Statistical discrimination of foreshocks from other earthquake clusters

When earthquake activity begins, it may be a foreshock sequence to a larger earthquake, a swarm, or a simple main-shock-aftershock sequence. This paper is concerned with the conditional probability that it will be foreshock activity of a later larger earthquake, depending on the occurrence pattern of some early events in the sequence. The earthquake catalogue of the Japan Meteorological Agency (1926-1993, M(J) greater than or equal to 4) is decomposed into a large number of clusters in time and space in order to compare statistical features of foreshocks with those of swarms and aftershocks. Using such a data set, Ogata, Utsu & Katsura (1995) revealed some discriminating features of foreshocks relative to the other types of clusters, for example the events' closer proximity in time and space, and a tendency towards chronologically increasing magnitudes, which encouraged us to construct models which forecast the probability of the earthquakes being foreshocks. Specifically, the probability is a function of the history of magnitude differences, spans between origin times and distances between epicentres within a cluster. For purposes of illustration, the models were fitted to the early part of the data (1926-1975) and the validity of the forecasting procedure was checked on data from the later period (1976-1993). Two procedures for evaluating the performance of the probability forecast are suggested. Furthermore, for the case where only a single event is available (i.e. either it is the first event in a cluster or an isolated event), we also forecast the probability of the event being a foreshock as a function of its geographic location. Then, the validity of the forecast is demonstrated in a similar manner. Finally, making use of the multi-element prediction formula, we show that the forecasting performance is enhanced by the joint use of the information in the location of the first event, and that in the subsequent interevent history in the cluster.