelet transform and some statistical applications[M]. New York:Wavelets and Statistics, 1995: 918-926.
Walden A T, Cristan A C . Matching pursuit by undecimated discrete wavelet transform for non-stationary time series of arbitrary length[J]. Statistics and Computing, 1998,8(3):205-219.
Berkner K, Wells Jr R O . Smoothness estimates for soft-threshold de-noising via translation-invariant wavelet transforms[J]. Applied & Computational Harmonic Analysis, 2002,12(1):1-24.