Application of Keilis-Borok and McNally prediction algorithms to earthquakes in the Lake Jocassee area, South Carolina

Abstract

Following a M L = 3.2 earthquake in November 1975 in the vicinity of Lake Jocassee in the northwestern part of South Carolina, microearthquake activity has been monitored on portable seismographs, with events as small as M L = −0.6 being well located. Using modified forms of prediction algorithms developed by Keilis-Borok et al. (1977) and McNally (1978a, b) systematic patterns in temporal and spatial distribution of the smaller (−0.6 < M L < 2.0) events were found to precede the larger felt events ( M L ⩾ 2.0). Using the Keilis-Borok algorithm, “swarms” were recognized before fiv earthquakes were preceded by large clusters (>three events per cluster), and one was preceded by several smaller clusters (two or three events per cluster). The series of events were also tested for a Poisson random process. For the earlier, more active time frame, November 1975–June 1976 (mean rate of occurrence between locatable events ∼20 hours), the Poisson hypothesis was rejected at a highly significant level. However, for the less active time period, October 1976–December 1977 (∼40 hours for mean rate), the Poisson process may be a reasonable approximation to the earthquake occurrences. In comparing the two prediction algorithms, the modified form of the Keilis-Borok algorithm was found to be more successful in the recognition of precursory activity with a smaller number of false alarms (three). When the data begin to approach a Poisson process, however, the algorithm is less accurate and physical constraints are relied on to decrease the number of false alarms. The McNally method was found to be less biased in application and easy to use for future examination of seismic activity. A major weakness, however, was the need for an additional constraint to differentiate between precursory activity before the larger events ( M L ⩾ 2.0) and smaller events (1.0 ⩽ M L < 2.0).