Process And Study Of Noise Elimination Method In Magnetotelluric Sounding

At present, the magnetotelluric sounding techniques have been widely used inmany fields, especially in the aspects of mineral exploration. light, magnetotelluricsounding technology has lightweight, high efficiency, wide range of exploration, noaffected by shielding from resistive formation characteristics. The collection ofinformation in the study area is large, the exploration depth is deeper, and thereexists high resistance stratum in the deep underground,so we select EH-4highfrequency magnetotelluric sounding equipment, equipment can effectively completethe collection task, but the same as other magnetotelluric sounding equipment,which is vulnerable to external noise interference, so the effective noise eliminationmethod needs to be introduced in the information collection and data processingaspect.The article used this as a starting point, through the study of magnetotelluricsounding theory principle and noise characteristics, carrying on the analysis fromtwo aspects of field information collection and data processing. In the aspect ofinformation collection, analyzing the noise impact which field operation factorsmake, clarifying the necessity of field operation and the actual operationrequirements; in the aspect of data processing, for the shortage of inversion softwareImagem attaches to EH-4high-frequency magnetotelluric soundingequipment,through the understanding of data storage format of EH-4equipment,reading and reconstructing the data of time series Y files by Matlab software, usingwavelet transform method to eliminate the noise;In the aspect of practicalapplications,adopting the curvilinear translation method for noise suppression,andobtained good suppression effect in eliminating static effect, in addition, the terraincorrection method is brought for the2D inversion, to eliminate the false informationin the process of the surface inversion.Summarizing a set of effective noise reduction method,with the above method,so as to improve the data accuracy and reliability.