Optimal, scalable forward models for computing gravity anomalies

TL;DR

This paper introduces three scalable methods for computing gravity anomalies from density variations, emphasizing optimal linear scaling and parallel efficiency for large-scale problems.

Contribution

It presents novel PDE-based approaches, including Finite Element and Green's function methods, that are optimal and highly scalable for gravity modeling.

Findings

All methods scale linearly with problem size.

Methods demonstrate excellent parallel scalability up to 10^8 voxels.

Comparative analysis shows trade-offs in accuracy and computational efficiency.

Abstract

We describe three approaches for computing a gravity signal from a density anomaly. The first approach consists of the classical "summation" technique, whilst the remaining two methods solve the Poisson problem for the gravitational potential using either a Finite Element (FE) discretization employing a multilevel preconditioner, or a Green's function evaluated with the Fast Multipole Method (FMM). The methods utilizing the PDE formulation described here differ from previously published approaches used in gravity modeling in that they are optimal, implying that both the memory and computational time required scale linearly with respect to the number of unknowns in the potential field. Additionally, all of the implementations presented here are developed such that the computations can be performed in a massively parallel, distributed memory computing environment. Through numerical experiments, we compare the methods on the basis of their discretization error, CPU time and parallel scalability. We demonstrate the parallel scalability of all these techniques by running forward models with up to $10^8$ voxels on 1000's of cores.