Global Operational Earthquake Response with Imaging Geodesy

The U.S. Geological Survey National Earthquake Information Center (NEIC) leads real-time efforts to provide rapid and accurate assessments of the impacts of global earthquakes, including estimates of ground shaking, ground failure, and the resulting human impacts. These efforts primarily rely on analysis of the seismic wavefield to characterize the source of the earthquake, which in turn informs a suite of disaster response products such as ShakeMap and PAGER. In recent years, the proliferation of rapidly acquired and openly available remote sensing observations has opened new avenues for responding to earthquakes around the world in the days following significant events. Geodetic observations, particularly from interferometric synthetic aperture radar (InSAR) and satellite optical imagery, provide a means to robustly constrain the dimensions and spatial complexity of earthquakes beyond what is typically possible with seismic observations alone. Here, we highlight recent cases where geodetic observations directly contributed important information to operational earthquake response efforts of the NEIC- from informing and validating seismically-derived source models to independently constraining earthquake impact products - and the conditions under which geodetic observations improve earthquake response products. We use examples from the 2013 Mw7.7 Baluchistan, Pakistan, 2015 Mw7.8 Gorkha, Nepal, and 2018 Mw7.5 Palu, Indonesia earthquakes to highlight the varying ways geodetic observations have contributed to earthquake response efforts at the NEIC. In examples like the Palu earthquake, satellite geodetic observations served to more precisely define rupture extent and location relative to major population centers, leading to elevated impact estimates that accurately reflected the true impacts of the earthquake. We additionally outline future avenues for systematic and operational geodetic detection and analysis of earthquakes through emerging technologies like Artificial Neural Networks.