Earthquake Rate Models for Evolving Induced Seismicity Hazard in the Central and Eastern US

Abstract:

Injection-induced earthquake rates can vary rapidly in space and time, which presents significant challenges to traditional probabilistic seismic hazard assessment methodologies that are based on a time-independent model of mainshock occurrence. To help society cope with rapidly evolving seismicity, the USGS is developing one-year hazard models for areas of induced seismicity in the central and eastern US to forecast the shaking due to all earthquakes, including aftershocks which are generally omitted from hazards assessments (Petersen et al., 2015). However, the spatial and temporal variability of the earthquake rates make them difficult to forecast even on time-scales as short as one year. An initial approach is to use the previous year’s seismicity rate to forecast the next year’s seismicity rate. However, in places such as northern Oklahoma the rates vary so rapidly over time that a simple linear extrapolation does not accurately forecast the future, even when the variability in the rates is modeled with simulations based on an Epidemic-Type Aftershock Sequence (ETAS) model (Ogata, JASA, 1988) to account for earthquake clustering.

Instead of relying on a fixed time period for rate estimation, we explore another way to determine when the earthquake rate should be updated. This approach could also objectively identify new areas where the induced seismicity hazard model should be applied. We will estimate the background seismicity rate by optimizing a single set of ETAS aftershock triggering parameters across the most active induced seismicity zones -- Oklahoma, Guy-Greenbrier, the Raton Basin, and the Azle-Dallas-Fort Worth area -- with individual background rate parameters in each zone. The full seismicity rate, with uncertainties, can then be estimated using ETAS simulations and changes in rate can be detected by applying change point analysis in ETAS transformed time with methods already developed for Poisson processes.