def __init__(self,
factor=1.,
thresh=0.5,
waterdepth=-1.,
vpwater=1.5,
stdfactor=2.):
self.data = data.data1d()
self.factor = factor
self.thresh = thresh
self.stdfactor = stdfactor
# models
self.avg_model = vmodel.model1d()
self.min_model = vmodel.model1d()
self.init_model = vmodel.model1d()
self.real_model = vmodel.model1d()
self.temp_model = vmodel.model1d()
#
self.vprfwrd = vprofile.vprofile1d()
self.waterdepth = waterdepth
self.vpwater = vpwater
#
self.avg_misfit = 0.
self.code = ''
return
import vprofile import modparam vpr = vprofile.vprofile1d() vpr.readdisp(infname='./disp_-112.2_36.4_lov.txt', wtype='l') vpr.readdisp(infname='./disp_-112.2_36.4_ray.txt', wtype='r') vpr.readaziamp(infname='./aziamp_-112.2_36.4.txt', wtype='r') vpr.readaziphi(infname='./aziphi_-112.2_36.4.txt', wtype='r') vpr.readmod(infname='mod_-112.2.36.4.mod', mtype='tti') vpr.getpara(mtype='tti') vpr.update_mod(mtype='tti') vpr.model.ttimod.get_rho() vpr.get_vmodel(mtype='tti') vpr.get_period() vpr.compute_tcps(wtype='ray') vpr.compute_tcps(wtype='love') vpr.model.ttimod.mod2para() # # vpr.model.ttimod.para.new_paraval(1) vpr.model.ttimod.para2mod() vpr.model.ttimod.update() vpr.get_vmodel(mtype='tti') vpr.model.dipArr[vpr.model.dipArr != 0.] = 90. vpr.model.strikeArr[vpr.model.strikeArr != 0.] = 33. vpr.model.init_etensor() vpr.model.rot_dip_strike()
def mc_inv_iso(self, instafname=None, ref=True, phase=True, group=False, outdir='./workingdir', wdisp=0.2, rffactor=40.,\
monoc=True, verbose=False, step4uwalk=1500, numbrun=10000, subsize=1000, nprocess=None, parallel=True):
"""
Bayesian Monte Carlo joint inversion of receiver function and surface wave data for an isotropic model
==================================================================================================================
::: input :::
instafname - input station list file indicating the stations for joint inversion
ref - include receiver function data or not
phase/group - include phase/group velocity dispersion data or not
outdir - output directory
wdisp - weight of dispersion curve data (0. ~ 1.)
rffactor - factor for downweighting the misfit for likelihood computation of rf
monoc - require monotonical increase in the crust or not
step4uwalk - step interval for uniform random walk in the parameter space
numbrun - total number of runs
subsize - size of subsets, used if the number of elements in the parallel list is too large to avoid deadlock
nprocess - number of process
parallel - run the inversion in parallel or not
==================================================================================================================
"""
if not os.path.isdir(outdir):
os.makedirs(outdir)
if instafname is None:
stalst = self.waveforms.list()
else:
stalst = []
with open(instafname, 'r') as fid:
for line in fid.readlines():
sline = line.split()
if sline[2] == '1':
stalst.append(sline[0])
if not ref and wdisp != 1.:
wdisp = 1.
print 'wdisp is forced to be 1. for inversion without receiver function data'
if phase and group:
dispdtype = 'both'
elif phase and not group:
dispdtype = 'ph'
else:
dispdtype = 'gr'
ista = 0
Nsta = len(stalst)
for staid in stalst:
netcode, stacode = staid.split('.')
staid_aux = netcode+'_'+stacode
stla, elev, stlo = self.waveforms[staid].coordinates.values()
#-----------------------------
# get data
#-----------------------------
vpr = vprofile.vprofile1d()
if phase:
try:
indisp = self.auxiliary_data['RayDISPcurve']['ray']['ph'][staid_aux].data.value
vpr.get_disp(indata=indisp, dtype='ph', wtype='ray')
except KeyError:
print 'WARNING: No phase dispersion data for station: '+staid
if group:
try:
indisp = self.auxiliary_data['RayDISPcurve']['ray']['gr'][staid_aux].data.value
vpr.get_disp(indata=indisp, dtype='gr', wtype='ray')
except KeyError:
print 'WARNING: No group dispersion data for station: '+staid
if vpr.data.dispR.npper == 0 and vpr.data.dispR.ngper == 0:
print 'WARNING: No dispersion data for station: '+staid
continue
if ref:
try:
inrf = self.auxiliary_data['RefR'][staid_aux+'_P'].data.value
N = self.auxiliary_data['RefR'][staid_aux+'_P'].parameters['npts']
dt = self.auxiliary_data['RefR'][staid_aux+'_P'].parameters['delta']
indata = np.zeros((3, N))
indata[0, :]= np.arange(N)*dt
indata[1, :]= inrf[0, :]
indata[2, :]= inrf[3, :]
vpr.get_rf(indata = indata)
except KeyError:
print 'WARNING: No receiver function data for station: '+staid
#-----------------------------
# initial model parameters
#-----------------------------
vsdata = self.auxiliary_data['ReferenceModel'][staid_aux].data.value
crtthk = self.auxiliary_data['MohoDepth'][staid_aux].parameters['moho_depth']
sedthk = self.auxiliary_data['SediDepth'][staid_aux].parameters['sedi_depth']
vpr.model.isomod.parameterize_input(zarr=vsdata[:, 0], vsarr=vsdata[:, 1], crtthk=crtthk, sedthk=sedthk, maxdepth=200.)
vpr.getpara()
ista += 1
# if staid != 'AK.HDA': continue
# # # if np.random.rand() > 0.9:
# # # print staid
# # # return vpr, vsdata
# # # else:
# # # continue
print '--- Joint MC inversion for station: '+staid+' '+str(ista)+'/'+str(Nsta)
if parallel:
vpr.mc_joint_inv_iso_mp(outdir=outdir, dispdtype=dispdtype, wdisp=wdisp, rffactor=rffactor,\
monoc=monoc, pfx=staid, verbose=verbose, step4uwalk=step4uwalk, numbrun=numbrun, subsize=subsize, nprocess=nprocess)
else:
vpr.mc_joint_inv_iso(outdir=outdir, dispdtype=dispdtype, wdisp=wdisp, rffactor=rffactor,\
monoc=monoc, pfx=staid, verbose=verbose, step4uwalk=step4uwalk, numbrun=numbrun)
# vpr.mc_joint_inv_iso(outdir=outdir, wdisp=wdisp, rffactor=rffactor,\
# monoc=monoc, pfx=staid, verbose=verbose, step4uwalk=step4uwalk, numbrun=numbrun)
# if staid == 'AK.COLD':
# return vpr
return
