10.21420/HYDZ-8W17
Iturrieta, P.
P.
Iturrieta
Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences
Gerstenberger, Matt C.
Matt C.
Gerstenberger
GNS Science
Rollins, Chris
Chris
Rollins
GNS Science
Van Dissen, Russ J.
Russ J.
Van Dissen
GNS Science
Wang, T.
T.
Wang
University of Otago
Schorlemmer, D.
D.
Schorlemmer
Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences
Accounting for earthquake rates’ temporal and spatial variability through least-information Uniform Rate Zone forecasts
GNS Science
2022
ScholarlyArticle
9781991013484
The distribution of earthquakes in time and space is seldom stationary. In low-seismicity regions, non-stationarity and data scarcity may preclude a significant statistical analysis. We investigate the performance of traditional stationary Poisson forecasts (such as smoothed-seismicity models [SSM]), with applications in Probabilistic Seismic Hazard Assessment, in terms of the available training data. We design bootstrap experiments that use multiple pairs of consecutive training-forecast windows of a catalogue to: (i) analyse the lowest available training data in which SSM performs spatially better than the least-informative Uniform Rate Zone (URZ) and (ii) characterise the rate temporal variability in multiple training-forecast windows in terms of its over-dispersion and non-stationarity. The experiments rely on the assumption of fast-forward catalogues, i.e. the variability in catalogues from high-seismicity regions can be used as a proxy of long-term low-seismicity region catalogues. Formally, the strong stationarity assumption is relaxed into local and incremental stationarity, and self-similarity is described by a power law. Results show a rate variability up to 10 times higher, as predicted by Poisson models, and evidence the impact of non-stationarity in assuming a constant mean rate in training-forecast intervals. The description of rate variability is translated into a reduction of spatial precision, whose impact on seismic hazard is evaluated. First, counting processes (e.g. negative binomial) are devised to capture rate distributions, considering the rate as a random variable, which is now conditioned to the available information in a training period. Second, under the assumption that strain/stress rate is related to the timescale of earthquake interactions, we devise a data-driven method based on strain rate maps to delimit URZs. A rate distribution is inferred from the training earthquake counts within each URZ. We provide a set of forecasts for the New Zealand National Seismic Hazard Model update, which have mean rates up to four times higher in extensive low-seismicity regions compared to optimised smoothed-seismicity models. The forecasts’ impact in the hazard space is studied by implementing a negative binomial formulation in the OpenQuake hazard suite. For a 10% probability of exceedance in 50 years, forecasts that only reduce the spatial precision, i.e. stationary Poisson URZs, cause an increase of up to 0.1 g in low seismicity regions, compared to SSM. Furthermore, including the rate variability in URZ models increases the expected PGA up to 0.16 g in low-seismicity regions, whereas the effect on high-seismicity is minimal (~0.01–0.05 g). The hazard estimates presented here highlight the relevance, as well as the feasibility, of including analytical formulations of seismicity that extend beyond the inadequate stationary-Poisson description of seismicity. (The authors)