ProbShakemap: a Python toolbox for urgent earthquake source uncertainty quantification

Seismic urgent computing aims at assessing the potential impact of earthquakes through rapid simulation-based ground-shaking forecasts. However, uncertainty quantification remains a significant challenge in this domain.

While current practice accounts for the uncertainty arising from Ground Motion Models (GMMs), it neglects the uncertainty about the source model, which is only known approximately in the first minutes after an earthquake. Addressing this issue involves propagating earthquake source uncertainty from a multi-scenarios ensemble that captures source variability to ground motion predictions. In principle, this could be accomplished with 3D modelling of seismic wave propagation for multiple earthquake sources. However, full ensemble simulation is unfeasible under emergency conditions with strict time constraints.

Here we present ProbShakemap, a Python toolbox which generates multi-scenario ensembles and delivers ensemble-based forecasts for urgent source uncertainty quantification. It implements GMMs to efficiently propagate source uncertainty from the ensemble of scenarios to ground motion predictions at a set of points, while also accounting for model uncertainty (by accommodating multiple GMMs, if available) along with their intrinsic uncertainty. Notably, ProbShakemap does not rely on any recorded data, and only requires the following event-specific information: latitude, longitude, magnitude and time. ProbShakemap incorporates functionalities from two open-source toolboxes routinely implemented in seismic hazard and risk analyses: the USGS ShakeMap software and the OpenQuake-engine.

We quantitatively test ProbShakemap against past earthquakes, illustrating its capability to rapidly quantify earthquake source uncertainty.