def save_model(h, vs, vpvs=1.73, outfile=None):
"""Save input model as ASCII file."""
h = np.array(h)
vs = np.array(vs)
vp = vs * vpvs
rho = vp * 0.32 + 0.77
if outfile is None:
outfile = 'syn_mod.dat'
x = np.arange(10)
target = Targets.PReceiverFunction(x=x, y=None)
target.moddata.plugin.write_startmodel(h, vp, vs, rho, outfile)
logger.info('Model file saved: %s' % outfile)
def return_rfdata(h, vs, vpvs=1.73, pars=dict(), x=None):
"""Return dictionary of forward modeled data based on RFMini.
- x must be linspace to provide an equal sampling rate
- pars is a dictionary of additional parameters used for RF
computation (uses defaults if empty), such as:
- gauss: Gaussian factor (low pass filter),
- water: water level,
- p: slowness in s/deg
- nsv: near surface velocity (km/s) for RF rotation angle.
"""
if x is None:
x = np.linspace(-5, 35, 201)
h = np.array(h)
vs = np.array(vs)
gauss = pars.get('gauss', 1.0)
water = pars.get('water', 0.001)
p = pars.get('p', 6.4)
nsv = pars.get('nsv', None)
target5 = Targets.PReceiverFunction(x=x, y=None)
target5.moddata.plugin.set_modelparams(gauss=gauss,
water=water,
p=p,
nsv=nsv)
target6 = Targets.SReceiverFunction(x=x, y=None)
target6.moddata.plugin.set_modelparams(gauss=gauss,
water=water,
p=p,
nsv=nsv)
vp = vs * vpvs
rho = vp * 0.32 + 0.77
targets = [target5, target6]
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 RF with gauss: %.2f, waterlevel: ' % gauss +
'%.4f, slowness: %.2f' % (water, p))
return data
'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
