Site-Condition Proxies, Ground Motion Variability, and Data-Driven GMPEs: Insights from the NGA-West2 and RESORCE Data Sets

We compare the ability of various site-condition proxies (SCPs) to reduce the aleatory variability of ground motion prediction equations (GMPEs). Three SCPs (measured V S30 , inferred V S30 , local topographic slope) and two accelerometric databases (RESORCE and NGA-West2) are considered. An artificial neural network (ANN) approach including a random-effect procedure is used to derive GMPEs setting the relationship between peak ground acceleration ( PGA), peak ground velocity ( PGV), pseudo-spectral acceleration [ PSA( T)], and explanatory variables ( M w , R JB , and V S30 or Slope). The analysis is performed using both discrete site classes and continuous proxy values. All “non-measured” SCPs exhibit a rather poor performance in reducing aleatory variability, compared to the better performance of measured V S30 . A new, fully data-driven GMPE based on the NGA-West2 is then derived, with an aleatory variability value depending on the quality of the SCP. It proves very consistent with previous GMPEs built on the same data set. Measuring V S30 allows for benefit from an aleatory variability reduction up to 15%.