3D density inversion of gravity gradiometry data with a multilevel hybrid parallel algorithm

Abstract

The density inversion of gravity gradiometry data has attracted considerable attention; however, in large datasets, the multiplicity and low depth resolution as well as efficiency are constrained by time and computer memory requirements. To solve these problems, we improve the reweighting focusing inversion and probability tomography inversion with joint multiple tensors and prior information constraints, and assess the inversion results, computing efficiency, and dataset size. A Message Passing Interface (MPI)-Open Multi-Processing (OpenMP)-Computed Unified Device Architecture (CUDA) multilevel hybrid parallel inversion, named Hybrinv for short, is proposed. Using model and real data from the Vinton Dome, we confirm that Hybrinv can be used to compute the density distribution. For data size of 100×100×20, the hybrid parallel algorithm is fast and based on the run time and scalability we infer that it can be used to process the large-scale data.