Abstract
Magnetotelluric (MT) surveying is an Electromagnetic (EM) surveying technique used in geophysics and mineral exploration. The main problem in MT surveying is the estimation of the impedance of the ground, which is obtained as the ratio between the natural environmental electric and magnetic fields measured on the surface of the target area. Because these measurements are inherently corrupted by noise, the impedance estimate may be biased (Errors-In-Variables (EIV)), which is a difficulty well-known in MT literature and typically overcome by the use of additional independent measurements. This paper formulates the MT problem as a standard system identification problem, and uses output-error model structures to obtain scalar (SISO) and vector (TISO) ground impedance estimates. Estimation bias is minimised by selecting segments of data with high Signal-to-Noise Ratio (SNR). The SISO and TISO modelling approaches are discussed and compared on results obtained from experimental MT data.
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