TURNkey Report D3.8 - Report on improved procedures for rapid mapping of earthquake shaking, including adjustment factors for local site effects (RRE)

This report details the procedures that have been applied, developed or improved in Task 3.4 for the rapid generation of ground shaking maps (i.e., shake-maps). The report revolves around several actions and technical results, which are organized as follows:

 Section 3 summarizes the state-of-the-art of current shake-map algorithms and systems, based on a review by Guérin-Marthe et al. (2021). The USGS ShakeMap v4 approach is compared to a method based on Bayesian updating, which is put forward as one of the technical solutions to be implemented in the TURNkey platform.

 Section 4 is based on previous work carried out in Task 3.3.2: in each TB, various GMMs (Ground-Motion Models) are evaluated and ranked, via different scoring metrics. The selected GMMs may then be used for the generation of shake-maps in each TB.

 Section 5 details novel research efforts, where the GMM coefficients are updated in order to match the observations. This approach is useful to rapidly update a GMM in a given area for a given earthquake, so that the updated GMM may be reused for subsequent events (e.g., in a seismic sequence) in order to improve the accuracy of ground-shaking estimates. Two parallel and complementary methods are presented, namely a direct calibration of the coefficients (EUC) and a Bayesian updating of the uncertain coefficients in the shake-map (BRGM).

 Section 6 discusses site amplification models that are available in each TB: besides local models, Vs30 maps generated at the European level (in the European project SERA) are extracted for each TB. Recommendations are given for each TB on which model should be applied, and whether the SERA model represents a satisfying approximation.

 Section 7 explores additional sources of observations that may be used to characterise shake-maps: collection and aggregation of felt reports by EMSC; and extraction of Twitter data and integration as soft evidence in the shake-maps by BRGM.

The Bayesian approach for the derivation of shake-maps has been implemented in a Python code, which is briefly described in the companion deliverable report D3.9.