← Back to Gallery

Embracing Data Incompleteness for Better Earthquake Forecasting

The magnitude of completeness m_c of an earthquake catalog is of crucial importance for any statistical analysis of seismicity, and hence for PSHA. The threshold magnitude above which all earthquakes are assumed to be detected has been found to vary with space and time, depending in the longer term on the configuration of the seismic network, but also depending on temporarily increased seismicity rates in the form of short-term aftershock incompleteness. Most seismicity studies assume a constant m_c for the entire catalog, enforcing a compromise between deliberately misestimating m_c and excluding large amounts of valuable data.

Epidemic-Type Aftershock Sequence (ETAS) models have been shown to be the most successful earthquake forecasting models, both for short- and long-term hazard assessment. To be able to leverage historical data with high m_c as well as modern data which is complete at low magnitudes, we developed a method to calibrate the ETAS model when time-varying completeness magnitude m_c(t) is given as a step function. As a further refinement of the model, we designed a self-consistent algorithm to jointly estimate high-frequency detection incompleteness and ETAS parameters. For this, we generalized the concept of m_c and consider a rate- and magnitude-dependent detection probability – embracing incompleteness instead of avoiding it.

Preliminary results of pseudo-prospective forecasting experiments in California indicate that the newly gained information leads to significantly improved forecasts. Two features of our model are distinguished: Small earthquakes are allowed and assumed to trigger aftershocks, and ETAS parameters are estimated differently. We compare the forecasting performance of a model having both features, and two additional models each having one of the features, to the current state-of-the-art base model. This will shed light on which aspect is the most promising to pursue in the search of the next generation earthquake forecasting model.

Presenting Author: Leila Mizrahi

Student Presenter: Yes

Day: 4/19/2021

Time: 2:00 PM - 3:15 PM Pacific

Additional Authors