In the presence of background noise and interference, arrival times picked from a surface microseismic data set usually include a number of false picks which lead to uncertainty in location estimation. To eliminate false picks and improve the accuracy of location estimates, we develop a classification algorithm (RATEC) that clusters picked arrival times into event groups based on random sampling and fitting moveout curves that approximate hyperbolas. Arrival times far from the fitted hyperbolas are classified as false picks and removed from the data set prior to location estimation. Simulations of synthetic data for a 1-D linear array show that RATEC is robust under different noise conditions and generally applicable to various types of media. By generalizing the underlying moveout model, RATEC is extended to the case of a 2-D surface monitoring array. The effectiveness of event location for the 2-D case is demonstrated using a data set collected by a 5200-element dense 2-D array deployed for microearthquake monitoring.