Error effects on magnetotelluric inversion
误差对大地电磁测深反演的影响

We investigate the effects of various error distributions on magnetotelluric (MT) inversion using numerical model simulations. We first generate the ideal noise-free synthetic data from forward modeling of some known models. Then the above ideal synthetic data sets are contaminated by the random noise satisfying Gamma, Gaussian and Uniform distributions with different means and standard deviations respectively. Finally, we make the MT inversion by applying the nonlinear conjugate gradients algorithm to the above synthetic data with various noises. The preliminary results showed that the error effects on MT inversion seem not significant for the simpler model considered in this study. However, such influence cannot be neglected for some complicated models, because the inversion results deviate more from the known complicated models with increasing error levels in the synthetic data. Our results also indicated that the inversion results are not sensitive to the error distributions as long as they have the same mean value and the same standard deviation.