**PREDICTION OF LARGE EVENTS ON A DYNAMICAL MODEL OF
A FAULT**

PEPKE SL, CARLSON JM, **SHAW BE**
**JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH**

99: (B4) 6769-6788 APR 10 1994

**Abstract:**

We present results for long-term and intermediate-term
prediction algorithms applied to a simple mechanical model of a fault.
The long-term techniques we consider include the slip-predictable and time-predictable
methods and prediction based upon the distribution of repeat times between
large events. Neither the slip-predictable nor time-predictable method
works well on our model. In comparison, the time interval method is much
more effective and is used here to establish a benchmark for predictability.
We consider intermediate-term prediction techniques which employ pattern
recognition to identify seismic precursors. These methods are found to
be significantly more effective at predicting coming large events than
methods based on recurrence intervals. The performances of four specific
precursors are compared using a quality function Q, which is similar to
functions used in linear cost-benefit analysis. When the quality function
equally weights (1) the benefit of a successful prediction, (2) the cost
of maintaining alerts, and (3) the cost of false alarms, we find that Q
is optimized in algorithms based on the most conventional precursors when
alarms occupy 10-20% of the mean recurrence interval and approximately
90% of the events are successfully predicted. The measure Q is further
used to explore optimization questions such as variation in the space,
time, and magnitude windows used in the pattern recognition algorithms.
Finally, we study the intrinsic uncertainties associated with seismicity
catalogs of restricted lengths. In particular, we test the hypothesis that
many shorter catalogs are as effective as one long catalog in determining
algorithm parameters, and we find that the hypothesis is valid for the
model when the catalogs are of the order of the mean recurrence interval.