Document Type




Format of Original

14 p.

Publication Date



Seismological Society of America

Source Publication

Bulletin of the Seismological Society of America

Source ISSN


Original Item ID

doi: 10.1785/0120110293


The Conditional Spectrum (CS) is a target spectrum (with conditional mean and conditional standard deviation) that links seismic hazard information with ground motion selection for nonlinear dynamic analysis. Probabilistic seismic hazard analysis (PSHA) estimates the ground motion hazard by incorporating the aleatory uncertainties in all earthquake scenarios and resulting ground motions as well as the epistemic uncertainties in ground motion prediction models (GMPMs) and seismic source models. Typical CS calculations to date are produced for a single earthquake scenario using a single GMPM, but more precise use requires consideration of at least multiple causal earthquakes and multiple GMPMs that are often considered in a PSHA computation. This paper presents the mathematics underlying these more precise CS calculations. Despite requiring more effort to compute than approximate calculations using a single causal earthquake and GMPM, the proposed approach produces an exact output that has a theoretical basis. To demonstrate the results of this approach and compare the exact and approximate calculations, several example calculations are performed for real sites in the western U.S. (WUS). The results also provide some insights regarding the circumstances under which approximate results are likely to closely match more exact results. To facilitate these more precise calculations for real applications, the exact CS calculations can now be performed for real sites in the U.S. using new deaggregation features in the U.S. Geological Survey hazard mapping tools. Details regarding this implementation are discussed in this paper.


Published version. Bulletin of the Seismological Society of America, Vol. 103, No. 2A (April 2013): 1103-1116. DOI. © 2013 Seismological Society of America. Used with permission.