Forecasting how many earthquakes will occur following a potentially damaging earthquake helps the public and emergency operators stay safe and make informed decisions. The U.S. Geological Survey issues aftershock forecasts following potentially damaging earthquakes, using models to predict the number of earthquakes that should occur within the next day, week, month, and year with 95% confidence to reflect the uncertainty in aftershock behavior. The USGS considers the forecast to be successful when the number of earthquakes observed within the forecasted time period is within the 95% confidence interval. For aftershock sequences that occur along the forearc of the Alaskan subduction zone, the observations consistently lie within this broad range of success, however the forecasts systematically over-predict the number of aftershocks. This suggests that the parameters which drive the forecast model’s rate of decay needs to change to reflect the decreased observed aftershock activity. I analyzed seventeen earthquake sequences in the Alaska forearc region and have developed a set of parameters that may improve early-sequence aftershock forecasting in the region. I stacked the 17 sequences and used maximum likelihood estimation to determine the new parameters and their uncertainty. Following the lessons of Page et al. (BSSA, 2016), I combined the inter-sequence variability and the uncertainty of the parameters from the stacked sequences to produce the total standard deviation for a new forecast model
