  Shallow seismic sources excite Rayleigh wave ground motion with azimuthally
dependent radiation patterns. We place binary hypothesis tests on theoretical
models of such radiation patterns to screen cylindrically symmetric sources
(like explosions) from non-symmetric sources (like non-vertical dip-slip, or
non-VDS faults). These models for data include sources with several unknown
parameters, contaminated by Gaussian noise and embedded in a layered
half-space. The generalized maximum likelihood ratio tests that we derive from
these data models produce screening statistics and decision rules that depend
on measured, noisy ground motion at discrete sensor locations. We explicitly
quantify how the screening power of these statistics increase with the size of
any dip-slip and strike-slip components of the source, relative to noise
(faulting signal strength), and how they vary with network geometry. As
applications of our theory, we apply these tests to (1) find optimal sensor
locations that maximize the probability of screening non-circular radiation
patterns, and (2) invert for the largest non-VDS faulting signal that could be
mistakenly attributed to an explosion with damage, at a particular attribution
probability. Lastly, we quantify how certain errors that are sourced by opening
cracks increase screening rate errors. While such theoretical solutions are
ideal and require future validation, they remain important in underground
explosion monitoring scenarios because they provide fundamental physical limits
on the discrimination power of tests that screen explosive from non-VDS
faulting sources.
