This short article explores and extends the adaptive detection algorithm recently developed by Arrowsmith, Whitaker, et al. (2008). In particular, this article highlights its application for seismic data, compares results for colocated seismic and infrasonic data, and assesses detector performance through comparison with analyst picks. We assess the adaptive detector by generating receiver-operating characteristic (ROC) curves, illustrating the trade-off between detection probability and false-alarm probability, and comparing the results with the conventional F-detector. The results show that the adaptive detector performs much better than the conventional detector for both seismic and infrasound data by maintaining high detection probabilities while significantly decreasing false-alarm probabilities, illustrating that correlated noise is ubiquitous for both types of data. The effect of the adaptation window is illustrated and shown to be especially important for infrasound data where diurnal variations in ambient noise levels are pronounced. A window choice of 1 hr (i.e., significantly less than 24 hr) is shown to be adequate for representing variations in ambient noise levels.