Improvement of the Focusing Inversion of Gravity Data with Hybrid Conjugate Gradient Parameter Method

Potential field methods, such as the gravity technique, have become essential tools for exploration. Inversion of gravity data is the most important stage in the interpretation of the data. Inversion is a mathematical technique that constructs a geophysical subsurface model automatically from measured data by adding some prior knowledge. Inversion of gravity data is time-consuming and needs a long time because of numerous data and model parameters. Thus, a fast and effective inversion method is necessary to improve the speed of the inversion process. Many algorithms are available for focusing inversion of gravity data, such as the reweighted regularized conjugate gradient (RRCG) method. This method is iterative, and it takes a long time to converge to a solution. In this algorithm, there is a conjugate gradient parameter that is effective in inversion. In this paper, we used a hybrid conjugate gradient parameter method for focusing inversion of gravity data and compared the results with the conventional Fletcher-Reeves (FR) conjugate gradient parameter method. We applied this method for the data from a synthetic model and Shoaz iron ore deposit in Yazd, Iran. The inversion result indicated that the hybrid conjugate gradient parameter method converges to the solution faster than the FR method, while the results from both approaches have remarkable correlations with the true geological structures.