Using Independent Component Analysis to Process Magnetotelluric Data

It is much more difficult to estimate magnetotelluric(MT) impedance tensor in the sites which are contaminated by high noise. In order to estimate a precise impedance tensor, we examine a new method called independent component analysis (ICA) that is developed to remove the noise in the recorded data. ICA is a time series analysis method, in which complicated data sets can be separated into all underlying sources without knowing these sources or the way that they are mixed. In this paper, we use the ICA method to process real MT data. All results show that apparent resistivity and phases which are preprocessed by ICA and derived from impedance tensors are generally more stable than only robust processing. These results reveal that ICA has the potential to handle noisy data.