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- Earthquake ground motions (1)
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In this article, we address the question of how observed ground-motion data can most effectively be modeled for engineering seismological purposes. Toward this goal, we use a data-driven method, based on a deep-learning autoencoder with a variable number of nodes in the bottleneck layer, to determine how many parameters are needed to reconstruct synthetic and observed ground-motion data in terms of their median values and scatter. The reconstruction error as a function of the number of nodes in the bottleneck is used as an indicator of the underlying dimensionality of ground-motion data, that is, the minimum number of predictor variables needed in a ground-motion model. Two synthetic and one observed datasets are studied to prove the performance of the proposed method. We find that mapping ground-motion data to a 2D manifold primarily captures magnitude and distance information and is suited for an approximate data reconstruction. The data reconstruction improves with an increasing number of bottleneck nodes of up to three and four, but it saturates if more nodes are added to the bottleneck.
1-D site response analysis dominates earthquake engineering practice, while local 2-D/3-D models are often required at sites where the site response is complex. For such sites, the 1-D representation of the soil column can account neither for topographic effects or dipping layers nor for locally generated horizontally propagating surface waves. It then remains a crucial task to identify whether the site response can be modelled sufficiently precisely by 1-D analysis. In this study we develop a method to classify sites according to their 1-D or 2-D/3-D nature. This classification scheme is based on the analysis of surface earthquake recordings and the evaluation of the variability and similarity of the horizontal Fourier spectra. The taxonomy is focused on capturing significant directional dependencies and interevent variabilities indicating a more probable 2-D/3-D structure around the site causing the ground motion to be more variable. While no significant correlation of the 1-D/3-D site index with environmental parameters and site proxies seems to exist, a reduction in the within-site (single-station) variability is found. The reduction is largest (up to 20 per cent) for purely 1-D sites. Although the taxonomy system is developed using surface stations of the KiK-net network in Japan as considerable additional information is available, it can also be applied to any (non-downhole array) site.