Charles Richter’s observation that ‘only fools and charlatans predict earthquakes,’ reflects the
fact that despite more than 100 years of effort, seismologists remain unable to do so with
reliable and accurate results. Meaningful prediction involves specifying the location, time, and
size of an earthquake before it occurs to greater precision than expected purely by chance from
the known statistics of earthquakes in an area. In this context, ‘forecasting’ implies a
prediction with a specification of a probability of the time, location, and magnitude. Two
general approaches have been used. In one, the rate of motion accumulating across faults and
the amount of slip in past earthquakes is used to infer where and when future earthquakes will
occur and the shaking that would be expected. Because the intervals between earthquakes are
highly variable, these long-term forecasts are accurate to no better than a hundred years. They
are thus valuable for earthquake hazard mitigation, given the long lives of structures, but have
clear limitations. The second approach is to identify potentially observable changes in the
Earth that precede earthquakes. Various precursors have been suggested, and may have been
real in certain cases, but none have yet proved to be a general feature preceding all earthquakes
or to stand out convincingly from the normal variability of the Earth’s behavior. However, new
types of data, models, and computational power may provide avenues for progress using
machine learning that were not previously available. At present, it is unclear whether
deterministic earthquake prediction is possible. The frustrations of this search have led to the
observation that (echoing Yogi Berra) ‘it is difficult to predict earthquakes, especially before