A combined wavelet transform algorithm used for de-noising magnetotellurics data in the strong human noise

作者：Ling Zhen-Bao; Wang Pei-Yuan; Wan Yun-Xia^*; Wang Yan-Zhang; Cheng De-Fu; Li Tong-Lin

来源：Acta Geophysica Sinica, 2016, 59(9): 3436-3447.

DOI：10.6038/cjg20160926

• Human disturbance
• Multi-resolution analysis
• Wavelet threshold
• Apparent resistivity
• Source effect

Magnetotellurics (MT) is normally applied for mineral exploration, because of its low cost and deeper penetration, as well as high resolving power in horizontally. However, the quality of MT data is affected by the human noise seriously around the mine area, which result in a unreliable result of inversion and even a wrong explanation. Therefore, it is important to remove the various disturbance. The multi-resolution algorithm (MRA) has domination in terms of making a high resolution for the frequency domain of MT data, and the wavelet thresholding algorithm (WTA) is at an advantage when removing the high frequency noise. A combined wavelet transform algorithm based on MRA and WTA is proposed. db3 wavelet is adopted in processing. Baseline drift and periodic noise of square wave can be removed by multi-resolution algorithm. Bayes estimation combined with improved threshold function which based on algorithm of wavelet threshold value is used to eliminate the impulse noise, triangular waveform as well as other forms of high frequency noises. Processing case indicated that the quality of the processed data, including the time series data and apparent resistivity curves, is improved significantly. Meanwhile near-source effect, which is caused by human interference, is suppressed effectively.

• 出版日期2016-9
• 单位吉林大学