Bayesian identification of multiple seismic change points and varying seismic rates caused by induced seismicity

The Central and Eastern United States (CEUS) has experienced an abnormal increase in seismic activity, which is believed to be related to anthropogenic activities. The U.S. Geological Survey has acknowledged this situation and developed the CEUS 2016 1 year seismic hazard model using the catalog of 2015 by assuming stationary seismicity in that period. However, due to the nonstationary nature of induced seismicity, it is essential to identify change points for accurate probabilistic seismic hazard analysis (PSHA). We present a Bayesian procedure to identify the most probable change points in seismicity and define their respective seismic rates. It uses prior distributions in agreement with conventional PSHA and updates them with recent data to identify seismicity changes. It can determine the change points in a regional scale and may incorporate different types of information in an objective manner. It is first successfully tested with simulated data, and then it is used to evaluate Oklahoma's regional seismicity.