Regularizing method with a priori knowledge for seismic impedance inversion

This paper proposes a regularized restarted conjugate gradient method with a priori knowledge for solving ill-posed problems in impedance inversion. In Inner loop, we use a modified conjugate gradient algorithm and a restarted technique; in outer loop, the Morozov's discrepancy principle is used as a stopping rule. For choice of the regularization parameter, we use a geometric manner. The shortcoming of the traditional conjugate gradient method is that the iterations may be surplus or insufficient. This algorithm can overcome these two shortcomings by controlling the iterations within a reasonable range, so it converges quickly and accurately. A priori knowledge is obtained from the steepest descent method and a non-uniform constraint is considered to impose on the solution. Theoretic simulations are made and compared with the classical conjugate gradient method. Field data applications are performed. It reveals that the proposed algorithm has the advantages of high precision, robustness, fast calculation and practicability.