Utility of Stochastic Ground Motion Models in Conditional Spectrum-Based Selection Consistent with the Causal Earthquakes

Conditional Spectrum (CS), characterized by a conditional mean and conditional standard deviation, serves as a target spectrum linking seismic hazard information to ground motion selection for seismic demand analysis. Current Ground Motion Selection and Modification (GMSM) methods aim to align with this target spectrum but face challenges due to the limited availability of recorded ground motions and potential modifications, leading to difficulties in ensuring selected ground motions accurately reflect site characteristics. Addressing this issue, the study presents an algorithm to select hazard-consistent CS-based records utilizing a stochastic ground motion model with a dual objective: (1) alleviate the need for scaling during the selection process, and (2) select records consistent with the target hazard and the contributing causal scenarios. To achieve both objectives, the study generates a database of hazard-targeted ground motions utilizing the kriging surrogate with scenarios sampled from the disaggregation matrix. Then, a postprocessing step explicitly considers causative parameters in the selection process. The algorithm's potential to select site-specific ground motions is demonstrated using sites in the Western United States, providing insights into the computational cost and accuracy. Additionally, statistical comparisons are conducted to explore circumstances where similar hazard consistency can be achieved without the postprocessing step, reducing the overall computational cost. Finally, the selected ground motions are favorably compared to those selected through traditional conditional spectrum-based approaches based on their spectral shape and ground motion intensity measures.