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Jvec working for MT1D, Jtvec getting close

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GudniRos
2015-06-24 11:33:14 -07:00
parent 2cbfe2d6b9
commit f2a8cf0a62
5 changed files with 265 additions and 225 deletions
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simpegMT/BaseMT.py
+38 -39
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@@ -2,7 +2,8 @@ from simpegEM.FDEM import BaseFDEMProblem
from SurveyMT import SurveyMT
from DataMT import DataMT
from FieldsMT import FieldsMT
from SimPEG import SolverLU as SimpegSolver
from SimPEG import SolverLU as SimpegSolver, mkvc
import numpy as np
class BaseMTProblem(BaseFDEMProblem):
@@ -24,18 +25,32 @@ class BaseMTProblem(BaseFDEMProblem):
# Might need to add more stuff here.
def Jvec(self, m, v, f=None):
"""
Function to calculate the data sensitivities dD/dm times a vector.
:param numpy.ndarray (nC, 1) - conductive model
:param numpy.ndarray (nC, 1) - random vector
:param MTfields object (optional) - MT fields object, if not given it is calculated
:rtype: MTdata object
:return: Data sensitivities wrt m
"""
# Calculate the fields
if f is None:
f = self.fields(m)
# Set current model
self.curModel = m
# Initiate the Jv object
Jv = self.dataPair(self.survey)
# Loop all the frequenies
for freq in self.survey.freqs:
dA_du = self.getA(freq) #
dA_duI = self.Solver(dA_du, **self.solverOpts)
for src in self.survey.getSrcByFreq(freq):
# We need fDeriv_m = df/du*du/dm + df/dm
# Construct du/dm, it requires a solve
ftype = self._fieldType + 'Solution'
u_src = f[src, ftype]
dA_dm = self.getADeriv_m(freq, u_src, v)
@@ -44,28 +59,23 @@ class BaseMTProblem(BaseFDEMProblem):
du_dm = dA_duI * ( - dA_dm )
else:
du_dm = dA_duI * ( - dA_dm + dRHS_dm )
# Calculate the projection derivatives
for rx in src.rxList:
# df_duFun = u.deriv_u(rx.fieldsUsed, m)
if 'e' in self._fieldType:
projField = 'b'
elif 'b' in self._fieldType:
projField = 'e'
df_duFun = getattr(f, '_%sDeriv_u'%projField, None)
df_du = df_duFun(src, du_dm, adjoint=False)
if df_du is not None:
du_dm = df_du
# Get the stacked derivative
# df_duFun = getattr(f, '_fDeriv_u', None)
# df_dmFun = getattr(f, '_fDeriv_m', None)
# df_dm = df_dmFun(src,v,adjoint=False)
# if df_dm is None:
# fDeriv_m = df_duFun(src, du_dm, adjoint=False)
# else:
# fDeriv_m = df_duFun(src, du_dm, adjoint=False) + df_dm
# Not needed for now. Since PDeriv does this currently.
df_dmFun = getattr(f, '_%sDeriv_m'%projField, None)
df_dm = df_dmFun(src, v, adjoint=False)
if df_dm is not None:
du_dm += df_dm
P = lambda v: rx.projectFieldsDeriv(src, self.mesh, f, v) # wrt u, also have wrt m
Jv[src, rx] = P(du_dm)
return Utils.mkvc(Jv)
# Get the projection derivative
PDeriv = lambda v: rx.projectFieldsDeriv(src, self.mesh, f, v) # wrt u, also have wrt m
Jv[src, rx] = PDeriv(du_dm)
# Return the vectorized sensitivities
return mkvc(Jv)
def Jtvec(self, m, v, f=None):
if f is None:
@@ -88,29 +98,18 @@ class BaseMTProblem(BaseFDEMProblem):
u_src = f[src, ftype]
for rx in src.rxList:
# Get the adjoint projectFieldsDeriv
PTv = rx.projectFieldsDeriv(src, self.mesh, f, v[src, rx], adjoint=True) # wrt u, need possibility wrt m
df_duTFun = getattr(f, '_%sDeriv_u'%rx.projField, None)
df_duT = df_duTFun(src, PTv, adjoint=True)
if df_duT is not None:
dA_duIT = ATinv * df_duT
else:
dA_duIT = ATinv * PTv
# Get the
dA_duIT = ATinv * PTv
dA_dmT = self.getADeriv_m(freq, u_src, dA_duIT, adjoint=True)
dRHS_dmT = self.getRHSDeriv_m(src, dA_duIT, adjoint=True)
# Make du_dmT
if dRHS_dmT is None:
du_dmT = - dA_dmT
else:
du_dmT = -dA_dmT + dRHS_dmT
df_dmFun = getattr(f, '_%sDeriv_m'%rx.projField, None)
dfT_dm = df_dmFun(src, PTv, adjoint=True)
if dfT_dm is not None:
du_dmT += dfT_dm
# Select the correct component
real_or_imag = rx.projComp
if real_or_imag == 'real':
Jtv += du_dmT.real