Exploring the Effect of Minimum Magnitude on California Seismic Hazard Maps

Abstract

A recent topic of interest is the performance of probabilistic seismic hazard maps relative to historical shaking datasets. Maps developed for different countries appear to overpredict shaking relative to historical shaking datasets. To explore whether this discrepancy arises because of incompleteness in historical datasets, we consider maps and historical data from California. Current probabilistic seismic hazard maps for California appear to predict stronger short period shaking than historical maxima captured by the California Historical Intensity Mapping Project (CHIMP) dataset between 1857 and 2019. We estimate that CHIMP has a magnitude completeness between M 6-6.6, whereas California hazard maps assume a minimum magnitude (MMin) of 5. Disaggregating the maps shows that earthquakes smaller than M 6 and 6.6 respectfully contribute about 25% and 45% of the hazard across California. Increasing the hazard map's MMin to 6 and 6.6 respectively reduces the discrepancy between predicted and observed shaking by approximately 10-20% and 30-35%. These reductions are not enough to bring the maps and data in alignment. Thus, MMin inconsistencies contribute to, but are not the sole cause of, the discrepancy between predicted and historically observed shaking. These results may be generally applicable to maps elsewhere, although MMin will vary for different historical datasets.

Why do seismic hazard models worldwide appear to overpredict historical intensity observations?

“…CHIMP catalog completeness may be as high as ~magnitude (M) 6.6 but is no lower than M 6, whereas the hazard model [U.S. Geological Survey National Seismic Hazard Model 2018 ( 25 )] has minimum magnitude ~M 5. Numerical experiments were done by recalculating the hazard model with minimum magnitude M 6 and comparing it to a modified CHIMP catalog consisting only of observations from M 6+ ( 26 ). Correcting for the different magnitudes of completeness reduces the discrepancy by approximately 10 to 15%, which is not enough to bring the models and data in alignment.…”