Sensitivity of Probabilistic Seismic Hazard Obtained by Algorithmic Differentiation: A Feasibility Study - Molkenthin,C; Scherbaum,F; Griewank,A; Kuehn,N; Stafford,PJ; Leovey,H (Bulletin of the Seismological Society of America, 2015)
Probabilistic seismic-hazard analysis (PSHA) is the current tool of thetrade used to estimate the future seismic demands at a site of interest. A modern PSHArepresents a complex framework that combines different models with numerous inputs.It is important to understand and assess the impact of these inputs on the modeloutput in a quantitative way. Sensitivity analysis is a valuable tool for quantifyingchanges of a model output as inputs are perturbed, identifying critical input parameters,and obtaining insight about the model behavior. Differential sensitivity analysisrelies on calculating first-order partial derivatives of the model output with respect toits inputs; however, obtaining the derivatives of complex models can be challenging.In this study, we show how differential sensitivity analysis of a complex frameworksuch as PSHA can be carried out using algorithmic/automatic differentiation(AD). AD has already been successfully applied for sensitivity analyses in variousdomains such as oceanography and aerodynamics. First, we demonstrate the feasibilityof the AD methodology by comparing AD-derived sensitivities with analyticallyderived sensitivities for a basic case of PSHA using a simple ground-motion predictionequation. Second, we derive sensitivities via AD for a more complex PSHA studyusing a stochastic simulation approach for the prediction of ground motions. The presentedapproach is general enough to accommodate more advanced PSHA studies ofgreater complexity.