Extracting body-wave arrivals from ambient seismic recordings remains a challenging task, largely because ambient records are usually dominated by surface-wave energy. Most ambient seismic data-processing strategies aimed at enhancing body-wave energy focus on a cross-correlation plus stack methodology. While this approach is useful for interferometric investigations, it effectively squares the magnitude of unwanted coherent noise events (e.g. surface waves, burst-like or strong monochromatic energy) that commonly overpower ambient body-wave signal. Accordingly, coherent noise events are usually treated with a binary accept-or-reject decision of individual data windows based on root-mean-squared energy considerations. Applying a data-processing workflow to uncorrelated ambient seismic data represents an alternate strategy for mitigating coherent noise. However, this pre-stack methodology requires significant computational effort due to the often terabyte-sized data volumes. To make this approach feasible, we outline an automated processing workflow to automatically identify and mitigate coherent noise events that otherwise does not severely degrade the remaining waveforms. After each processing step, we apply a number of quality control measures to monitor the convergence rate of cross-correlation plus stack waveforms and for evidence of emerging body-wave reflection events. We apply the processing flow to an ambient seismic data set acquired on a large-N array at a mine site near Lalor Lake, Manitoba, Canada. Our quality control analyses suggest that automated preprocessing of uncorrelated ambient seismic recordings successfully mitigates unwanted impulsive and monochromatic coherent noise events. Accordingly, we assert that applying an automated data-processing approach would be beneficial for body-wave and other imaging and inversion analyses applied to ambient seismic recordings.