In principle, a goal of any regional seismic network (RSN) is simple - detect and review seismicity in the network’s region of interest. Vigorous aftershock sequences however present a unique challenge to an RSN’s mission in that they can overwhelm automated real-time and analyst-driven post-processing systems. This inundation with seismicity can the lead to earthquake catalogs that are incomplete and erroneous in the area around the aftershock sequence. In response to this challenge, this abstract explores integrating machine learning as a tool to expedite the University of Utah Seismogram Station’s (UUSS)’s response to the September, 2017 Soda Springs event in southeast Idaho. In the months following the event the UUSS was able to locate over 1000 aftershocks and was catalog complete to approximately 2.5 M_L. Owing to the fact that a temporary deployment of 6 six-channel and 2 strong-motion instruments were deployed to the area shortly after the event, it is anticipated that many more high-quality locations can be obtained without resorting to template matching. Therefore, it is our goal to attempt to recover the existing UUSS catalog with machine learning enabled workflows that can produce analyst-grade picks for location and subsequent magnitude estimation. Should this be successful then the anticipated consequence is that analysts can quickly screen and approve more events and by extension lower the network’s magnitude completeness during the aftershock sequence.