In this paper, two modeling method are employed. First, a method based on the Marquardt’s
algorithm is presented to invert the gravity anomaly due to a finite vertical cylinder source. The
inversion outputs are the depth to top and bottom, and radius parameters. Second, Forced Neural
Networks (FNN) for interpreting the gravity field as try to fit the computed gravity in accordance
with the estimated subsurface density distribution to the observed gravity. To evaluate the ability of
the methods, those are employed for analyzing the gravity anomalies from assumed models with
different initial parameters as the satisfactory results were achieved. We have also applied these
approaches for inverse modeling the gravity anomaly due to a Chromite deposit mass, situated east
of Sabzevar, Iran. The interpretation of the real gravity data using both methods yielded almost the
same results.
