Estimation of the efficiency of unbiased predictive risk estimator in the inversion of 2D magnetotelluric data

Abstract

Tikhonov Regularization is the most widely used method for geophysical inversion problems. The result of previous and current research has shown that how to estimate the regularization parameter has a dramatic effect on inversion results. In the present research, conventional methods, including L-curve, Discrepancy principle, GCV, and ACB are compared with an innovative technique called Unbiased Predictive Risk Estimator (UPRE) in the inversion of 2D magnetotelluric data. For this purpose, MT2DInvMatlab is applied as the main program. It uses the Levenberg–Marquardt method as the inversion core and the ACB method to estimate the regularization parameter. Then, this program was developed in a way that it could estimate the regularization parameter using all of the above-mentioned methods. Next, a relatively complex model consisting of two layers and three blocks was used as a synthetic model. Comparing the results of all methods in TM, TE, and joint modes showed that the UPRE method, which previously provided desirable results in the inversion of potential field data in terms of convergence rate, time, and accuracy of results, here along with the ACB method, presented more acceptable results in the same indicators. Therefore, these two methods were used in a geothermal case in the North-West of Iran as a real test. In this case, the UPRE presented results at the same level as the ACB method and better than it in terms of some indicators. So, the UPRE method, especially in large-scale problems, could be a suitable alternative to the ACB method.