On November 4, 2013, residents near a quarry in the western suburbs of Chicago felt shaking from a rare, small earthquake. The USGS reported a magnitude of 3.2 and Dr. Robert Herrmann reported a dip-slip source mechanism from analyzing surface wave amplitudes recorded by USArray stations. With the goal of detecting potential aftershocks in this region of low seismicity and possibly gaining more insight into the source mechanism, a broadband seismic station was installed in the source region by researchers of Northwestern University. Due to the station’s suburban setting and proximity to various transportation arteries, industrial operations, and city infrastructure including a deep tunnel and reservoir, detecting and discriminating small earthquakes from urban noise events poses a serious challenge. Average daily noise levels can be 50 dB above typical noise levels for broadband seismometers in Illinois in pertinent frequency bands, so aftershock signals can be buried deep within the noise, rendering typical STA/LTA detection methods relatively ineffective. A preliminary analysis of several months of waveform data identified seismic signals from ~1000 events. None of these events occurred on a Sunday or at night, implying an anthropogenic origin and further illustrating the challenge. Recorded signals from these events span a wide range of waveforms, rendering popular detection methods like template matching less effective than in other settings. We aim to define and engineer a set of waveform features to aid with seismic event detection using data from a single broadband station in a noisy, urban environment. To identify useful spectral parameters, we first computed power spectral density (PSD) estimates using segments ranging from the hour-scale to the second-scale. Week-long spectrograms of the PSD estimates revealed characteristic frequencies that are likely associated with routine quarry operations. Select features were then tested for their ability to detect regional and local seismic events for one month of data. We will present the results of this analysis, including the performance of several features and discuss their respective benefits and limitations for seismic event detection in an urban environment.