def lnpostfn_emcee(p):
if not np.all(p > space[0, :]):
return -np.inf
if not np.all(p < space[1, :]):
return -np.inf
T = disp_data[:, 0]
pvelo = disp_data[:, 1]
stdpvelo= disp_data[:, 2]
model = vmodel.model1d()
model.get_para_model(paraval=p)
model.isomod.mod2para()
ilvry = 2
nper = T.size
per = np.zeros(200, dtype=np.float64)
per[:nper] = T[:]
qsinv = 1./model.qs
# print model.qs
(ur0,ul0,cr0,cl0) = fast_surf.fast_surf(model.nlay, ilvry, \
model.vpv, model.vsv, model.rho, model.h, qsinv, per, nper)
pvelp = cr0[:nper]
gvelp = ur0[:nper]
# misfit
misfit = ((pvelo - pvelp)**2/stdpvelo**2).sum()
L = np.exp(-0.5 * np.sqrt(misfit))
return np.log(L)
def init_eigensolver(self, inmodel=None, T=None, Tmin=10., Tmax=50., dT=5.,
c=None, cmin=2500., cmax=5000., dc=50., nmodes=1, zmax=400., dz=1.):
#####
# initialize eigenfunction solver
#####
if isinstance(inmodel, vmodel.model1d):
eigensolver = eigen.eigen_solver(inmodel)
else:
m=vmodel.model1d()
m.get_radius(zmax, dz)
m.model_prem()
eigensolver = eigen.eigen_solver(m)
if not isinstance(T, np.ndarray):
T = np.arange(Tmin, Tmax+dT, dT, dtype=np.float32)
if not isinstance(c, np.ndarray):
c = np.arange(cmin, cmax+dc, dc, dtype=np.float32)
rmin = np.float32(6371000. - zmax*1000.)
dr = np.float32(dz*1000.)
eigensolver.init_dbase(T, c, rmin, dr, np.int32(nmodes))
# if newsolver:
# self.eigensolver.init_dbase(T, c, rmin, dr, np.int32(nmodes))
# else:
# r = np.arange(rmin, 6371000.+dr, dr, dtype=np.float32)
# if r.size == self.eigensolver.r.size and T.size == self.eigensolver.T.size and \
# c.size == self.eigensolver.c.size and nmodes == self.eigensolver.nmodes:
# if not (np.allclose(r, self.eigensolver.r) and np.allclose(T, self.eigensolver.T) \
# and np.allclose(c, self.eigensolver.c) and nmodes == self.eigensolver.nmodes):
# self.eigensolver.init_dbase(T, c, rmin, dr, nmodes)
# else:
# self.eigensolver.init_dbase(T, c, rmin, dr, nmodes)
return eigensolver
def __init__(self):
self.model = vmodel.model1d()
self.indata = data.data1d()
self.eigkR = eigenkernel.eigkernel()
self.eigkL = eigenkernel.eigkernel()
self.hArr = np.array([], dtype=np.float32)
self.disprefR = False
self.disprefL = False
return
def __init__(self):
self.model = vmodel.model1d()
self.data = data.data1d()
self.fs = 40.
self.slowness = 0.06
self.gausswidth = 2.5
self.amplevel = 0.005
self.t0 = 0.
return
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
def logp_sampyl(x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13):
T = disp_data[:, 0]
pvelo = disp_data[:, 1]
stdpvelo= disp_data[:, 2]
model = vmodel.model1d()
model.get_para_model(paraval=np.array([x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13]))
model.isomod.mod2para()
ilvry = 2
nper = T.size
per = np.zeros(200, dtype=np.float64)
per[:nper] = T[:]
qsinv = 1./model.qs
# print model.qs
(ur0,ul0,cr0,cl0) = fast_surf.fast_surf(model.nlay, ilvry, \
model.vpv, model.vsv, model.rho, model.h, qsinv, per, nper)
pvelp = cr0[:nper]
gvelp = ur0[:nper]
# misfit
misfit = ((pvelo - pvelp)**2/stdpvelo**2).sum()
L = np.exp(-0.5 * misfit)
return L
def fitness(self, x):
T = disp_data[:, 0]
pvelo = disp_data[:, 1]
stdpvelo = disp_data[:, 2]
model = vmodel.model1d()
model.get_para_model(paraval=x)
model.isomod.mod2para()
ilvry = 2
nper = T.size
per = np.zeros(200, dtype=np.float64)
per[:nper] = T[:]
qsinv = 1./model.qs
(ur0,ul0,cr0,cl0) = fast_surf.fast_surf(model.nlay, ilvry, \
model.vpv, model.vsv, model.rho, model.h, qsinv, per, nper)
pvelp = cr0[:nper]
gvelp = ur0[:nper]
# misfit
misfit = ((pvelo - pvelp)**2/stdpvelo**2).sum()
# L = np.exp(-0.5 * np.sqrt(misfit))
# print 'run'
return [misfit]
m.add_perturb_layer_love(0, 20., 4, 0.2, True) (~ 0.15 %, ~ 0.25 % for Love group)
m.add_perturb_layer_love(0, 20., 4, -0.1, True); m.add_perturb_layer_love(0, 20., 3, -0.2, True) (~ 0.15 %, ~ 0.25 % for Love group)
m.add_perturb_layer_love(0, 20., 5, +-0.3, True) (~ 0.15 %, ~ 0.25 % for Love group)
Cases failed !
m.add_perturb_layer_love(0, 20., 3, -0.3, True) T = 5 sec, aniprop yields seemly wrong result. OTHERS are consistent ( ~ 0.05 %)
m.add_perturb_layer_love(0, 20., 4, 0.3, True), T = 5 sec, aniprop yields seemly wrong result. OTHERS are consistent ( ~ 0.05 %)
m.add_perturb_layer_love(0, 20., 4, -0.1, True); m.add_perturb_layer_love(0, 20., 3, -0.3, True) T = 5 sec, wrong results ( ~ 0.05 %)
"""
import eigen, tcps, aniproppy, ref
import vmodel
import numpy as np
import matplotlib.pyplot as plt
m1=vmodel.model1d()
m1.model_ak135_cps()
m1.flat=1
m1.add_perturb_layer(0, 20., 0, 3.494, False)
m1.add_perturb_layer(0, 20., 1, 3.702, False)
m1.add_perturb_layer(0, 20., 2, 5.94, False)
m1.add_perturb_layer(0, 20., 3, 6.28, False)
m1.add_perturb_layer(0, 20., 4, 0.82, False)
m1.add_perturb_layer(0, 20., 5, 2.73, False)
m1.init_tilt()
m1.dipArr[-1] = 34; m1.dipArr[-2] = 34
m1.strikeArr[-1] = 20; m1.strikeArr[-2] = 20
m.add_perturb_layer_love(0, 20., 4, 0.2, True) (~ 0.15 %, ~ 0.25 % for Love group)
m.add_perturb_layer_love(0, 20., 4, -0.1, True); m.add_perturb_layer_love(0, 20., 3, -0.2, True) (~ 0.15 %, ~ 0.25 % for Love group)
m.add_perturb_layer_love(0, 20., 5, +-0.3, True) (~ 0.15 %, ~ 0.25 % for Love group)
Cases failed !
m.add_perturb_layer_love(0, 20., 3, -0.3, True) T = 5 sec, aniprop yields seemly wrong result. OTHERS are consistent ( ~ 0.05 %)
m.add_perturb_layer_love(0, 20., 4, 0.3, True), T = 5 sec, aniprop yields seemly wrong result. OTHERS are consistent ( ~ 0.05 %)
m.add_perturb_layer_love(0, 20., 4, -0.1, True); m.add_perturb_layer_love(0, 20., 3, -0.3, True) T = 5 sec, wrong results ( ~ 0.05 %)
"""
import eigen, tcps, aniproppy
import vmodel
import numpy as np
import matplotlib.pyplot as plt
m=vmodel.model1d()
m.model_ak135_cps()
m.flat=1
#
# # model perturbation: isotropic -> VTI
#
# # m.add_perturb_layer_love(0, 20., 4, -0.1, True)
# # m.add_perturb_layer_love(0, 20., 3, -0.3, True)
#
# m.add_perturb_layer_love(0, 20., 0, -0.05, True)
# m.add_perturb_layer_love(0, 20., 1, 0.05, True)
# m.add_perturb_layer_love(0, 20., 3, -0.05, True)
# m.add_perturb_layer_love(0, 20., 4, 0.05, True)
m.add_perturb_layer_love(0, 35, 0, -0.02, True)
def __init__(self):
self.model = vmodel.model1d()
self.indata = data.data1d()
self.eigkR = eigenkernel.eigkernel()
self.eigkL = eigenkernel.eigkernel()
return
