d_1234#

PySeismoSoil.helper_gof_scores.d_1234(measurement: ndarray, simulation: ndarray, fmin: float | None = None, fmax: float | None = None, baseline: bool = True, show_fig: bool = False) → tuple[float, float, float, float][source]#

Calculate the first four goodness-of-fit scores in the GoF scheme described in Shi & Asimaki (2017).

Parameters:

  • measurement (np.ndarray) – Measured time history. Must be two-columned.

  • simulation (np.ndarray) – Simulated time history. Must be two-columned.

  • fmin (float | None) – Minimum frequency to be considered, in units of Hz. Default is (sampling frequency)/(length of time series).

  • fmax (float | None) – Maximum frequency to be considered, in units of Hz. Default is (sampling frequency)/2.0.

  • baseline (bool) – Whether or not to perform baseline correction of the time series.

  • show_fig (bool) – Whether or not to plot.

Returns:

  • d1 (float) – Normalized arias intensity score.

  • d2 (float) – Normalized energy integral score.

  • d3 (float) – Peak arias intensity score.

  • d4 (float) – Peak energy integral score.

References

  1. J. Shi, and D. Asimaki. (2017) “From stiffness to strength: Formulation and validation of a hybrid hyperbolic nonlinear soil model for site- response analyses.” Bulletin of the Seismological Society of America. Vol. 107, No. 3, 1336-1355.

Notes

Original Matlab code (c) Jian Shi, 2/17/2015 Ported to Python by Flora Xia, 02/2024