Estimating spatio-temporal variable parameters of Epidemic Type Aftershock Sequence model in a region with limited seismic network coverage: a case study of the East African Rift System

Summary The Epidemic-Type Aftershock Sequence (ETAS) model is currently the most powerful statistical seismicity model that reproduces the general characteristics of earthquake clustering in space and time. However, its application can be hampered by biased parameter estimations related to earthquake catalog deficiencies, particularly in regions where the spatial coverage of local recording networks is relatively poor. Here, we systematically investigate the possible influences of the effect introduced by data truncation through the choice of the cutoff magnitude (${m}_{cut})$ and missing events due to heterogeneity of the seismic network on ETAS parameter estimates along the East African Rift System (EARS). After dividing the region into six source zones based on rheological and mechanical behaviors, the ETAS model is fitted to the earthquakes within each zone using the Davidon-Fletcher-Powell optimization algorithm. The fits and variations in parameter estimates are compared for each zone to the others and the seismological implications are discussed. We found that some parameters vary as a function of ${m}_{cut}$ primarily driven by changes in catalog size. Additionally, a systematic regional dependency of ETAS parameters is found across source zones. Furthermore, a median heat flow value for each analyzed source zone in the EARS is calculated. In contrast to previous findings in other tectonic settings, the results reveal no significant correlations between the crustal heat flows and the ETAS parameters describing earthquake productivity (${K}_0$) and the relative efficiency of an earthquake with magnitude M to produce aftershocks ($\alpha $). Our findings have significant implications for understanding the mechanisms of earthquake interaction and, therefore, provide tight constraints on the model's parameters that may serve as a testbed for existing earthquake forecasting models in this region where the vulnerability of local buildings and structures exacerbate seismic risk.