In an effort to help address debates on the usefulness of operational earthquake forecasting (OEF), we illustrate a number of OEF products that could be automatically generated in near‐real time. To exemplify, we use an M 7.1 mainshock on the Hayward fault, which is very similar to the U.S. Geological Survey (USGS) HayWired earthquake planning scenario. Given that there is always some background level of hazard or risk, we emphasize that probability gains (the ratio of short‐term to long‐term‐average estimates) might be of particular interest to users. We also illustrate how such gains are highly sensitive to forecast duration and latency, with the latter representing how long it takes to generate the forecast and/or to take action. The influence of fault‐based information, which has traditionally been ignored in OEF, is also evaluated using the newly developed the third Uniform California Earthquake Rupture Forecast epidemic‐type aftershock sequence (UCERF3‐ETAS) model. We find that the inclusion of faults only makes a difference for hazard and risk metrics that are dominated by large‐event likelihoods. We also show how the ShakeMap of a mainshock represents a decent estimate of the ground motions that have a 6% chance of being exceeded due to aftershocks in the week that follows. The ultimate value of these types of OEF products can only be determined in the context of specific uses, and because these vary widely, institutions responsible for providing OEF products will depend heavily on user feedback, especially when making resource‐allocation decisions.
Additional publication details
|Publication Subtype||Journal Article|
|Title||Candidate products for operational earthquake forecasting illustrated using the HayWired planning scenario, including one very quick (and not‐so‐dirty) hazard‐map option|
|Series title||Seismological Research Letters|
|Publisher||Seismological Society of America|
|Contributing office(s)||Geologic Hazards Science Center, John Wesley Powell Center for Analysis and Synthesis|
|Google Analytic Metrics||Metrics page|