Nowcasting is a term originating from economics, finance and
meteorology. It refers to the process of determining the uncertain state
of the economy, markets or the weather at the current time by indirect
means. In this paper we describe a simple 2-parameter data analysis that
reveals hidden order in otherwise seemingly chaotic earthquake
seismicity. One of these parameters relates to a mechanism of seismic
quiescence arising from the physics of strain-hardening of the crust
prior to major events. We observe an earthquake cycle associated with
major earthquakes in California, similar to what has long been
postulated. An estimate of the earthquake hazard revealed by this state
variable timeseries can be can be optimized by the use of machine
learning in the form of the Receiver Operating Characteristic skill
score. The ROC skill is used here as a loss function in a supervised
learning mode. Our analysis is conducted in the region of
5o x 5o in latitude-longitude
centered on Los Angeles, a region which we used in previous papers to
build similar timeseries using more involved methods (Rundle and
Donnellan, 2020; Rundle et al., 2021). Here we show that not only does
the state variable timeseries have forecast skill, the associated
spatial probability densities have skill as well. In addition, use of
the standard ROC and Precision (PPV) metrics allow probabilities of
current earthquake hazard to be defined in a simple, straightforward and
rigorous way.