IMPROVING THE S/N RATIO OF MT DATA BY WAVELET ANALYSIS
He Lanfang, Wang Xuben, Wang Chengxiang (Chengdu University of Technology, China)
Magnetotelluric Sounding (MT) is one of the basic prospecting methods for oil and gas. Noise is a vital affect on its precision and a stumbling block to its development. It is necessary to hunt a good way to lower the noises. Wavelet analysis could decompose the composite signal which consists of several components of different frequencies into a series of signal blocks, so it is an effective method for dissociating the noise from signal. The authors detects that MT original data are time series. This time series often composes several spectra fltied by band pass filter, and sometimes composes regular frequency noises caused by a certain source. In order to filter this regular noise, the authors used wavelet analysis to process the added noise MT data. Judging by the comparison with the synthetic MT data, the result is perfect. Thus the authors consider that wavelet analysis is useful for improving the S/N ratio of MT data.