Abstarct: In order to determine the location of the anomaly resources for potential field, require the processing and accurate interpretation of the anomaly maps derived from the capture of potential field data. The interpretation of potential field anomalies is due to the inherent ambiguity in geophysical problems using different methods. One of these methods is data inverse modeling. The inverse modeling is used to determine the model parameters of the data. Hence, the inversion of gravity data is of great importance in the interpretation of exploratory data. Calculation of model parameters in the gravity method is used to estimate the density distribution in the subsurface model. Also, in the magnetic method, we calculate and estimate the magnetic susceptibility of subsurface masses. There are several methods baxsed on Computational equations in inversion of geophysical data. Hence, one of the important issues in inversion of geophysical data is the increase of speed and reduction of space used in computers to solve an inverse problem in research topics. Another important issue discussed in research topics is the choice of the regularization parameters in the process of solving an inverse problem. The purpose of this research is to provide a fast and accurate algorithm to improve the inverse modeling for potential field data. For this purpose, the necessary codes for solving the forward and inverse problems of potential field data using Lanczos bidiagnalization method using Generalized cross validation(GCV) method, and Active constraine balancing(ACB) method for choosing the regularization parameters Created in MATLAB programming environment. Finally, the proposed algorithm was applied to the data obtained from three-dimensional synthetic models and field data and its results were compared with other exploratory results. The results of this study showed that the active constraint balancing method for the estimation of the regularization parameter compared with the generalized cross-validation method for the smooth inversion of field data by Lanczos bidiagnaliztion method was better than the parameters Physical (density and magnetic susceptibility) model provides.