def return_swddata(h, vs, vpvs=1.73, pars=dict(), x=None):
"""Return dictionary of forward modeled data based on Surf96."""
if x is None:
x = np.linspace(1, 40, 20)
h = np.array(h)
vs = np.array(vs)
mode = pars.get('mode', 1) # fundamental mode
target1 = Targets.RayleighDispersionPhase(x=x, y=None)
target1.moddata.plugin.set_modelparams(mode=mode)
target2 = Targets.RayleighDispersionGroup(x=x, y=None)
target2.moddata.plugin.set_modelparams(mode=mode)
target3 = Targets.LoveDispersionPhase(x=x, y=None)
target3.moddata.plugin.set_modelparams(mode=mode)
target4 = Targets.LoveDispersionGroup(x=x, y=None)
target4.moddata.plugin.set_modelparams(mode=mode)
vp = vs * vpvs
rho = vp * 0.32 + 0.77
targets = [target1, target2, target3, target4]
data = {}
for i, target in enumerate(targets):
xmod, ymod = target.moddata.plugin.run_model(
h=h, vp=vp, vs=vs, rho=rho)
data[target.ref] = np.array([xmod, ymod])
logger.info('Compute SWD for %d periods, with model vp/vs %.2f.'
% (x.size, vpvs))
return data
'nlays': 3,
'noise': truenoise,
'explike': explike,
}
print truenoise, explike
#
# ----------------------------------------------------------- DEFINE TARGETS
#
# Only pass x and y observed data to the Targets object which is matching
# the data type. You can chose for SWD any combination of Rayleigh, Love, group
# and phase velocity. Default is the fundamendal mode, but this can be updated.
# For RF chose P or S. You can also use user defined targets or replace the
# forward modeling plugin wih your own module.
target1 = Targets.RayleighDispersionPhase(xsw, ysw)
target2 = Targets.PReceiverFunction(xrf, yrf)
target2.moddata.plugin.set_modelparams(gauss=1., water=0.01, p=6.4)
# Join the targets. targets must be a list instance with all targets
# you want to use for MCMC Bayesian inversion.
targets = Targets.JointTarget(targets=[target1, target2])
#
# --------------------------------------------------- Quick parameter update
#
# "priors" and "initparams" from config.ini are python dictionaries. You could
# also simply define the dictionaries directly in the script, if you don't want
# to use a config.ini file. Or update the dictionaries as follows, e.g. if you
# have station specific values, etc.
# See docs/bayhunter.pdf for explanation of parameters
'nlays': 3,
'noise': truenoise,
'explike': explike,
}
print truenoise, explike
#
# ----------------------------------------------------------- DEFINE TARGETS
#
# Only pass x and y observed data to the Targets object which is matching
# the data type. You can chose for SWD any combination of Rayleigh, Love, group
# and phase velocity. Default is the fundamendal mode, but this can be updated.
# For RF chose P or S. You can also use user defined targets or replace the
# forward modeling plugin wih your own module.
target1 = Targets.RayleighDispersionPhase(xsw, ysw, yerr=ysw_err)
target2 = Targets.PReceiverFunction(xrf, yrf)
target2.moddata.plugin.set_modelparams(gauss=1., water=0.01, p=6.4)
# Join the targets. targets must be a list instance with all targets
# you want to use for MCMC Bayesian inversion.
targets = Targets.JointTarget(targets=[target1, target2])
#
# --------------------------------------------------- Quick parameter update
#
# "priors" and "initparams" from config.ini are python dictionaries. You could
# also simply define the dictionaries directly in the script, if you don't want
# to use a config.ini file. Or update the dictionaries as follows, e.g. if you
# have station specific values, etc.
# See docs/bayhunter.pdf for explanation of parameters