  The solar acoustic oscillations are likely stochastically excited by
convective dynamics in the solar photosphere, though few direct observations of
individual source events have been made and their detailed characteristics are
still unknown. Wave source identification requires measurements that can
reliably discriminate the local wave signal from the background convective
motions and resonant modal power. This is quite challenging as these 'noise'
contributions have amplitudes several orders of magnitude greater than the
sources and the propagating wave fields they induce. In this paper, we employ a
high-temporal-frequency filter to identify sites of acoustic emission in a
radiative magnetohydrodynamic simulation. The properties of the filter were
determined from a convolutional neural network trained to identify the
two-dimensional acoustic Green's function response of the atmosphere, but once
defined, it can be directly applied to an image time series to extract the
signal of local wave excitation, bypassing the need for the original neural
network. Using the filter developed, we have uncovered previously unknown
properties of the acoustic emission process. In the simulation, acoustic events
are found to be clustered at mesogranular scales, with peak emission quite
deep, about 500 km below the photosphere, and sites of very strong emission can
result from the interaction of two supersonic downflows that merge at that
depth. We suggest that the method developed, when applied to high-resolution
high-cadence observations, such as those forthcoming with Daniel K. Inouye
Solar Telescope (DKIST), will have important applications in chromospheric
wave-studies and may lead to new investigations in high-resolution
local-helioseismology.
