Sentence examples for motion prediction models from inspiring English sources
Implications of these results for the development and evaluation of motion prediction models are presented.
Peak ground accelerations of the artificial accelerograms are compared with those coming from an empirically derived ground motion prediction model.
In developing a motion prediction model it is important to initially consider the sources of variability that a model should reproduce.
An approximate procedure for assessing the upper bound of ground-motion intensity on the basis of ground-motion prediction models is also proposed.
To meet the needs for real-time and high-accuracy polar motion prediction, a hybrid model that integrated singular spectrum analysis (SSA), least-squares (LS) extrapolation and an autoregressive moving average (ARMA) model was proposed.
Additional context is provided by comparison of ground motion amplitudes with empirical prediction models and reconciling 'outliers' based on inferred physical mechanisms.
Because all asperities of the expected Nankai and Tonankai earthquake source models are deep, it is possible to neglect the effect of the oceanic water layer on the strong ground motion prediction for these source models. .
Because all asperities of the expected Nankai and Tonankai earthquake source models are deep, it is possible to neglect the effect of the oceanic water layer on the strong ground motion prediction for these source models.
We use a characterized source model proposed by Irikura and Miyake (2001), which is one of the most reliable approaches for broadband strong ground motion prediction, as a source model for strong ground motion simulation.
Synchronous motions are also predicted in the two prediction models and show good agreement with the test data.
When applying such models to ground motion prediction, it is unrealistic to expect that the precise parameters of the scenario source model can be known before an earthquake occurs because of the complexity of the nature of earthquakes.
