  With dedicated exoplanet surveys underway for multiple extreme precision
radial velocity (EPRV) instruments, the near-future prospects of RV exoplanet
science are promising. These surveys' generous time allocations are expected to
facilitate the discovery of Earth analogs around bright, nearby Sun-like stars.
But survey success will depend critically on the choice of observing strategy,
which will determine the survey's ability to mitigate known sources of noise
and extract low-amplitude exoplanet signals. Here, we present an analysis of
the Fisher information content of simulated EPRV surveys, accounting for the
most recent advances in our understanding of stellar variability on both short
and long timescales (i.e., oscillations and granulation within individual
nights, and activity-induced variations across multiple nights). In this
analysis, we capture the correlated nature of stellar variability by
parameterizing these signals with Gaussian Process kernels. We describe the
underlying simulation framework as well as the physical interpretation of the
Fisher information content, and we evaluate the efficacy of EPRV survey
strategies that have been presented in the literature. We explore and compare
strategies for scheduling observations over various timescales and we make
recommendations to optimize survey performance for the detection of Earth-like
exoplanets.
