2.3 Geologic recurrence values
We summarize geologic recurrence values for each fault section in Table
1. Two primary types of recurrence data are depicted: recurrence
inferred from land-level changes, and recurrence assumed from tsunami
deposits. The sensitivity of both types of data to earthquake magnitude
is unknown, and various combinations of slip, magnitude, and location
likely influence land-level change and tsunami generation. Only the
largest (Mw ≥ 8.5) events may leave unambiguous records:
for example, the Mw 8.2 Chignik rupture generated a
negligible near- and far-field tsunami and small (< 0.08 m)
vertical displacements (Elliott et al., 2022; Ye et al., 2022), less
than the theoretical detection limit of 0.1- 0.2 m discussed by Shennan
et al. (2016). Until more is known about the sensitivity of land-level
change and tsunami recorders to earthquake rupture characteristics in
the AASZ, we assume that the geologic data records earthquakes ≥
Mw 8.5.
Uncertainties are not reported in a standardized way for the geologic
recurrence data we summarize here, so we use author-reported recurrence
intervals and uncertainties. Where not supplied by the authors, we
calculate the mean recurrence interval by dividing n-1
events into the total closed interval (oldest event to most recent
event) or n events into the total open interval (oldest
event to present day) and assign uncertainty equal to the standard
deviation of the mean recurrence value (Table 1). More complicated
calculations are possible (Field et al., 2013) but are not yet warranted
for the AASZ because of the relative lack of data, and the sometimes
disparate approaches and assumptions used for event identification and
subduction interface earthquake age estimates. We presume that
recurrence calculations are standardized within any particular hazard
model framework, and a logic tree approach will be used to propagate
uncertainties in recurrence and paleo-event size for classic
probabilistic seismic hazard analysis (National Research Council, 1997)
or that recurrence values with standardized uncertainty will be used as
a constraint in inversion-based PSHA (Field et al., 2020).