pygmm.abrahamson_shi_yang_2016.AbrahamsonShiYang2016

class pygmm.abrahamson_shi_yang_2016.AbrahamsonShiYang2016(scenario, backbone=None, pga=None, sa_t1=None)

Abrahamson, Shi, and Yang (2016, [Abrahamson et al., 2016]) Arias intensity model.

Conditional ground-motion model (CGMM) and scenario-based models for Arias intensity (Ia, m/s) for shallow crustal tectonic settings, consistent with the NGA-West2 ground-motion models. Calibrated on the NGA-West2 database (~15,209 recordings, 326 events, M 3.0–7.9).

Two evaluation modes are supported:

Conditional mode (backbone=None): Ia is estimated from user-supplied PGA and SA(T=1 s) values (both in g) using Equations (3.3) and (3.4). The aleatory variability is \(\sigma = \sqrt{\tau^2 + \phi^2} \approx 0.38\).

Scenario mode (backbone is one of 'ASK14', 'BSSA14', 'CB14', 'CY14', 'I14'): The median PGA and SA(T=1 s), and their lognormal standard deviations, are taken from the chosen NGA-West2 GMM. These are then combined with the conditional model via propagation of errors (Equation 4.4) to yield a full scenario-based Ia estimate.

Parameters:

• scenario (pygmm.model.Scenario) – Earthquake scenario. When using scenario mode the scenario must also contain all parameters required by the chosen backbone GMM.

• backbone (str or None, optional) – Backbone NGA-West2 GMM for scenario mode. Accepted values: 'ASK14', 'BSSA14', 'CB14', 'CY14', 'I14'. Default None selects conditional mode.

• pga (float or None, optional) – Peak ground acceleration (g) for conditional mode. Required when backbone is None.

• sa_t1 (float or None, optional) – Spectral acceleration at T=1 s (g) for conditional mode. Required when backbone is None.

References

[1]

Abrahamson, C., Shi, H.-J. M., and Yang, B. (2016). “Ground-Motion Prediction Equations for Arias Intensity Consistent with the NGA-West2 Ground-Motion Models.” PEER Report No. 2016/05, Pacific Earthquake Engineering Research Center, University of California, Berkeley.

NAME = 'Abrahamson, Shi, & Yang (2016)'

Long name of the model

ABBREV = 'ASY16'

Short name of the model

C1 = 0.47

C2 = -0.28

C3 = 0.5

C4 = 1.52

C5 = 0.21

C8 = 0.09

TAU = 0.15

PHI = 0.35

SIGMA_COND = 0.3807886552931954

C4_DERIV = 1.53

C5_DERIV = 0.2

RHO_PGA_SAT1 = 0.52

SUPPORTED_BACKBONE_MODELS = ('ASK14', 'BSSA14', 'CB14', 'CY14', 'I14')

LIMITS = {'dist_rup': (0, 400), 'mag': (3.0, 7.9), 'v_s30': (180, 2000)}

Limits of model applicability

PARAMS = [<pygmm.model.NumericParameter object>, <pygmm.model.NumericParameter object>, <pygmm.model.NumericParameter object>, <pygmm.model.NumericParameter object>, <pygmm.model.NumericParameter object>, <pygmm.model.CategoricalParameter object>]

Model parameters

__init__(scenario, backbone=None, pga=None, sa_t1=None)

Initialize the model.

property ln_ia

Natural log of the median Arias intensity (ln(m/s)).

property ia

Median Arias intensity (m/s).

property ln_std

Total lognormal standard deviation of Arias intensity.

property tau

Between-event standard deviation (conditional mode only).

property phi

Within-event standard deviation (conditional mode only).

property ia_plus_sigma

exp(ln_ia + ln_std).

Type:

84th-percentile Arias intensity (m/s)

property ia_minus_sigma

exp(ln_ia - ln_std).

Type:

16th-percentile Arias intensity (m/s)

property scenario