Fast Density Inversion Solution for Full Tensor Gravity Gradiometry Data

We modify the classical preconditioned conjugate gradient method for full tensor gravity gradiometry data. The resulting parallelized algorithm is implemented on a cluster to achieve rapid density inversions for various scenarios, overcoming the problems of computation time and memory requirements caused by too many iterations. The proposed approach is mainly based on parallel programming using the Message Passing Interface, supplemented by Open Multi-Processing. Our implementation is efficient and scalable, enabling its use with large-scale data. We consider two synthetic models and real survey data from Vinton Dome, US, and demonstrate that our solutions are reliable and feasible.