Towards automated rapid earthquake dynamics characterization: a proof of concept based on the 2024 Mw7.4 Noto Peninsula, Japan, earthquake

Rapid earthquake source characterization following an earthquake is crucial for effective emergency response and risk management. It may help for example in warning of potential secondary hazards like tsunamis or aftershocks or may guide resource allocation towards efficient coordination of rescue operations. The United States Geological Survey (USGS) provides routinely generated kinematic models based on teleseismic body and surface wave data, CMT solutions, and scaling relationships (Hayes, 2017). For the most significant earthquakes, models can be iteratively updated based on available data, such as strong motion or geodetic data (Goldberg, 2022). Nevertheless, in most cases, such automatically derived source characterization is limited to static slip. Yet, a characterization of rupture kinematics or dynamics would be beneficial as well. 

We here propose a workflow for the automated generation of earthquake dynamic rupture scenarios based on the fault slip distribution of a given kinematic model, and we present a proof of concept based on the 2024 Mw7.4 Noto Peninsula, Japan, earthquake. Our workflow, here based on the USGS model,  consists of retrieving the fault slip associated with the kinematic model, automatically generating a mesh and the input files, and running a set of dynamic rupture scenarios, informed by the stress change of the kinematic model. We plan to explore a limited parameter set, from which a preferred scenario could be selected, based on the fit to the input slip distribution, the fit to routinely inferred moment rate release, or to other available datasets, such as teleseismic, geodetic, or strong motion data. 

We expect such routinely derived dynamic rupture scenarios to be beneficial in the emergency response phase, for example, to better assess the potential damage to structures. More generally, such systematic source characterization could feed earthquake source databases, and therefore contribute to improving earthquake hazard assessment and the overall understanding of tectonic processes.