Using genetic algorithms to design satellite constellations for recovering daily Earth system mass change

Single pairs of satellites like the Gravity Recovery and Climate Experiment (GRACE) mission and GRACE-FO have provided two decades of near-continuous information on the Earth's time-varying gravity field. These missions are, through their design, inherently limited in their spatio-temporal coverage, spatially to a few hundred kilometers and temporally to roughly monthly resolution.
In order to increase the global spatio-temporal resolution and therefore allow for the determination of sub-monthly time-varying gravity field events, a constellation of GRACE-type pairs is a possible path forward. Small satellite instrumentation is becoming increasingly affordable, reliable and more precise. This will soon allow a constellation of GRACE-type small satellites to be deployed.
In this work we investigate the viability and limitations of a genetic algorithm-based optimization and its objective function in order to generate satellite constellations aimed at recovering daily Earth system mass changes. The developed approach is used to generate satellite constellations that are optimally designed for both daily as well as monthly recovery of Earth’s time-varying gravity field. By using a simplified analysis of the constellation’s performance, we are able to navigate through a very large search space in a relatively short period of time. This allows us to estimate the relative performance of constellations to each other and using Darwinian theory converge towards a set of optimal orbits. The performance of the designed constellations has then been validated using high-fidelity numerical simulations. We will summarize these results and discuss their implications for possible future constellations of small GRACE-like satellite pairs.
The resulting constellations have an inherent improved spatio-temporal performance which will reduce temporal aliasing errors and allow the characterization of daily mass-change effects. This improved spatio-temporal performance allows us to evaluate the improvement gained from such future mission architectures.