  We develop and implement a new type of global earthquake forecast. Our
forecast is a perturbation on a smoothed seismicity (Relative Intensity)
spatial forecast combined with a temporal time-averaged (Poisson) forecast. A
variety of statistical and fault-system models have been discussed for use in
computing forecast probabilities. Our paper takes a new approach. The idea is
based on the observation that GR statistics characterize seismicity for all
space and time. Small magnitude event counts (quake counts) are used as markers
for the approach of large events. More specifically, if the GR b-value = 1,
then for every 1000 M>3 earthquakes, one expects 1 M>6 earthquake. So if ~1000
M>3 events have occurred in a spatial region since the last M>6 earthquake,
another M>6 earthquake should be expected soon. In physics, event count models
have been called natural time models, since counts of small events represent a
physical or natural time scale characterizing the system dynamics. In a
previous paper, we used conditional Weibull statistics to convert event counts
into a temporal probability for a given fixed region. In the present paper, we
dispense with a fixed region, and develop a method to compute these Natural
Time Weibull (NTW) forecasts on a global scale, using an internally consistent
method, in regions of arbitrary shape and size. Among the results we find that
the Japan region is at serious risk for a major (M>8) earthquake over the next
year or two, a result that also follows from considering completeness of the
Gutenberg-Richter relation.
