We use modern seismological data processing tools to revisit the Apollo lunar seismic data set with the goal of extending and further characterizing the existing catalog of deep moonquakes. Our studies focus on the long-period data and include filtering and despiking noisy data, event classification, cluster identification, and robust methods for amplitude estimation. We perform cross-correlation analyses for known groups of deep events, confirming earlier visual classifications. By combining the cross-correlation approach with a robust median despiking algorithm, we produce improved differential times and amplitudes, enabling us to construct cleaner stacks. Each event group, represented by a single waveform stack of its constituent members, is cross correlated with the continuous time series. We focus on the A1 cluster because it has more cataloged events than any other cluster and is generally well characterized. Using this approach, we identify additional events that can be associated with previously defined deep clusters. For the deep event group A1 we have found 123 new events, which show phase behavior similar to the 323 previously cataloged events. Our new event search allows us to create optimized event stacks with improved signal to noise from which revised travel time picks (and thus location estimates) can be made. Application of our methods to other deep clusters should form a more complete event catalog and improve our understanding of the spatial and temporal distribution of deep lunar events.