In recent years, there has also been significant progress using data science techniques to complement regression or inversion approaches where data is plentiful (Derras et al., 2014 Khosravikia et al., 2019 Withers et al., 2020). This method has the benefit of relying upon physical trends to guide the development of ground motion behavior and thus can perform well when extrapolated to areas with limited data.
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In others, a more holistic approach is pursued, to better model the general features of ground motion from the source to receiver stations (Graves et al., 2011) and to help inform future GMMs (as in Wirth et al., 2018 Frankel et al., 2018 Moschetti et al., 2018, Withers et al., 2019a, Withers et al., 2019b).Ī typical technique of choice to update and improve ground motion characterization is to use mixed-effects regression on empirical datasets, using guidelines from historical models to best characterize behavior to incorporate into improved GMMs. In some cases, synthetic data focuses on a particular ground motion effect (e.g., hanging wall: Donahue and Abrahamson, 2014 basin effects: Day et al., 2008). This inability to describe ground motion behavior where limited recorded data exists has led researchers to supplement empirical models with simulated ground motions.
In all these examples, the lack of recorded data is a significant hindrance to adequately understanding new explanations of ground motion phenomena and determining the success of advancements via novel approaches. Other studies search for alternative methods to describe specific ground motion trends (e.g., near-source seismic directivity: Spudich et al., 2013). There are several techniques that have attempted to fundamentally rethink ground motion modeling strategies some efforts are focusing on moving towards nonergodic models (Kuehn et al., 2019 Dawood and Rodriguez-Marek, 2013 Landwehr et al., 2016 Abrahamson et al., 2019 Kuehn and Abrahamson, 2020) that are tailored for application to certain geographic regions. Radical improvements can only be overcome through fundamentally new approaches to ground motion modeling. However, the ability of the empirical GMM approach to provide substantial innovations is diminishing. These empirical models are a fundamental component of the U.S. GMMs are currently able to broadly characterize strong ground motions for varying tectonic zones, regions, and style of faulting. There is an increasing need for seismic hazard analysis to incorporate regional variations in ground motion characteristics that arises from variations in geologic processes and structures. These models are ergodic in the sense that they are empirically derived from data across many regions for a given seismotectonic region, such as for shallow crustal, stable, and subduction regions. The NSHM currently uses ergodic ground-motion models (GMMs). building codes, as well as other engineering-design requirements, risk assessments, and planning decisions (e.g., Petersen et al., 2014 Petersen et al., 2020). Geological Survey and forms the basis for the seismic provisions of U.S. Please follow the guidance of the airport staff and act calmly.The National Seismic Hazard Model (NSHM) is an important product of the U.S.Do not panic when riding the elevator, please wait for maintenance personnel to come to the rescue.
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