  Computing an ensemble of random fields using conditional simulation is an
ideal method for retrieving accurate estimates of a field conditioned on
available data and for quantifying the uncertainty of these realizations.
Methods for generating random realizations, however, are computationally
demanding, especially when the estimates are conditioned on numerous observed
data and for large domains. In this article, a \textit{new},
\textit{approximate} conditional simulation approach is applied that builds on
\textit{circulant embedding} (CE), a fast method for simulating stationary
Gaussian processes. The standard CE is restricted to simulating stationary
Gaussian processes (possibly anisotropic) on regularly spaced grids. In this
work we explore two possible algorithms, namely local Kriging and nearest
neighbor Kriging, that extend CE for irregularly spaced data points. We
establish the accuracy of these methods to be suitable for practical inference
and the speedup in computation allows for generating conditional fields close
to an interactive time frame. The methods are motivated by the U.S. Geological
Survey's software \textit{ShakeMap}, which provides near real-time maps of
shaking intensity after the occurrence of a significant earthquake. An example
for the 2019 event in Ridgecrest, California is used to illustrate our method.
