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https://github.com/wassname/simpeg.git
synced 2026-06-27 21:08:35 +08:00
working on IP
fix bug in RhoDeriv!!
This commit is contained in:
seogi_macbook
+1
-2
@@ -191,8 +191,7 @@ class BaseEMProblem(Problem.BaseProblem):
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dMfRhoI_dI = -self.MfRhoI**2
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dMf_drho = self.mesh.getFaceInnerProductDeriv(self.curModel.rho)(u)
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drho_dm = self.curModel.rhoDeriv
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return dMfRhoI_dI * ( dMf_drho * ( drho_dm))
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return dMfRhoI_dI * ( dMf_drho * (-Utils.sdiag(self.curModel.rho**2) * self.curModel.sigmaDeriv) )
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# return self.mesh.getFaceInnerProductDeriv(self.curModel.rho, invMat=True)(u) * self.curModel.rhoDeriv
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+121
-159
@@ -1,182 +1,144 @@
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from SimPEG import *
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from BaseDC import SurveyDC, FieldsDC_CC
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from SimPEG import Problem, Utils
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from SimPEG.EM.Base import BaseEMProblem
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from SurveyDC import Survey
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from FieldsDC import Fields, Fields_CC, Fields_N
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from SimPEG.Utils import sdiag
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import numpy as np
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from SimPEG.Utils import Zero
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from BoundaryUtils import getxBCyBC_CC
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class SurveyIP(SurveyDC):
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class IPPropMap(Maps.PropMap):
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"""
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**SurveyDC**
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Geophysical DC resistivity data.
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Property Map for IP Problems. The electrical chargeability,
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(\\(\\eta\\)) is the default inversion property
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"""
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def __init__(self, srcList, **kwargs):
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self.srcList = srcList
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Survey.BaseSurvey.__init__(self, **kwargs)
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self._Ps = {}
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def dpred(self, m, f=None):
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"""
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Predicted data.
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.. math::
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d_\\text{pred} = Pf(m)
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"""
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return self.prob.forward(m)
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eta = Maps.Property("Electrical Chargeability", defaultInvProp = True)
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sigma = Maps.Property("Electrical Conductivity", defaultInvProp = False, propertyLink=('rho',Maps.ReciprocalMap))
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rho = Maps.Property("Electrical Resistivity", propertyLink=('sigma', Maps.ReciprocalMap))
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class ProblemIP(BaseDCProblem):
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"""
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**ProblemIP**
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class BaseIPProblem(BaseEMProblem):
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Geophysical IP resistivity problem.
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"""
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surveyPair = SurveyDC
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Solver = Solver
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sigma = None
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surveyPair = Survey
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fieldsPair = Fields
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PropMap = IPPropMap
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Ainv = None
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u = None
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def __init__(self, mesh, **kwargs):
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Problem.BaseProblem.__init__(self, mesh)
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self.mesh.setCellGradBC('neumann')
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Utils.setKwargs(self, **kwargs)
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# deleteTheseOnModelUpdate = ['_A', '_Msig', '_dMdsig']
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@property
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def Msig(self):
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if getattr(self, '_Msig', None) is None:
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# sigma = self.curModel.transform
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sigma = self.sigma
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Av = self.mesh.aveF2CC
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self._Msig = Utils.sdiag(1/(self.mesh.dim * Av.T * (1/sigma)))
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return self._Msig
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@property
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def dMdsig(self):
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if getattr(self, '_dMdsig', None) is None:
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# sigma = self.curModel.transform
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sigma = self.sigma
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Av = self.mesh.aveF2CC
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dMdprop = self.mesh.dim * Utils.sdiag(self.Msig.diagonal()**2) * Av.T * Utils.sdiag(1./sigma**2)
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self._dMdsig = lambda Gu: Utils.sdiag(Gu) * dMdprop
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return self._dMdsig
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@property
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def A(self):
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"""
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Makes the matrix A(m) for the DC resistivity problem.
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:param numpy.array m: model
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:rtype: scipy.csc_matrix
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:return: A(m)
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.. math::
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c(m,u) = A(m)u - q = G\\text{sdiag}(M(mT(m)))Du - q = 0
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Where M() is the mass matrix and mT is the model transform.
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"""
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if getattr(self, '_A', None) is None:
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D = self.mesh.faceDiv
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G = self.mesh.cellGrad
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self._A = D*self.Msig*G
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# Remove the null space from the matrix.
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self._A[-1,-1] /= self.mesh.vol[-1]
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self._A = self._A.tocsc()
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return self._A
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def getRHS(self):
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# if self.mesh not in self._rhsDict:
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RHS = np.array([src.eval(self) for src in self.survey.srcList]).T
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# self._rhsDict[mesh] = RHS
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# return self._rhsDict[mesh]
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return RHS
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f = None
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def fields(self, m):
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if self.u is None:
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A = self.A
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if self.Ainv == None:
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self.Ainv = self.Solver(A, **self.solverOpts)
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Q = self.getRHS()
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self.u = self.Ainv * Q
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return self.u
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def forward(self, m, u=None):
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# Set current model; clear dependent property $\mathbf{A(m)}$
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self.curModel = m
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# sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
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sigma = self.sigma
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if self.u is None:
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# Run forward simulation if $u$ not provided
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u = self.fields(sigma)
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shp = (self.mesh.nC, self.survey.nSrc)
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u = self.u.reshape(shp, order='F')
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D = self.mesh.faceDiv
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G = self.mesh.cellGrad
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# Derivative of model transform, $\deriv{\sigma}{\m}$
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# dsigdm_x_v = self.curModel.transformDeriv * v
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dsigdm_x_v = Utils.sdiag(sigma) * self.curModel.transformDeriv * m
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# Take derivative of $C(m,u)$ w.r.t. $m$
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dCdm_x_v = np.empty_like(u)
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# loop over fields for each source
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for i in range(self.survey.nSrc):
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# Derivative of inner product, $\left(\mathbf{M}_{1/\sigma}^f\right)^{-1}$
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dAdsig = D * self.dMdsig( G * u[:,i] )
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dCdm_x_v[:, i] = dAdsig * dsigdm_x_v
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# Take derivative of $C(m,u)$ w.r.t. $u$
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if self.Ainv == None:
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self.Ainv = self.Solver(A, **self.solverOpts)
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# dCdu = self.A
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# Solve for $\deriv{u}{m}$
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# dCdu_inv = self.Solver(dCdu, **self.solverOpts)
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P = self.survey.getP(self.mesh)
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J_x_v = - P * mkvc( self.Ainv * dCdm_x_v )
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return -J_x_v
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if self.f is None:
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f = self.fieldsPair(self.mesh, self.survey)
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if self.Ainv == None:
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A = self.getA()
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self.Ainv = self.Solver(A, **self.solverOpts)
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RHS = self.getRHS()
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u = self.Ainv * RHS
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Srcs = self.survey.srcList
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f[Srcs, self._solutionType] = u
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return f
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def Jvec(self, m, v, f=None):
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return self.forward(v)
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def Jtvec(self, m, v, f=None):
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if f is None:
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f = self.fields(m)
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self.curModel = m
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# sigma = self.curModel.transform # $\sigma = \mathcal{M}(\m)$
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sigma = self.sigma
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if self.u is None:
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u = self.fields(sigma)
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else:
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u = self.u
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shp = (self.mesh.nC, self.survey.nSrc)
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u = u.reshape(shp, order='F')
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P = self.survey.getP(self.mesh)
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PT_x_v = (P.T*v).reshape(shp, order='F')
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D = self.mesh.faceDiv
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G = self.mesh.cellGrad
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A = self.A
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mT_dm = Utils.sdiag(sigma)*self.mapping.deriv(m)
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# mT_dm = self.mapping.deriv(m)
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Jv = self.dataPair(self.survey) #same size as the data
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# dCdu = A.T
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# Ainv = self.Solver(dCdu, **self.solverOpts)
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# if self.Ainv == None:
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self.Ainv = self.Solver(A.T, **self.solverOpts)
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A = self.getA()
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w = self.Ainv * PT_x_v
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for src in self.survey.srcList:
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u_src = f[src, self._solutionType] # solution vector
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dA_dm_v = self.getADeriv(u_src, v)
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dRHS_dm_v = self.getRHSDeriv(src, v)
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du_dm_v = self.Ainv * ( - dA_dm_v + dRHS_dm_v )
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Jtv = 0
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for i, ui in enumerate(u.T): # loop over each column
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Jtv += self.dMdsig( G * ui ).T * ( D.T * w[:,i] )
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for rx in src.rxList:
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df_dmFun = getattr(f, '_%sDeriv'%rx.projField, None)
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df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
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Jv[src, rx] = rx.evalDeriv(src, self.mesh, f, df_dm_v)
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return Utils.mkvc(Jv)
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Jtv = - mT_dm.T * ( Jtv )
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return -Jtv
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def Jtvec(self, m, v, f=None):
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if f is None:
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f = self.fields(m)
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self.curModel = m
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# Ensure v is a data object.
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if not isinstance(v, self.dataPair):
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v = self.dataPair(self.survey, v)
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Jtv = np.zeros(m.size)
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AT = self.getA()
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for src in self.survey.srcList:
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u_src = f[src, self._solutionType]
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for rx in src.rxList:
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PTv = rx.evalDeriv(src, self.mesh, f, v[src, rx], adjoint=True) # wrt f, need possibility wrt m
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df_duTFun = getattr(f, '_%sDeriv'%rx.projField, None)
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df_duT, df_dmT = df_duTFun(src, None, PTv, adjoint=True)
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ATinvdf_duT = self.Ainv * df_duT
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dA_dmT = self.getADeriv(u_src, ATinvdf_duT, adjoint=True)
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dRHS_dmT = self.getRHSDeriv(src, ATinvdf_duT, adjoint=True)
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du_dmT = -dA_dmT + dRHS_dmT
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Jtv += df_dmT + du_dmT
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return Utils.mkvc(Jtv)
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def getSourceTerm(self):
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"""
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takes concept of source and turns it into a matrix
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"""
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"""
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Evaluates the sources, and puts them in matrix form
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:rtype: (numpy.ndarray, numpy.ndarray)
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:return: q (nC or nN, nSrc)
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"""
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Srcs = self.survey.srcList
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if self._formulation is 'EB':
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n = self.mesh.nN
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# return NotImplementedError
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elif self._formulation is 'HJ':
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n = self.mesh.nC
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q = np.zeros((n, len(Srcs)))
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for i, src in enumerate(Srcs):
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q[:,i] = src.eval(self)
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return q
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@property
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def deleteTheseOnModelUpdate(self):
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toDelete = []
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return toDelete
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# assume log rho or log cond
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def MfRhoIDeriv(self,u):
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"""
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Derivative of :code:`MfRhoI` with respect to the model.
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"""
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dMfRhoI_dI = -self.MfRhoI**2
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dMf_drho = self.mesh.getFaceInnerProductDeriv(self.curModel.rho)(u)
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drho_dlogrho = Utils.sdiag(self.curModel.rho)
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return dMfRhoI_dI * ( dMf_drho * ( drho_dlogrho))
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# TODO: This should take a vector
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def MeSigmaDeriv(self, u):
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"""
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Derivative of MeSigma with respect to the model
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"""
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dsigma_dlogsigma = Utils.sdiag(self.curModel.sigma)
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return self.mesh.getEdgeInnerProductDeriv(self.curModel.sigma)(u) * dsigma_dlogsigma
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