Identifying potentially induced seismicity and assessing statistical   significance in Oklahoma and California
  We develop a statistical method for identifying induced seismicity from large datasets and apply the method to decades of wastewater disposal and seismicity data in California and Oklahoma. The method is robust against a variety of potential pitfalls. The study regions are divided into gridblocks. We use a longitudinal study design, seeking associations between seismicity and wastewater injection along time-series within each gridblock. The longitudinal design helps control for non-random application of wastewater injection. We define a statistical model that is flexible enough to describe the seismicity observations, which have temporal correlation and high kurtosis. In each gridblock, we find the maximum likelihood estimate for a model parameter that relates induced seismicity hazard to total volume of wastewater injected each year. To assess significance, we compute likelihood ratio test statistics in each gridblock and each state, California and Oklahoma. Resampling is used to empirically derive reference distributions used to estimate p-values from the likelihood ratio statistics. In Oklahoma, the analysis finds with extremely high confidence that seismicity associated with wastewater disposal (or other related activities, such as reservoir depletion) has occurred. In California, the analysis finds that seismicity associated with wastewater disposal has probably occurred, but the result is not strong enough to be conclusive. We identify areas where temporal association between wastewater disposal and seismicity is apparent. Our method could be applied to other datasets, extended to identify risk factors that increase induced seismic hazard, or modified to test alternative statistical models for natural and induced seismicity. 
Mark McClure, Riley Gibson, Kitkwan Chiu, and Rajesh Ranganath
physics.geo-ph