We thank Alan Douglas (Douglas, 2004) for his thoughtful comments on Myers and Schultz (2000). In Myers and Schultz (2000) we improve seismic location accuracy and uncertainty estimates for a sparse, regional network by improving travel-time predictions and better characterizing residual uncertainties. Station-specific, travel-time corrections and associated uncertainty estimates are calculated using the Bayesian kriging methodology of Schultz et al. (1998). The Bayesian kriging methodology is an empirical approach that exploits the spatial correlation of travel-time residuals to interpolate an empirical (calibration) set of travel-time residuals into a minimum-variance, geographically continuous surface of travel-time corrections and an associated uncertainty surface. In Myers and Schultz (2000) we test the Bayesian kriging approach by relocating events in the 1991 Racha, Georgia earthquake sequence (∼ 42°N, 43°E). We compare uncalibrated locations (based on P -wave travel times for the ak135 model; Kennett et al. , 1995) and calibrated locations (calibrated travel-times and uncertainties) to benchmark locations that are constrained with a dense, local-distance network. The calibration data set consists of event locations that are constrained by a teleseismic network. One goal of the study was to assess the utility of teleseismically constrained events for calibration. We find that better travel-time prediction for regional paths can be achieved by using the teleseismically constrained calibration events, and the improved travel-time prediction improves regional-network location accuracy. A major advantage of Bayesian kriging over many other interpolation techniques is the propagation of uncertainty from the calibration data set into a travel-time-prediction uncertainty surface. Sources of data set uncertainty include calibration-event locations, phase observations for calibration events, and calibration-event spacing. We find that coverage ellipses (Evernden, 1969) are representative of observed epicenter accuracy (i.e., confidence level of the coverage ellipse is indicative of the number of occurrences of the benchmark location within the ellipse) when the …