def MfRhoIDeriv(self, u, v, adjoint=False):
"""
Derivative of :code:`MfRhoI` with respect to the model.
"""
dMfRhoI_dI = -self.MfRhoI**2
if self.storeInnerProduct:
if adjoint:
return (
self.MfRhoDerivMat.T * (
Utils.sdiag(u) * (dMfRhoI_dI.T * v)
)
)
else:
return dMfRhoI_dI * (Utils.sdiag(u) * (self.MfRhoDerivMat*v))
else:
if self.storeJ:
drho_dlogrho = Utils.sdiag(self.rho)*self.actMap.P
else:
drho_dlogrho = Utils.sdiag(self.rho)
dMf_drho = self.mesh.getFaceInnerProductDeriv(self.rho)(u)
if adjoint:
return drho_dlogrho.T * (dMf_drho.T * (dMfRhoI_dI.T*v))
else:
return dMfRhoI_dI * (dMf_drho * (drho_dlogrho*v))
def MeSigmaDeriv(self, u, v, adjoint=False):
"""
Derivative of MeSigma with respect to the model times a vector (u)
"""
if self.storeInnerProduct:
if adjoint:
return self.MeSigmaDerivMat.T * (Utils.sdiag(u)*v)
else:
return Utils.sdiag(u)*(self.MeSigmaDerivMat * v)
else:
if self.storeJ:
dsigma_dlogsigma = Utils.sdiag(self.sigma)*self.actMap.P
else:
dsigma_dlogsigma = Utils.sdiag(self.sigma)
if adjoint:
return (
dsigma_dlogsigma.T * (
self.mesh.getEdgeInnerProductDeriv(self.sigma)(u).T * v
)
)
else:
return (
self.mesh.getEdgeInnerProductDeriv(self.sigma)(u) *
(dsigma_dlogsigma * v)
)
def Wd(self):
"""getWd(survey)
The data weighting matrix.
The default is based on the norm of the data plus a noise floor.
:rtype: scipy.sparse.csr_matrix
:return: Wd
"""
if getattr(self, '_Wd', None) is None:
survey = self.survey
if getattr(survey,'std', None) is None:
print('SimPEG.DataMisfit.l2_DataMisfit assigning default std of 5%')
survey.std = 0.05
if getattr(survey, 'eps', None) is None:
print('SimPEG.DataMisfit.l2_DataMisfit assigning default eps of 1e-5 * ||dobs||')
survey.eps = np.linalg.norm(Utils.mkvc(survey.dobs),2)*1e-5
self._Wd = Utils.sdiag(1/(abs(survey.dobs)*survey.std+survey.eps))
return self._Wd
def getInterpolationMatCartMesh(self, Mrect, locType="CC", locTypeTo=None):
"""
Takes a cartesian mesh and returns a projection to translate onto
the cartesian grid.
"""
assert self.isSymmetric, (
"Currently we have not taken into account " "other projections for more complicated " "CylMeshes"
)
if locTypeTo is None:
locTypeTo = locType
if locType == "F":
# do this three times for each component
X = self.getInterpolationMatCartMesh(Mrect, locType="Fx", locTypeTo=locTypeTo + "x")
Y = self.getInterpolationMatCartMesh(Mrect, locType="Fy", locTypeTo=locTypeTo + "y")
Z = self.getInterpolationMatCartMesh(Mrect, locType="Fz", locTypeTo=locTypeTo + "z")
return sp.vstack((X, Y, Z))
if locType == "E":
X = self.getInterpolationMatCartMesh(Mrect, locType="Ex", locTypeTo=locTypeTo + "x")
Y = self.getInterpolationMatCartMesh(Mrect, locType="Ey", locTypeTo=locTypeTo + "y")
Z = Utils.spzeros(getattr(Mrect, "n" + locTypeTo + "z"), self.nE)
return sp.vstack((X, Y, Z))
grid = getattr(Mrect, "grid" + locTypeTo)
# This is unit circle stuff, 0 to 2*pi, starting at x-axis, rotating
# counter clockwise in an x-y slice
theta = -np.arctan2(grid[:, 0] - self.cartesianOrigin[0], grid[:, 1] - self.cartesianOrigin[1]) + np.pi / 2
theta[theta < 0] += np.pi * 2.0
r = ((grid[:, 0] - self.cartesianOrigin[0]) ** 2 + (grid[:, 1] - self.cartesianOrigin[1]) ** 2) ** 0.5
if locType in ["CC", "N", "Fz", "Ez"]:
G, proj = np.c_[r, theta, grid[:, 2]], np.ones(r.size)
else:
dotMe = {
"Fx": Mrect.normals[: Mrect.nFx, :],
"Fy": Mrect.normals[Mrect.nFx : (Mrect.nFx + Mrect.nFy), :],
"Fz": Mrect.normals[-Mrect.nFz :, :],
"Ex": Mrect.tangents[: Mrect.nEx, :],
"Ey": Mrect.tangents[Mrect.nEx : (Mrect.nEx + Mrect.nEy), :],
"Ez": Mrect.tangents[-Mrect.nEz :, :],
}[locTypeTo]
if "F" in locType:
normals = np.c_[np.cos(theta), np.sin(theta), np.zeros(theta.size)]
proj = (normals * dotMe).sum(axis=1)
if "E" in locType:
tangents = np.c_[-np.sin(theta), np.cos(theta), np.zeros(theta.size)]
proj = (tangents * dotMe).sum(axis=1)
G = np.c_[r, theta, grid[:, 2]]
interpType = locType
if interpType == "Fy":
interpType = "Fx"
elif interpType == "Ex":
interpType = "Ey"
Pc2r = self.getInterpolationMat(G, interpType)
Proj = Utils.sdiag(proj)
return Proj * Pc2r
def setUp(self):
cs = 25.
hx = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
hy = [(cs,7, -1.3),(cs,21),(cs,7, 1.3)]
hz = [(cs,7, -1.3),(cs,20)]
mesh = Mesh.TensorMesh([hx, hy, hz],x0="CCN")
sigma = np.ones(mesh.nC)*1e-2
x = mesh.vectorCCx[(mesh.vectorCCx>-155.)&(mesh.vectorCCx<155.)]
y = mesh.vectorCCx[(mesh.vectorCCy>-155.)&(mesh.vectorCCy<155.)]
Aloc = np.r_[-200., 0., 0.]
Bloc = np.r_[200., 0., 0.]
M = Utils.ndgrid(x-25.,y, np.r_[0.])
N = Utils.ndgrid(x+25.,y, np.r_[0.])
phiA = EM.Analytics.DCAnalyticHalf(Aloc, [M,N], 1e-2, earth_type="halfspace")
phiB = EM.Analytics.DCAnalyticHalf(Bloc, [M,N], 1e-2, earth_type="halfspace")
data_anal = phiA-phiB
rx = DC.Rx.Dipole(M, N)
src = DC.Src.Dipole([rx], Aloc, Bloc)
survey = DC.Survey([src])
self.survey = survey
self.mesh = mesh
self.sigma = sigma
self.data_anal = data_anal
try:
from pymatsolver import PardisoSolver
self.Solver = PardisoSolver
except ImportError:
self.Solver = SolverLU
def test_regularizationMesh(self):
for i, mesh in enumerate(self.meshlist):
print('Testing {0:d}D'.format(mesh.dim))
# mapping = r.mapPair(mesh)
# reg = r(mesh, mapping=mapping)
# m = np.random.rand(mapping.nP)
if mesh.dim == 1:
indAct = Utils.mkvc(mesh.gridCC <= 0.8)
elif mesh.dim == 2:
indAct = (
Utils.mkvc(
mesh.gridCC[:,-1] <=
2*np.sin(2*np.pi*mesh.gridCC[:, 0]) + 0.5
)
)
elif mesh.dim == 3:
indAct = (
Utils.mkvc(
mesh.gridCC[:, -1] <=
2*np.sin(2*np.pi*mesh.gridCC[:, 0]) +
0.5 * 2*np.sin(2*np.pi*mesh.gridCC[:, 1]) + 0.5
)
)
regmesh = Regularization.RegularizationMesh(
mesh, indActive=indAct
)
assert (regmesh.vol == mesh.vol[indAct]).all()
def meshs(self):
if getattr(self, '_meshs', None) is None:
csx, ncx, npadx = 50, 21, 12
csy, ncy, npady = 50, 21, 12
csz, ncz, npadz = 25, 40, 14
pf = 1.5
hx = Utils.meshTensor(
[(csx, npadx, -pf), (csx, ncx), (csx, npadx, pf)]
)
hy = Utils.meshTensor(
[(csy, npady, -pf), (csy, ncy), (csy, npady, pf)]
)
hz = Utils.meshTensor(
[(csz, npadz, -pf), (csz, ncz), (csz, npadz, pf)]
)
x0 = np.r_[-hx.sum()/2., -hy.sum()/2., -hz[:npadz+ncz].sum()]
self._meshs = Mesh.TensorMesh([hx, hy, hz], x0=x0)
print('Secondary Mesh ... ')
print(
' xmin, xmax, zmin, zmax: ', self._meshs.vectorCCx.min(),
self._meshs.vectorCCx.max(), self._meshs.vectorCCy.min(),
self._meshs.vectorCCy.max(), self._meshs.vectorCCz.min(),
self._meshs.vectorCCz.max()
)
print(' nC, vnC', self._meshs.nC, self._meshs.vnC)
return self._meshs
def __init__(self, rxList, freq, loc, orientation="Z", moment=1.0, mu=mu_0, **kwargs):
self.freq = float(freq)
self.loc = loc
if isinstance(orientation, str):
assert orientation.upper() in ["X", "Y", "Z"], (
"orientation must " "be in 'X', 'Y', " "'Z' not {}".format(orientation)
)
orientation = orientationDict[orientation.upper()]
elif (
np.linalg.norm(orientation - np.r_[1.0, 0.0, 0.0]) > 1e-6
or np.linalg.norm(orientation - np.r_[0.0, 1.0, 0.0]) > 1e-6
or np.linalg.norm(orientation - np.r_[0.0, 0.0, 1.0]) > 1e-6
):
warnings.warn(
"Using orientations that are not in aligned with"
" the mesh axes is not thoroughly tested. PR on "
"a test??"
)
assert np.linalg.norm(orientation) == 1.0, "Orientation must have unit" " length, not {}".format(
np.linalg.norm(orientation)
)
self.orientation = orientation
self.moment = moment
self.mu = mu
Utils.setKwargs(self, **kwargs)
BaseSrc.__init__(self, rxList)
def evalDeriv(self, f, freq, P0, df_dm_v=None, v=None, adjoint=False):
if adjoint:
if self.component == "real":
PTvr = (P0.T*v).astype(complex)
dZr_dfT_v = Utils.sdiag((1./(f**2)))*PTvr
return dZr_dfT_v
elif self.component == "imag":
PTvi = P0.T*v*-1j
dZi_dfT_v = Utils.sdiag((1./(f**2)))*PTvi
return dZi_dfT_v
elif self.component == "both":
PTvr = (P0.T*np.r_[v[0]]).astype(complex)
PTvi = P0.T*np.r_[v[1]]*-1j
dZr_dfT_v = Utils.sdiag((1./(f**2)))*PTvr
dZi_dfT_v = Utils.sdiag((1./(f**2)))*PTvi
return dZr_dfT_v + dZi_dfT_v
else:
raise NotImplementedError('must be real, imag or both')
else:
dZd_dm_v = P0 * (Utils.sdiag(1./(f**2)) * df_dm_v)
if self.component == "real":
return dZd_dm_v.real
elif self.component == "imag":
return dZd_dm_v.imag
elif self.component == "both":
return np.r_[dZd_dm_v.real, dZd_dm_v.imag]
else:
raise NotImplementedError('must be real, imag or both')
def test_getInterpMatCartMesh_Edges2Faces(self):
Mr = Mesh.TensorMesh([100,100,2], x0='CC0')
Mc = Mesh.CylMesh([np.ones(10)/5,1,10],x0='0C0',cartesianOrigin=[-0.2,-0.2,0])
Pe2f = Mc.getInterpolationMatCartMesh(Mr, 'E', locTypeTo='F')
me = np.ones(Mc.nE)
frect = Pe2f * me
excc = Mr.aveFx2CC*Mr.r(frect, 'F', 'Fx')
eycc = Mr.aveFy2CC*Mr.r(frect, 'F', 'Fy')
ezcc = Mr.r(frect, 'F', 'Fz')
indX = Utils.closestPoints(Mr, [0.45, -0.2, 0.5])
indY = Utils.closestPoints(Mr, [-0.2, 0.45, 0.5])
TOL = 1e-2
assert np.abs(float(excc[indX]) - 0) < TOL
assert np.abs(float(excc[indY]) + 1) < TOL
assert np.abs(float(eycc[indX]) - 1) < TOL
assert np.abs(float(eycc[indY]) - 0) < TOL
assert np.abs(ezcc.sum()) < TOL
mag = (excc**2 + eycc**2)**0.5
dist = ((Mr.gridCC[:,0] + 0.2)**2 + (Mr.gridCC[:,1] + 0.2)**2)**0.5
assert np.abs(mag[dist > 0.1].max() - 1) < TOL
assert np.abs(mag[dist > 0.1].min() - 1) < TOL
def test_regularization_ActiveCells(self):
for R in dir(Regularization):
r = getattr(Regularization, R)
if not inspect.isclass(r):
continue
if not issubclass(r, Regularization.BaseRegularization):
continue
for i, mesh in enumerate(self.meshlist):
print('Testing Active Cells {0:d}D'.format((mesh.dim)))
if mesh.dim == 1:
indActive = Utils.mkvc(mesh.gridCC <= 0.8)
elif mesh.dim == 2:
indActive = Utils.mkvc(mesh.gridCC[:,-1] <= 2*np.sin(2*np.pi*mesh.gridCC[:,0])+0.5)
elif mesh.dim == 3:
indActive = Utils.mkvc(mesh.gridCC[:,-1] <= 2*np.sin(2*np.pi*mesh.gridCC[:,0])+0.5 * 2*np.sin(2*np.pi*mesh.gridCC[:,1])+0.5)
for indAct in [indActive, indActive.nonzero()[0]]: # test both bool and integers
reg = r(mesh, indActive=indAct)
m = np.random.rand(mesh.nC)[indAct]
reg.mref = np.ones_like(m)*np.mean(m)
print('Check: phi_m (mref) = {0:f}'.format(reg.eval(reg.mref)))
passed = reg.eval(reg.mref) < TOL
self.assertTrue(passed)
print('Check:', R)
passed = Tests.checkDerivative(lambda m : [reg.eval(m), reg.evalDeriv(m)], m, plotIt=False)
self.assertTrue(passed)
print('Check 2 Deriv:', R)
passed = Tests.checkDerivative(lambda m : [reg.evalDeriv(m), reg.eval2Deriv(m)], m, plotIt=False)
self.assertTrue(passed)
def test_getInterpMatCartMesh_Faces2Edges(self):
Mr = Mesh.TensorMesh([100,100,2], x0='CC0')
Mc = Mesh.CylMesh([np.ones(10)/5,1,10],x0='0C0',cartesianOrigin=[-0.2,-0.2,0])
Pf2e = Mc.getInterpolationMatCartMesh(Mr, 'F', locTypeTo='E')
mf = np.ones(Mc.nF)
ecart = Pf2e * mf
excc = Mr.aveEx2CC*Mr.r(ecart, 'E', 'Ex')
eycc = Mr.aveEy2CC*Mr.r(ecart, 'E', 'Ey')
ezcc = Mr.r(ecart, 'E', 'Ez')
indX = Utils.closestPoints(Mr, [0.45, -0.2, 0.5])
indY = Utils.closestPoints(Mr, [-0.2, 0.45, 0.5])
TOL = 1e-2
assert np.abs(float(excc[indX]) - 1) < TOL
assert np.abs(float(excc[indY]) - 0) < TOL
assert np.abs(float(eycc[indX]) - 0) < TOL
assert np.abs(float(eycc[indY]) - 1) < TOL
assert np.abs((ezcc - 1).sum()) < TOL
mag = (excc**2 + eycc**2)**0.5
dist = ((Mr.gridCC[:,0] + 0.2)**2 + (Mr.gridCC[:,1] + 0.2)**2)**0.5
assert np.abs(mag[dist > 0.1].max() - 1) < TOL
assert np.abs(mag[dist > 0.1].min() - 1) < TOL
def test_getInterpMatCartMesh_Faces(self):
Mr = Mesh.TensorMesh([100,100,2], x0='CC0')
Mc = Mesh.CylMesh([np.ones(10)/5,1,10],x0='0C0',cartesianOrigin=[-0.2,-0.2,0])
Pf = Mc.getInterpolationMatCartMesh(Mr, 'F')
mf = np.ones(Mc.nF)
frect = Pf * mf
fxcc = Mr.aveFx2CC*Mr.r(frect, 'F', 'Fx')
fycc = Mr.aveFy2CC*Mr.r(frect, 'F', 'Fy')
fzcc = Mr.r(frect, 'F', 'Fz')
indX = Utils.closestPoints(Mr, [0.45, -0.2, 0.5])
indY = Utils.closestPoints(Mr, [-0.2, 0.45, 0.5])
TOL = 1e-2
assert np.abs(float(fxcc[indX]) - 1) < TOL
assert np.abs(float(fxcc[indY]) - 0) < TOL
assert np.abs(float(fycc[indX]) - 0) < TOL
assert np.abs(float(fycc[indY]) - 1) < TOL
assert np.abs((fzcc - 1).sum()) < TOL
mag = (fxcc**2 + fycc**2)**0.5
dist = ((Mr.gridCC[:,0] + 0.2)**2 + (Mr.gridCC[:,1] + 0.2)**2)**0.5
assert np.abs(mag[dist > 0.1].max() - 1) < TOL
assert np.abs(mag[dist > 0.1].min() - 1) < TOL
def setUp(self):
cs = 12.5
hx = [(cs, 7, -1.3), (cs, 61), (cs, 7, 1.3)]
hy = [(cs, 7, -1.3), (cs, 20)]
mesh = Mesh.TensorMesh([hx, hy], x0="CN")
sighalf = 1e-2
sigma = np.ones(mesh.nC)*sighalf
x = np.linspace(0, 250., 20)
M = Utils.ndgrid(x-12.5, np.r_[0.])
N = Utils.ndgrid(x+12.5, np.r_[0.])
A0loc = np.r_[-150, 0.]
A1loc = np.r_[-125, 0.]
rxloc = np.c_[M, np.zeros(20)]
data_ana = EM.Analytics.DCAnalytic_Dipole_Pole(
[np.r_[A0loc, 0.], np.r_[A1loc, 0.]],
rxloc, sighalf, earth_type="halfspace")
rx = DC.Rx.Pole_ky(M)
src0 = DC.Src.Dipole([rx], A0loc, A1loc)
survey = DC.Survey_ky([src0])
self.survey = survey
self.mesh = mesh
self.sigma = sigma
self.data_ana = data_ana
self.plotIt = False
try:
from pymatsolver import PardisoSolver
self.Solver = PardisoSolver
except ImportError:
self.Solver = SolverLU
def test_updateMultipliers(self):
nP = 10
m = np.random.rand(nP)
W1 = Utils.sdiag(np.random.rand(nP))
W2 = Utils.sdiag(np.random.rand(nP))
phi1 = ObjectiveFunction.L2ObjectiveFunction(W=W1)
phi2 = ObjectiveFunction.L2ObjectiveFunction(W=W2)
phi = phi1 + phi2
self.assertTrue(phi(m) == phi1(m) + phi2(m))
phi.multipliers[0] = Utils.Zero()
self.assertTrue(phi(m) == phi2(m))
phi.multipliers[0] = 1.
phi.multipliers[1] = Utils.Zero()
self.assertTrue(len(phi.objfcts) == 2)
self.assertTrue(len(phi.multipliers) == 2)
self.assertTrue(len(phi) == 2)
self.assertTrue(phi(m) == phi1(m))
def drapeTopotoLoc(mesh, pts, actind=None, option="top", topo=None):
"""
Drape location right below (cell center) the topography
"""
if mesh.dim == 2:
if pts.ndim > 1:
raise Exception("pts should be 1d array")
elif mesh.dim == 3:
if pts.shape[1] == 3:
raise Exception("shape of pts should be (x,3)")
else:
raise NotImplementedError()
if actind is None:
actind = Utils.surface2ind_topo(mesh, topo)
if mesh._meshType == "TENSOR":
meshtemp, topoCC = gettopoCC(mesh, actind, option=option)
inds = Utils.closestPoints(meshtemp, pts)
elif mesh._meshType == "TREE":
if mesh.dim == 3:
uniqXYlocs, topoCC = gettopoCC(mesh, actind, option=option)
inds = closestPointsGrid(uniqXYlocs, pts)
else:
raise NotImplementedError()
else:
raise NotImplementedError()
out = np.c_[pts, topoCC[inds]]
return out
def forward(self, m, f=None):
if f is None:
f = self.fields(m)
self.curModel = m
Jv = self.dataPair(self.survey) #same size as the data
# A = self.getA()
JvAll = []
for tind in range(len(self.survey.times)):
#Pseudo-chareability
t = self.survey.times[tind]
v = self.DebyeTime(t)
for src in self.survey.srcList:
u_src = f[src, self._solutionType] # solution vector
dA_dm_v = self.getADeriv(u_src, v)
dRHS_dm_v = self.getRHSDeriv(src, v)
du_dm_v = self.Ainv * ( - dA_dm_v + dRHS_dm_v )
for rx in src.rxList:
timeindex = rx.getTimeP(self.survey.times)
if timeindex[tind]:
df_dmFun = getattr(f, '_{0!s}Deriv'.format(rx.projField), None)
df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
Jv[src, rx, t] = rx.evalDeriv(src, self.mesh, f, df_dm_v)
# Conductivity (d u / d log sigma)
if self._formulation is 'EB':
return -Utils.mkvc(Jv)
# Resistivity (d u / d log rho)
if self._formulation is 'HJ':
return Utils.mkvc(Jv)
def Jfull(self, m=None, f=None):
if f is None:
f = self.fields(m)
nn = len(f)-1
Asubs, Adiags, Bs = list(range(nn)), list(range(nn)), list(range(nn))
for ii in range(nn):
dt = self.timeSteps[ii]
bc = self.getBoundaryConditions(ii, f[ii])
Asubs[ii], Adiags[ii], Bs[ii] = self.diagsJacobian(
m, f[ii], f[ii+1], dt, bc
)
Ad = sp.block_diag(Adiags)
zRight = Utils.spzeros(
(len(Asubs)-1)*Asubs[0].shape[0], Adiags[0].shape[1]
)
zTop = Utils.spzeros(
Adiags[0].shape[0], len(Adiags)*Adiags[0].shape[1]
)
As = sp.vstack((zTop, sp.hstack((sp.block_diag(Asubs[1:]), zRight))))
A = As + Ad
B = np.array(sp.vstack(Bs).todense())
Ainv = self.Solver(A, **self.solverOpts)
AinvB = Ainv * B
z = np.zeros((self.mesh.nC, B.shape[1]))
du_dm = np.vstack((z, AinvB))
J = self.survey.deriv(f, du_dm_v=du_dm) # not multiplied by v
return J
def Jvec(self, m, v, f=None):
if f is None:
f = self.fields(m)
self.curModel = m
Jv = self.dataPair(self.survey) #same size as the data
A = self.getA()
for src in self.survey.srcList:
u_src = f[src, self._solutionType] # solution vector
dA_dm_v = self.getADeriv(u_src, v)
dRHS_dm_v = self.getRHSDeriv(src, v)
du_dm_v = self.Ainv * ( - dA_dm_v + dRHS_dm_v )
for rx in src.rxList:
df_dmFun = getattr(f, '_{0!s}Deriv'.format(rx.projField), None)
df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
Jv[src, rx] = rx.evalDeriv(src, self.mesh, f, df_dm_v)
# Conductivity (d u / d log sigma)
if self._formulation is 'EB':
return -Utils.mkvc(Jv)
# Conductivity (d u / d log rho)
if self._formulation is 'HJ':
return Utils.mkvc(Jv)
def setUp(self):
cs = 25.
hx = [(cs, 7, -1.3), (cs, 21), (cs, 7, 1.3)]
hy = [(cs, 7, -1.3), (cs, 21), (cs, 7, 1.3)]
hz = [(cs, 7, -1.3), (cs, 20), (cs, 7, -1.3)]
mesh = Mesh.TensorMesh([hx, hy, hz], x0="CCC")
sigma = np.ones(mesh.nC)*1e-2
x = mesh.vectorCCx[(mesh.vectorCCx > -155.) & (mesh.vectorCCx < 155.)]
y = mesh.vectorCCx[(mesh.vectorCCy > -155.) & (mesh.vectorCCy < 155.)]
Aloc = np.r_[-200., 0., 0.]
Bloc = np.r_[200., 0., 0.]
M = Utils.ndgrid(x-25., y, np.r_[0.])
N = Utils.ndgrid(x+25., y, np.r_[0.])
phiA = EM.Analytics.DCAnalytic_Pole_Dipole(
Aloc, [M, N], 1e-2, earth_type="wholespace"
)
phiB = EM.Analytics.DCAnalytic_Pole_Dipole(
Bloc, [M, N], 1e-2, earth_type="wholespace"
)
data_anal = phiA-phiB
rx = DC.Rx.Dipole(M, N)
src = DC.Src.Dipole([rx], Aloc, Bloc)
survey = DC.Survey([src])
self.survey = survey
self.mesh = mesh
self.sigma = sigma
self.data_anal = data_anal
def Jtvec(self, m, v, f=None):
if f is None:
f = self.fields(m)
self.model = m
# Ensure v is a data object.
if not isinstance(v, self.dataPair):
v = self.dataPair(self.survey, v)
Jtv = np.zeros(m.size)
AT = self.getA()
for src in self.survey.srcList:
u_src = f[src, self._solutionType]
for rx in src.rxList:
PTv = rx.evalDeriv(src, self.mesh, f, v[src, rx], adjoint=True) # wrt f, need possibility wrt m
df_duTFun = getattr(f, '_{0!s}Deriv'.format(rx.projField), None)
df_duT, df_dmT = df_duTFun(src, None, PTv, adjoint=True)
ATinvdf_duT = self.Ainv * df_duT
dA_dmT = self.getADeriv(u_src, ATinvdf_duT, adjoint=True)
dRHS_dmT = self.getRHSDeriv(src, ATinvdf_duT, adjoint=True)
du_dmT = -dA_dmT + dRHS_dmT
Jtv += (df_dmT + du_dmT).astype(float)
# Conductivity ((d u / d log sigma).T)
if self._formulation == 'EB':
return -Utils.mkvc(Jtv)
# Conductivity ((d u / d log rho).T)
if self._formulation == 'HJ':
return Utils.mkvc(Jtv)
def setUp(self):
cs = 12.5
npad = 2
hx = [(cs, npad, -1.3), (cs, 21), (cs, npad, 1.3)]
hy = [(cs, npad, -1.3), (cs, 21), (cs, npad, 1.3)]
hz = [(cs, npad, -1.3), (cs, 20)]
mesh = Mesh.TensorMesh([hx, hy, hz], x0="CCN")
x = mesh.vectorCCx[(mesh.vectorCCx > -80.) & (mesh.vectorCCx < 80.)]
y = mesh.vectorCCx[(mesh.vectorCCy > -80.) & (mesh.vectorCCy < 80.)]
Aloc = np.r_[-100., 0., 0.]
Bloc = np.r_[100., 0., 0.]
M = Utils.ndgrid(x-12.5, y, np.r_[0.])
N = Utils.ndgrid(x+12.5, y, np.r_[0.])
radius = 50.
xc = np.r_[0., 0., -100]
blkind = Utils.ModelBuilder.getIndicesSphere(xc, radius, mesh.gridCC)
sigmaInf = np.ones(mesh.nC)*1e-2
eta = np.zeros(mesh.nC)
eta[blkind] = 0.1
sigma0 = sigmaInf*(1.-eta)
rx = DC.Rx.Dipole(M, N)
src = DC.Src.Dipole([rx], Aloc, Bloc)
surveyDC = DC.Survey([src])
self.surveyDC = surveyDC
self.mesh = mesh
self.sigmaInf = sigmaInf
self.sigma0 = sigma0
self.src = src
self.eta = eta
def run(plotIt=True):
M = Mesh.TensorMesh([100, 100])
h1 = Utils.meshTensor([(6, 7, -1.5), (6, 10), (6, 7, 1.5)])
h1 = h1/h1.sum()
M2 = Mesh.TensorMesh([h1, h1])
V = Utils.ModelBuilder.randomModel(M.vnC, seed=79, its=50)
v = Utils.mkvc(V)
modh = Maps.Mesh2Mesh([M, M2])
modH = Maps.Mesh2Mesh([M2, M])
H = modH * v
h = modh * H
if not plotIt:
return
ax = plt.subplot(131)
M.plotImage(v, ax=ax)
ax.set_title('Fine Mesh (Original)')
ax = plt.subplot(132)
M2.plotImage(H, clim=[0, 1], ax=ax)
ax.set_title('Course Mesh')
ax = plt.subplot(133)
M.plotImage(h, clim=[0, 1], ax=ax)
ax.set_title('Fine Mesh (Interpolated)')
def test_ana_forward(self):
survey = PF.BaseMag.BaseMagSurvey()
Inc = 45.0
Dec = 45.0
Btot = 51000
b0 = PF.MagAnalytics.IDTtoxyz(Inc, Dec, Btot)
survey.setBackgroundField(Inc, Dec, Btot)
xr = np.linspace(-300, 300, 41)
yr = np.linspace(-300, 300, 41)
X, Y = np.meshgrid(xr, yr)
Z = np.ones((xr.size, yr.size)) * 150
rxLoc = np.c_[Utils.mkvc(X), Utils.mkvc(Y), Utils.mkvc(Z)]
survey.rxLoc = rxLoc
self.prob.pair(survey)
u = self.prob.fields(self.chi)
B = u["B"]
bxa, bya, bza = PF.MagAnalytics.MagSphereAnaFunA(
rxLoc[:, 0], rxLoc[:, 1], rxLoc[:, 2], 100.0, 0.0, 0.0, 0.0, 0.01, b0, "secondary"
)
dpred = survey.projectFieldsAsVector(B)
err = np.linalg.norm(dpred - np.r_[bxa, bya, bza]) / np.linalg.norm(np.r_[bxa, bya, bza])
self.assertTrue(err < 0.08)
def set_ij_height(self):
"""
Compute (I, J) indicies to form sparse sensitivity matrix
This will be used in GlobalEM1DProblem when after sensitivity matrix
for each sounding is computed
"""
I = []
J = []
shift_for_J = 0
shift_for_I = 0
m = self.n_layer
for i in range(n_sounding):
n = self.nD_vec[i]
J_temp = np.tile(np.arange(m), (n, 1)) + shift_for_J
I_temp = (
np.tile(np.arange(n), (1, m)).reshape((n, m), order='F') +
shift_for_I
)
J.append(Utils.mkvc(J_temp))
I.append(Utils.mkvc(I_temp))
shift_for_J += m
shift_for_I = I_temp[-1, -1] + 1
J = np.hstack(J).astype(int)
I = np.hstack(I).astype(int)
return (I, J)
def setUp(self):
cs = 25.
hx = [(cs, 0, -1.3), (cs, 21), (cs, 0, 1.3)]
hy = [(cs, 0, -1.3), (cs, 21), (cs, 0, 1.3)]
hz = [(cs, 0, -1.3), (cs, 20)]
mesh = Mesh.TensorMesh([hx, hy, hz], x0="CCN")
blkind0 = Utils.ModelBuilder.getIndicesSphere(
np.r_[-100., -100., -200.], 75., mesh.gridCC
)
blkind1 = Utils.ModelBuilder.getIndicesSphere(
np.r_[100., 100., -200.], 75., mesh.gridCC
)
sigma = np.ones(mesh.nC)*1e-2
eta = np.zeros(mesh.nC)
tau = np.ones_like(sigma)*1.
eta[blkind0] = 0.1
eta[blkind1] = 0.1
tau[blkind0] = 0.1
tau[blkind1] = 0.01
x = mesh.vectorCCx[(mesh.vectorCCx > -155.) & (mesh.vectorCCx < 155.)]
y = mesh.vectorCCx[(mesh.vectorCCy > -155.) & (mesh.vectorCCy < 155.)]
Aloc = np.r_[-200., 0., 0.]
Bloc = np.r_[200., 0., 0.]
M = Utils.ndgrid(x-25., y, np.r_[0.])
N = Utils.ndgrid(x+25., y, np.r_[0.])
times = np.arange(10)*1e-3 + 1e-3
rx = SIP.Rx.Dipole(M, N, times)
src = SIP.Src.Dipole([rx], Aloc, Bloc)
survey = SIP.Survey([src])
wires = Maps.Wires(('eta', mesh.nC), ('taui', mesh.nC))
problem = SIP.Problem3D_N(
mesh,
sigma=sigma,
etaMap=wires.eta,
tauiMap=wires.taui
)
problem.Solver = Solver
problem.pair(survey)
mSynth = np.r_[eta, 1./tau]
survey.makeSyntheticData(mSynth)
# Now set up the problem to do some minimization
dmis = DataMisfit.l2_DataMisfit(survey)
reg = Regularization.Tikhonov(mesh)
opt = Optimization.InexactGaussNewton(
maxIterLS=20, maxIter=10, tolF=1e-6,
tolX=1e-6, tolG=1e-6, maxIterCG=6
)
invProb = InvProblem.BaseInvProblem(dmis, reg, opt, beta=1e4)
inv = Inversion.BaseInversion(invProb)
self.inv = inv
self.reg = reg
self.p = problem
self.mesh = mesh
self.m0 = mSynth
self.survey = survey
self.dmis = dmis
def setUp(self):
cs = 12.5
hx = [(cs,7, -1.3),(cs,61),(cs,7, 1.3)]
hy = [(cs,7, -1.3),(cs,20)]
mesh = Mesh.TensorMesh([hx, hy],x0="CN")
sighalf = 1e-2
sigma = np.ones(mesh.nC)*sighalf
x = np.linspace(-135, 250., 20)
M = Utils.ndgrid(x-12.5, np.r_[0.])
N = Utils.ndgrid(x+12.5, np.r_[0.])
A0loc = np.r_[-150, 0.]
A1loc = np.r_[-130, 0.]
rxloc = [np.c_[M, np.zeros(20)], np.c_[N, np.zeros(20)]]
data_anal = EM.Analytics.DCAnalyticHalf(np.r_[A0loc, 0.], rxloc, sighalf, earth_type="halfspace")
rx = DC.Rx.Dipole_ky(M, N)
src0 = DC.Src.Pole([rx], A0loc)
survey = DC.Survey_ky([src0])
self.survey = survey
self.mesh = mesh
self.sigma = sigma
self.data_anal = data_anal
try:
from pymatsolver import MumpsSolver
self.Solver = MumpsSolver
except ImportError, e:
self.Solver = SolverLU
def __init__(self, h_in, x0_in=None):
assert type(h_in) in [list, tuple], 'h_in must be a list'
assert len(h_in) in [1,2,3], 'h_in must be of dimension 1, 2, or 3'
h = range(len(h_in))
for i, h_i in enumerate(h_in):
if Utils.isScalar(h_i) and type(h_i) is not np.ndarray:
# This gives you something over the unit cube.
h_i = self._unitDimensions[i] * np.ones(int(h_i))/int(h_i)
elif type(h_i) is list:
h_i = Utils.meshTensor(h_i)
assert isinstance(h_i, np.ndarray), ("h[%i] is not a numpy array." % i)
assert len(h_i.shape) == 1, ("h[%i] must be a 1D numpy array." % i)
h[i] = h_i[:] # make a copy.
x0 = np.zeros(len(h))
if x0_in is not None:
assert len(h) == len(x0_in), "Dimension mismatch. x0 != len(h)"
for i in range(len(h)):
x_i, h_i = x0_in[i], h[i]
if Utils.isScalar(x_i):
x0[i] = x_i
elif x_i == '0':
x0[i] = 0.0
elif x_i == 'C':
x0[i] = -h_i.sum()*0.5
elif x_i == 'N':
x0[i] = -h_i.sum()
else:
raise Exception("x0[%i] must be a scalar or '0' to be zero, 'C' to center, or 'N' to be negative." % i)
BaseRectangularMesh.__init__(self, np.array([x.size for x in h]), x0)
# Ensure h contains 1D vectors
self._h = [Utils.mkvc(x.astype(float)) for x in h]
def MnSigmaDeriv(self, u):
"""
Derivative of MnSigma with respect to the model
"""
sigma = self.curModel.sigma
sigmaderiv = self.curModel.sigmaDeriv
vol = self.mesh.vol
return Utils.sdiag(u)*self.mesh.aveN2CC.T*Utils.sdiag(vol) * self.curModel.sigmaDeriv
def diagsJacobian(self, m, hn, hn1, dt, bc):
"""Diagonals and rhs of the jacobian system
The matrix that we are computing has the form::
.- -. .- -. .- -.
| Adiag | | h1 | | b1 |
| Asub Adiag | | h2 | | b2 |
| Asub Adiag | | h3 | = | b3 |
| ... ... | | .. | | .. |
| Asub Adiag | | hn | | bn |
'- -' '- -' '- -'
"""
if m is not None:
self.model = m
DIV = self.mesh.faceDiv
GRAD = self.mesh.cellGrad
BC = self.mesh.cellGradBC
AV = self.mesh.aveF2CC.T
Dz = self.Dz
dT = self.water_retention.derivU(hn)
dT1 = self.water_retention.derivU(hn1)
dTm = self.water_retention.derivM(hn)
dTm1 = self.water_retention.derivM(hn1)
K1 = self.hydraulic_conductivity(hn1)
dK1 = self.hydraulic_conductivity.derivU(hn1)
dKm1 = self.hydraulic_conductivity.derivM(hn1)
# Compute part of the derivative of:
#
# DIV*diag(GRAD*hn1+BC*bc)*(AV*(1.0/K))^-1
DdiagGh1 = DIV*Utils.sdiag(GRAD*hn1+BC*bc)
diagAVk2_AVdiagK2 = (
Utils.sdiag((AV*(1./K1))**(-2)) *
AV*Utils.sdiag(K1**(-2))
)
Asub = (-1.0/dt)*dT
Adiag = (
(1.0/dt)*dT1 -
DdiagGh1*diagAVk2_AVdiagK2*dK1 -
DIV*Utils.sdiag(1./(AV*(1./K1)))*GRAD -
Dz*diagAVk2_AVdiagK2*dK1
)
B = (
DdiagGh1*diagAVk2_AVdiagK2*dKm1 +
Dz*diagAVk2_AVdiagK2*dKm1 +
(1.0/dt)*(dTm - dTm1)
)
return Asub, Adiag, B
def get_interpolation_matrix(
self,
npts=20,
epsilon=None
):
tree_2d = kdtree(self.topography[:, :2])
xy = Utils.ndgrid(self.mesh_3d.vectorCCx, self.mesh_3d.vectorCCy)
distance, inds = tree_2d.query(xy, k=npts)
if epsilon is None:
epsilon = np.min([self.mesh_3d.hx.min(), self.mesh_3d.hy.min()])
w = 1. / (distance + epsilon)**2
w = Utils.sdiag(1./np.sum(w, axis=1)) * (w)
I = Utils.mkvc(
np.arange(inds.shape[0]).reshape([-1, 1]).repeat(npts, axis=1)
)
J = Utils.mkvc(inds)
self._P = sp.coo_matrix(
(Utils.mkvc(w), (I, J)),
shape=(inds.shape[0], self.topography.shape[0])
)
mesh_1d = Mesh.TensorMesh([np.r_[self.hz[:-1], 1e20]])
z = self.P*self.topography[:, 2]
self._actinds = Utils.surface2ind_topo(self.mesh_3d, np.c_[xy, z])
Z = np.empty(self.mesh_3d.vnC, dtype=float, order='F')
Z = self.mesh_3d.gridCC[:, 2].reshape(
(self.mesh_3d.nCx*self.mesh_3d.nCy, self.mesh_3d.nCz), order='F'
)
ACTIND = self._actinds.reshape(
(self.mesh_3d.nCx*self.mesh_3d.nCy, self.mesh_3d.nCz), order='F'
)
self._Pz = []
# This part can be cythonized or parallelized
for i_xy in range(self.mesh_3d.nCx*self.mesh_3d.nCy):
actind_temp = ACTIND[i_xy, :]
z_temp = -(Z[i_xy, :] - z[i_xy])
self._Pz.append(mesh_1d.getInterpolationMat(z_temp[actind_temp]))
def switchKernal(xx):
"""Switches over the different options."""
if xType in ['CC', 'N']:
nn = (self._n) if xType == 'CC' else (self._n + 1)
assert xx.size == np.prod(
nn), "Number of elements must not change."
return outKernal(xx, nn)
elif xType in ['F', 'E']:
# This will only deal with components of fields, not full 'F' or 'E'
xx = Utils.mkvc(xx) # unwrap it in case it is a matrix
nn = self.vnF if xType == 'F' else self.vnE
nn = np.r_[0, nn]
nx = [0, 0, 0]
nx[0] = self.vnFx if xType == 'F' else self.vnEx
nx[1] = self.vnFy if xType == 'F' else self.vnEy
nx[2] = self.vnFz if xType == 'F' else self.vnEz
for dim, dimName in enumerate(['x', 'y', 'z']):
if dimName in outType:
assert self.dim > dim, (
"Dimensions of mesh not great enough for %s%s",
(xType, dimName))
assert xx.size == np.sum(
nn), 'Vector is not the right size.'
start = np.sum(nn[:dim + 1])
end = np.sum(nn[:dim + 2])
return outKernal(xx[start:end], nx[dim])
elif xTypeIsFExyz:
# This will deal with partial components (x, y or z) lying on edges or faces
if 'x' in xType:
nn = self.vnFx if 'F' in xType else self.vnEx
elif 'y' in xType:
nn = self.vnFy if 'F' in xType else self.vnEy
elif 'z' in xType:
nn = self.vnFz if 'F' in xType else self.vnEz
assert xx.size == np.prod(nn), 'Vector is not the right size.'
return outKernal(xx, nn)
def ePrimaryDeriv(self, prob, v, adjoint=False, f=None):
if f is None:
f = self._primaryFields(prob)
# if adjoint is True:
# raise NotImplementedError
if self.primaryProblem._formulation == 'EB':
if adjoint is True:
epDeriv = self._primaryFieldsDeriv(
prob, (self._ProjPrimary(prob, 'E', 'E').T * v),
f=f,
adjoint=adjoint)
else:
epDeriv = (self._ProjPrimary(prob, 'E', 'E') *
self._primaryFieldsDeriv(prob, v, f=f))
elif self.primaryProblem._formulation == 'HJ':
if adjoint is True:
PTv = (self.primaryProblem.MfI.T *
(self._ProjPrimary(prob, 'F', 'E').T * v))
epDeriv = (
self.primaryProblem.MfRhoDeriv(f[:, 'j']).T * PTv +
self._primaryFieldsDeriv(prob,
self.primaryProblem.MfRho.T * PTv,
adjoint=adjoint,
f=f))
# epDeriv =(
# (self.primaryProblem.MfI.T * PTv)
# )
else:
epDeriv = (self._ProjPrimary(prob, 'F', 'E') *
(self.primaryProblem.MfI *
((self.primaryProblem.MfRhoDeriv(f[:, 'j']) * v) +
(self.primaryProblem.MfRho *
self._primaryFieldsDeriv(prob, v, f=f)))))
return Utils.mkvc(epDeriv)
def bPrimary(self, prob):
"""
The primary magnetic flux density from the analytic solution for
magnetic fields from a dipole
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
formulation = prob._formulation
if formulation is 'EB':
gridX = prob.mesh.gridFx
gridY = prob.mesh.gridFy
gridZ = prob.mesh.gridFz
elif formulation is 'HJ':
gridX = prob.mesh.gridEx
gridY = prob.mesh.gridEy
gridZ = prob.mesh.gridEz
srcfct = MagneticDipoleFields
if prob.mesh._meshType is 'CYL':
if not prob.mesh.isSymmetric:
# TODO ?
raise NotImplementedError(
'Non-symmetric cyl mesh not implemented yet!')
bx = srcfct(self.loc, gridX, 'x', mu=self.mu, moment=self.moment)
bz = srcfct(self.loc, gridZ, 'z', mu=self.mu, moment=self.moment)
b = np.concatenate((bx, bz))
else:
bx = srcfct(self.loc, gridX, 'x', mu=self.mu, moment=self.moment)
by = srcfct(self.loc, gridY, 'y', mu=self.mu, moment=self.moment)
bz = srcfct(self.loc, gridZ, 'z', mu=self.mu, moment=self.moment)
b = np.concatenate((bx, by, bz))
return Utils.mkvc(b)
def magnetizationModel(self):
"""
magnetization vector
"""
if getattr(self, 'magfile', None) is None:
M = Magnetics.dipazm_2_xyz(
np.ones(self.nC) * self.survey.srcField.param[1],
np.ones(self.nC) * self.survey.srcField.param[2])
else:
with open(self.basePath + self.magfile) as f:
magmodel = f.read()
magmodel = magmodel.splitlines()
M = []
for line in magmodel:
M.append(map(float, line.split()))
# Convert list to 2d array
M = np.vstack(M)
# Cycle through three components and permute from UBC to SimPEG
for ii in range(3):
m = np.reshape(M[:, ii],
(self.mesh.nCz, self.mesh.nCx, self.mesh.nCy),
order='F')
m = m[::-1, :, :]
m = np.transpose(m, (1, 2, 0))
M[:, ii] = Utils.mkvc(m)
self._M = M
return self._M
def setUp(self):
M = Mesh.TensorMesh([np.ones(8), np.ones(30)])
M.setCellGradBC(['neumann', 'dirichlet'])
params = Richards.Empirical.HaverkampParams().celia1990
params['Ks'] = np.log(params['Ks'])
E = Richards.Empirical.Haverkamp(M, **params)
bc = np.array([-61.5, -20.7])
bc = np.r_[np.zeros(M.nCy * 2),
np.ones(M.nCx) * bc[0],
np.ones(M.nCx) * bc[1]]
h = np.zeros(M.nC) + bc[0]
prob = Richards.RichardsProblem(M,
E,
timeSteps=[(40, 3), (60, 3)],
boundaryConditions=bc,
initialConditions=h,
doNewton=False,
method='mixed',
tolRootFinder=1e-6,
debug=False)
prob.Solver = Solver
locs = Utils.ndgrid(np.array([5, 7.]), np.array([5, 15, 25.]))
times = prob.times[3:5]
rxSat = Richards.RichardsRx(locs, times, 'saturation')
rxPre = Richards.RichardsRx(locs, times, 'pressureHead')
survey = Richards.RichardsSurvey([rxSat, rxPre])
prob.pair(survey)
self.h0 = h
self.M = M
self.Ks = params['Ks']
self.prob = prob
self.survey = survey
def setUp(self):
print('\nTesting Transect for analytic')
cs = 10.
ncx, ncy, ncz = 10, 10, 10
npad = 5
freq = 1e2
hx = [(cs, npad, -1.3), (cs, ncx), (cs, npad, 1.3)]
hy = [(cs, npad, -1.3), (cs, ncy), (cs, npad, 1.3)]
hz = [(cs, npad, -1.3), (cs, ncz), (cs, npad, 1.3)]
mesh = Mesh.TensorMesh([hx, hy, hz], 'CCC')
mapping = Maps.ExpMap(mesh)
x = np.linspace(-10, 10, 5)
XYZ = Utils.ndgrid(x, np.r_[0], np.r_[0])
rxList = EM.FDEM.Rx.Point_e(XYZ, orientation='x', component='imag')
SrcList = [
EM.FDEM.Src.MagDipole([rxList], loc=np.r_[0., 0., 0.], freq=freq),
EM.FDEM.Src.CircularLoop([rxList],
loc=np.r_[0., 0., 0.],
freq=freq,
radius=np.sqrt(1. / np.pi)),
# EM.FDEM.Src.MagDipole_Bfield([rxList], loc=np.r_[0., 0., 0.],
# freq=freq), # less accurate
]
survey = EM.FDEM.Survey(SrcList)
prb = EM.FDEM.Problem3D_b(mesh, mapping=mapping)
prb.pair(survey)
try:
from pymatsolver import MumpsSolver
prb.Solver = MumpsSolver
except ImportError, e:
prb.Solver = SolverLU
def test_2sum(self):
nP = 80
alpha1 = 100
alpha2 = 200
phi1 = (ObjectiveFunction.L2ObjectiveFunction(
W=Utils.sdiag(np.random.rand(nP))) +
alpha1 * ObjectiveFunction.L2ObjectiveFunction())
phi2 = ObjectiveFunction.L2ObjectiveFunction() + alpha2 * phi1
self.assertTrue(phi2.test(eps=EPS))
self.assertTrue(len(phi1.multipliers) == 2)
self.assertTrue(len(phi2.multipliers) == 2)
self.assertTrue(len(phi1.objfcts) == 2)
self.assertTrue(len(phi2.objfcts) == 2)
self.assertTrue(len(phi2) == 2)
self.assertTrue(len(phi1) == 2)
self.assertTrue(len(phi2) == 2)
self.assertTrue(np.all(phi1.multipliers == np.r_[1., alpha1]))
self.assertTrue(np.all(phi2.multipliers == np.r_[1., alpha2]))
def B_field_from_SheetCurruent(XYZ, srcLoc, sig, f, E0=1., orientation='X', kappa=0., epsr=1., t=0.):
"""
Plane wave propagating downward (negative z (depth))
"""
XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
# Check
if XYZ.shape[0] > 1 & f.shape[0] > 1:
raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")
mu = mu_0*(1+kappa)
epsilon = epsilon_0*epsr
sig_hat = sig + 1j*omega(f)*epsilon
k = np.sqrt( omega(f)**2. *mu*epsilon -1j*omega(f)*mu*sig )
Z = omega(f)*mu/k
if orientation == "X":
z = XYZ[:,2]
Bx = mu*np.zeros_like(z)
By = mu*E0/Z*np.exp(1j*(k*(z-srcLoc)+omega(f)*t))
Bz = mu*np.zeros_like(z)
return Bx, By, Bz
else:
raise NotImplementedError()
def isInside(self, pts, locType='N'):
"""
Determines if a set of points are inside a mesh.
:param numpy.ndarray pts: Location of points to test
:rtype numpy.ndarray
:return inside, numpy array of booleans
"""
pts = Utils.asArray_N_x_Dim(pts, self.dim)
tensors = self.getTensor(locType)
if locType == 'N' and self._meshType == 'CYL':
#NOTE: for a CYL mesh we add a node to check if we are inside in the radial direction!
tensors[0] = np.r_[0., tensors[0]]
tensors[1] = np.r_[tensors[1], 2.0 * np.pi]
inside = np.ones(pts.shape[0], dtype=bool)
for i, tensor in enumerate(tensors):
TOL = np.diff(tensor).min() * 1.0e-10
inside = inside & (pts[:, i] >= tensor.min() - TOL) & (
pts[:, i] <= tensor.max() + TOL)
return inside
def activeCells(self):
if getattr(self, '_activeCells', None) is None:
if getattr(self, 'topofile', None) is not None:
topo = np.genfromtxt(self.basePath + self.topofile, skip_header=1)
# Find the active cells
active = Utils.surface2ind_topo(self.mesh, topo, 'N')
elif isinstance(self._staticInput, float):
active = self.m0 != self._staticInput
else:
# Read from file active cells with 0:air, 1:dynamic, -1 static
active = self.activeModel != 0
inds = np.asarray([inds for inds, elem in enumerate(active, 1)
if elem], dtype=int) - 1
self._activeCells = inds
# Reduce m0 to active space
if len(self.m0) > len(self._activeCells):
self._m0 = self.m0[self._activeCells]
return self._activeCells
def fields(self, m):
"""
Return magnetic potential (u) and flux (B)
u: defined on the cell center [nC x 1]
B: defined on the cell center [nF x 1]
After we compute u, then we update B.
.. math ::
\mathbf{B}_s = (\MfMui)^{-1}\mathbf{M}^f_{\mu_0^{-1}}\mathbf{B}_0-\mathbf{B}_0 -(\MfMui)^{-1}\Div^T \mathbf{u}
"""
self.makeMassMatrices(m)
A = self.getA(m)
rhs = self.getRHS(m)
m1 = sp.linalg.interface.aslinearoperator(Utils.sdiag(1 /
A.diagonal()))
u, info = sp.linalg.bicgstab(A, rhs, tol=1e-6, maxiter=1000, M=m1)
B0 = self.getB0()
B = self.MfMuI * self.MfMu0 * B0 - B0 - self.MfMuI * self._Div.T * u
return {'B': B, 'u': u}
def writeVectorUBC(mesh, fileName, model):
"""
Writes a vector model associated with a SimPEG TensorMesh
to a UBC-GIF format model file.
:param string fileName: File to write to
:param numpy.ndarray model: The model
"""
modelMatTR = np.zeros_like(model)
for ii in range(3):
# Reshape model to a matrix
modelMat = mesh.r(model[:, ii], "CC", "CC", "M")
# Transpose the axes
modelMatT = modelMat.transpose((2, 0, 1))
# Flip UBC order
modelMatTR[:, ii] = Utils.mkvc(modelMatT[::-1, :, :])
# Flip z to positive down for MeshTools3D
if ii == 2:
modelMatTR[:, ii] *= -1
np.savetxt(fileName, modelMatTR)
def getInitialFieldsDeriv(self, src, v, adjoint=False, f=None):
ifieldsDeriv = Utils.mkvc(
getattr(
src, '{}InitialDeriv'.format(self._fieldType), None
)(self, v, adjoint, f)
)
# take care of any utils.zero cases
if adjoint is False:
if self._fieldType in ['b', 'j']:
ifieldsDeriv += np.zeros(self.mesh.nF)
elif self._fieldType in ['e', 'h']:
ifieldsDeriv += np.zeros(self.mesh.nE)
elif adjoint is True:
if self._fieldType in ['b', 'j']:
ifieldsDeriv += np.zeros(self.mesh.nF)
elif self._fieldType in ['e', 'h']:
ifieldsDeriv[0] += np.zeros(self.mesh.nE)
ifieldsDeriv[1] += np.zeros_like(self.model) # take care of a Utils.Zero() case
return ifieldsDeriv
def run(plotIt=True):
M = Mesh.TensorMesh([32, 32])
v = Utils.ModelBuilder.randomModel(M.vnC, seed=789)
v = Utils.mkvc(v)
O = Mesh.TreeMesh([32, 32])
O.refine(1)
def function(cell):
if (cell.center[0] < 0.75 and cell.center[0] > 0.25
and cell.center[1] < 0.75 and cell.center[1] > 0.25):
return 5
if (cell.center[0] < 0.9 and cell.center[0] > 0.1
and cell.center[1] < 0.9 and cell.center[1] > 0.1):
return 4
return 3
O.refine(function)
P = M.getInterpolationMat(O.gridCC, 'CC')
ov = P * v
if not plotIt:
return
fig, axes = plt.subplots(1, 2, figsize=(10, 5))
out = M.plotImage(v, grid=True, ax=axes[0])
cb = plt.colorbar(out[0], ax=axes[0])
cb.set_label("Random Field")
axes[0].set_title('TensorMesh')
out = O.plotImage(ov, grid=True, ax=axes[1], clim=[0, 1])
cb = plt.colorbar(out[0], ax=axes[1])
cb.set_label("Random Field")
axes[1].set_title('TreeMesh')
def ElectricDipoleWholeSpace(XYZ, srcLoc, sig, f, current=1., length=1., orientation='X', mu=mu_0):
XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
dx = XYZ[:,0]-srcLoc[0]
dy = XYZ[:,1]-srcLoc[1]
dz = XYZ[:,2]-srcLoc[2]
r = np.sqrt( dx**2. + dy**2. + dz**2.)
k = np.sqrt( -1j*2.*np.pi*f*mu*sig )
kr = k*r
front = current * length / (4. * np.pi * sig * r**3) * np.exp(-1j*k*r)
mid = -k**2 * r**2 + 3*1j*k*r + 3
# Ex = front*((dx**2 / r**2)*mid + (k**2 * r**2 -1j*k*r))
# Ey = front*(dx*dy / r**2)*mid
# Ez = front*(dx*dz / r**2)*mid
if orientation.upper() == 'X':
Ex = front*((dx**2 / r**2)*mid + (k**2 * r**2 -1j*k*r-1.))
Ey = front*(dx*dy / r**2)*mid
Ez = front*(dx*dz / r**2)*mid
return Ex, Ey, Ez
elif orientation.upper() == 'Y':
# x--> y, y--> z, z-->x
Ey = front*((dy**2 / r**2)*mid + (k**2 * r**2 -1j*k*r-1.))
Ez = front*(dy*dz / r**2)*mid
Ex = front*(dy*dx / r**2)*mid
return Ex, Ey, Ez
elif orientation.upper() == 'Z':
# x --> z, y --> x, z --> y
Ez = front*((dz**2 / r**2)*mid + (k**2 * r**2 -1j*k*r-1.))
Ex = front*(dz*dx / r**2)*mid
Ey = front*(dz*dy / r**2)*mid
return Ex, Ey, Ez
def Jvec(self, m, v, f=None):
"""
Sensitivity times a vector.
:param numpy.array m: inversion model (nP,)
:param numpy.array v: vector which we take sensitivity product with (nP,)
:param SimPEG.EM.FDEM.FieldsFDEM.FieldsFDEM u: fields object
:rtype numpy.array:
:return: Jv (ndata,)
"""
if f is None:
f = self.fields(m)
self.curModel = m
Jv = self.dataPair(self.survey)
for freq in self.survey.freqs:
A = self.getA(freq)
Ainv = self.Solver(
A, **self.solverOpts
) # create the concept of Ainv (actually a solve)
for src in self.survey.getSrcByFreq(freq):
u_src = f[src, self._solutionType]
dA_dm_v = self.getADeriv(freq, u_src, v)
dRHS_dm_v = self.getRHSDeriv(freq, src, v)
du_dm_v = Ainv * (-dA_dm_v + dRHS_dm_v)
for rx in src.rxList:
df_dmFun = getattr(f, '_{0}Deriv'.format(rx.projField),
None)
df_dm_v = df_dmFun(src, du_dm_v, v, adjoint=False)
Jv[src, rx] = rx.evalDeriv(src, self.mesh, f, df_dm_v)
Ainv.clean()
return Utils.mkvc(Jv)
def E_field_from_SheetCurruent(XYZ, srcLoc, sig, t, E0=1., orientation='X', kappa=0., epsr=1.):
"""
Computing Analytic Electric fields from Plane wave in a Wholespace
TODO:
Add description of parameters
"""
XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
# Check
if XYZ.shape[0] > 1 & t.shape[0] > 1:
raise Exception("I/O type error: For multiple field locations only a single frequency can be specified.")
mu = mu_0*(1+kappa)
if orientation == "X":
z = XYZ[:, 2]
bunja = -E0*(mu*sig)**0.5 * z * np.exp(-(mu*sig*z**2) / (4*t))
bunmo = 2 * np.pi**0.5 * t**1.5
Ex = bunja / bunmo
Ey = np.zeros_like(z)
Ez = np.zeros_like(z)
return Ex, Ey, Ez
else:
raise NotImplementedError()
def hzAnalyticDipoleF(r, freq, sigma, secondary=True, mu=mu_0):
"""
4.56 in Ward and Hohmann
.. plot::
import matplotlib.pyplot as plt
from SimPEG import EM
freq = np.logspace(-1, 6, 61)
test = EM.Analytics.FDEM.hzAnalyticDipoleF(100, freq, 0.001, secondary=False)
plt.loglog(freq, abs(test.real))
plt.loglog(freq, abs(test.imag))
plt.title('Response at $r$=100m')
plt.xlabel('Frequency')
plt.ylabel('Response')
plt.legend(('real','imag'))
plt.show()
"""
r = np.abs(r)
k = np.sqrt(-1j * 2. * np.pi * freq * mu * sigma)
m = 1
front = m / (2. * np.pi * (k**2) * (r**5))
back = 9 - (9 + 9j * k * r - 4 * (k**2) * (r**2) - 1j * (k**3) *
(r**3)) * np.exp(-1j * k * r)
hz = front * back
if secondary:
hp = -1 / (4 * np.pi * r**3)
hz = hz - hp
if hz.ndim == 1:
hz = Utils.mkvc(hz, 2)
return hz
def bPrimary(self, prob):
"""
The primary magnetic flux density from the analytic solution for
magnetic fields from a dipole
:param BaseFDEMProblem prob: FDEM problem
:rtype: numpy.ndarray
:return: primary magnetic field
"""
formulation = prob._formulation
coordinates = "cartesian"
if formulation == 'EB':
gridX = prob.mesh.gridFx
gridY = prob.mesh.gridFy
gridZ = prob.mesh.gridFz
elif formulation == 'HJ':
gridX = prob.mesh.gridEx
gridY = prob.mesh.gridEy
gridZ = prob.mesh.gridEz
if prob.mesh._meshType == 'CYL':
coordinates = "cylindrical"
if prob.mesh.isSymmetric:
bx = self._srcFct(gridX)[:, 0]
bz = self._srcFct(gridZ)[:, 2]
b = np.concatenate((bx, bz))
else:
bx = self._srcFct(gridX, coordinates=coordinates)[:, 0]
by = self._srcFct(gridY, coordinates=coordinates)[:, 1]
bz = self._srcFct(gridZ, coordinates=coordinates)[:, 2]
b = np.concatenate((bx, by, bz))
return Utils.mkvc(b)
def getFields(self, itime):
src = self.srcList[0]
Ey = self.mesh.aveE2CC * self.f[src, "e", itime]
Jy = Utils.sdiag(self.prb.sigma) * Ey
self.Ey = Utils.mkvc(self.mirrorArray(Ey[self.activeCC],
direction="y"))
self.Jy = Utils.mkvc(self.mirrorArray(Jy[self.activeCC],
direction="y"))
self.Bx = Utils.mkvc(
self.mirrorArray(self.Pfx * self.f[src, "b", itime],
direction="x"))
self.Bz = Utils.mkvc(
self.mirrorArray(self.Pfz * self.f[src, "b", itime],
direction="z"))
self.dBxdt = Utils.mkvc(
self.mirrorArray(-self.Pfx * self.mesh.edgeCurl *
self.f[src, "e", itime],
direction="x"))
self.dBzdt = Utils.mkvc(
self.mirrorArray(-self.Pfz * self.mesh.edgeCurl *
self.f[src, "e", itime],
direction="z"))
def Intrgl_Fwr_Op(xn, yn, zn, rxLoc):
"""
Magnetic forward operator in integral form
flag = 'ind' | 'full'
1- ind : Magnetization fixed by user
3- full: Full tensor matrix stored with shape([3*ndata, 3*nc])
Return
_G = Linear forward modeling operation
"""
yn2, xn2, zn2 = np.meshgrid(yn[1:], xn[1:], zn[1:])
yn1, xn1, zn1 = np.meshgrid(yn[0:-1], xn[0:-1], zn[0:-1])
Yn = np.c_[Utils.mkvc(yn1), Utils.mkvc(yn2)]
Xn = np.c_[Utils.mkvc(xn1), Utils.mkvc(xn2)]
Zn = np.c_[Utils.mkvc(zn1), Utils.mkvc(zn2)]
ndata = rxLoc.shape[0]
# Pre-allocate forward matrix
G = np.zeros((int(3 * ndata), 3))
for ii in range(ndata):
tx, ty, tz = PF.Magnetics.get_T_mat(Xn, Yn, Zn, rxLoc[ii, :])
G[ii, :] = tx / 1e-9 * mu_0
G[ii + ndata, :] = ty / 1e-9 * mu_0
G[ii + 2 * ndata, :] = tz / 1e-9 * mu_0
return G
def resolve_1Dinversions(mesh,
dobs,
src_height,
freqs,
m0,
mref,
mapping,
std=0.08,
floor=1e-14,
rxOffset=7.86):
"""
Perform a single 1D inversion for a RESOLVE sounding for Horizontal
Coplanar Coil data (both real and imaginary).
:param discretize.CylMesh mesh: mesh used for the forward simulation
:param numpy.array dobs: observed data
:param float src_height: height of the source above the ground
:param numpy.array freqs: frequencies
:param numpy.array m0: starting model
:param numpy.array mref: reference model
:param Maps.IdentityMap mapping: mapping that maps the model to electrical conductivity
:param float std: percent error used to construct the data misfit term
:param float floor: noise floor used to construct the data misfit term
:param float rxOffset: offset between source and receiver.
"""
# ------------------- Forward Simulation ------------------- #
# set up the receivers
bzr = EM.FDEM.Rx.Point_bSecondary(np.array([[rxOffset, 0., src_height]]),
orientation='z',
component='real')
bzi = EM.FDEM.Rx.Point_b(np.array([[rxOffset, 0., src_height]]),
orientation='z',
component='imag')
# source location
srcLoc = np.array([0., 0., src_height])
srcList = [
EM.FDEM.Src.MagDipole([bzr, bzi], freq, srcLoc, orientation='Z')
for freq in freqs
]
# construct a forward simulation
survey = EM.FDEM.Survey(srcList)
prb = EM.FDEM.Problem3D_b(mesh, sigmaMap=mapping, Solver=PardisoSolver)
prb.pair(survey)
# ------------------- Inversion ------------------- #
# data misfit term
survey.dobs = dobs
dmisfit = DataMisfit.l2_DataMisfit(survey)
uncert = abs(dobs) * std + floor
dmisfit.W = 1. / uncert
# regularization
regMesh = Mesh.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])
reg = Regularization.Simple(regMesh)
reg.mref = mref
# optimization
opt = Optimization.InexactGaussNewton(maxIter=10)
# statement of the inverse problem
invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)
# Inversion directives and parameters
target = Directives.TargetMisfit()
inv = Inversion.BaseInversion(invProb, directiveList=[target])
invProb.beta = 2. # Fix beta in the nonlinear iterations
reg.alpha_s = 1e-3
reg.alpha_x = 1.
prb.counter = opt.counter = Utils.Counter()
opt.LSshorten = 0.5
opt.remember('xc')
# run the inversion
mopt = inv.run(m0)
return mopt, invProb.dpred, survey.dobs
def run(runIt=False, plotIt=True, saveIt=False, saveFig=False, cleanup=True):
"""
Run the bookpurnong 1D stitched RESOLVE inversions.
:param bool runIt: re-run the inversions? Default downloads and plots saved results
:param bool plotIt: show the plots?
:param bool saveIt: save the re-inverted results?
:param bool saveFig: save the figure
:param bool cleanup: remove the downloaded results
"""
# download the data
downloads, directory = download_and_unzip_data()
# Load resolve data
resolve = h5py.File(os.path.sep.join([directory, "booky_resolve.hdf5"]),
"r")
river_path = resolve["river_path"][()] # River path
nSounding = resolve["data"].shape[0] # the # of soundings
# Bird height from surface
b_height_resolve = resolve["src_elevation"][()]
# fetch the frequencies we are considering
cpi_inds = [0, 2, 6, 8, 10] # Indices for HCP in-phase
cpq_inds = [1, 3, 7, 9, 11] # Indices for HCP quadrature
frequency_cp = resolve["frequency_cp"][()]
# build a mesh
cs, ncx, ncz, npad = 1., 10., 10., 20
hx = [(cs, ncx), (cs, npad, 1.3)]
npad = 12
temp = np.logspace(np.log10(1.), np.log10(12.), 19)
temp_pad = temp[-1] * 1.3**np.arange(npad)
hz = np.r_[temp_pad[::-1], temp[::-1], temp, temp_pad]
mesh = Mesh.CylMesh([hx, 1, hz], '00C')
active = mesh.vectorCCz < 0.
# survey parameters
rxOffset = 7.86 # tx-rx separation
bp = -mu_0 / (4 * np.pi * rxOffset**3) # primary magnetic field
# re-run the inversion
if runIt:
# set up the mappings - we are inverting for 1D log conductivity
# below the earth's surface.
actMap = Maps.InjectActiveCells(mesh,
active,
np.log(1e-8),
nC=mesh.nCz)
mapping = Maps.ExpMap(mesh) * Maps.SurjectVertical1D(mesh) * actMap
# build starting and reference model
sig_half = 1e-1
sig_air = 1e-8
sigma = np.ones(mesh.nCz) * sig_air
sigma[active] = sig_half
m0 = np.log(1e-1) * np.ones(active.sum()) # starting model
mref = np.log(1e-1) * np.ones(active.sum()) # reference model
# initalize empty lists for storing inversion results
mopt_re = [] # recovered model
dpred_re = [] # predicted data
dobs_re = [] # observed data
# downsample the data for the inversion
nskip = 40
# set up a noise model
# 10% for the 3 lowest frequencies, 15% for the two highest
std = np.repeat(np.r_[np.ones(3) * 0.1, np.ones(2) * 0.15], 2)
floor = abs(20 * bp * 1e-6) # floor of 20ppm
# loop over the soundings and invert each
for rxind in range(nSounding):
# convert data from ppm to magnetic field (A/m^2)
dobs = np.c_[resolve["data"][rxind, :][cpi_inds].astype(float),
resolve["data"][rxind, :][cpq_inds].
astype(float)].flatten() * bp * 1e-6
# perform the inversion
src_height = b_height_resolve[rxind].astype(float)
mopt, dpred, dobs = resolve_1Dinversions(mesh,
dobs,
src_height,
frequency_cp,
m0,
mref,
mapping,
std=std,
floor=floor)
# add results to our list
mopt_re.append(mopt)
dpred_re.append(dpred)
dobs_re.append(dobs)
# save results
mopt_re = np.vstack(mopt_re)
dpred_re = np.vstack(dpred_re)
dobs_re = np.vstack(dobs_re)
if saveIt:
np.save("mopt_re_final", mopt_re)
np.save("dobs_re_final", dobs_re)
np.save("dpred_re_final", dpred_re)
mopt_re = resolve["mopt"][()]
dobs_re = resolve["dobs"][()]
dpred_re = resolve["dpred"][()]
sigma = np.exp(mopt_re)
indz = -7 # depth index
# so that we can visually compare with literature (eg Viezzoli, 2010)
cmap = "jet"
# dummy figure for colobar
fig = plt.figure()
out = plt.scatter(np.ones(3),
np.ones(3),
c=np.linspace(-2, 1, 3),
cmap=cmap)
plt.close(fig)
# plot from the paper
fs = 13 # fontsize
# matplotlib.rcParams['font.size'] = fs
plt.figure(figsize=(13, 7))
ax0 = plt.subplot2grid((2, 3), (0, 0), rowspan=2, colspan=2)
ax1 = plt.subplot2grid((2, 3), (0, 2))
ax2 = plt.subplot2grid((2, 3), (1, 2))
# titles of plots
title = [("(a) Recovered model, %.1f m depth") %
(-mesh.vectorCCz[active][indz]), "(b) Obs (Real 400 Hz)",
"(c) Pred (Real 400 Hz)"]
temp = sigma[:, indz]
tree = cKDTree(list(zip(resolve["xy"][:, 0], resolve["xy"][:, 1])))
d, d_inds = tree.query(list(zip(resolve["xy"][:, 0], resolve["xy"][:, 1])),
k=20)
w = 1. / (d + 100.)**2.
w = Utils.sdiag(1. / np.sum(w, axis=1)) * (w)
xy = resolve["xy"]
temp = (temp.flatten()[d_inds] * w).sum(axis=1)
Utils.plot2Ddata(xy,
temp,
ncontour=100,
scale="log",
dataloc=False,
contourOpts={
"cmap": cmap,
"vmin": 1e-2,
"vmax": 1e1
},
ax=ax0)
ax0.plot(resolve["xy"][:, 0], resolve["xy"][:, 1], 'k.', alpha=0.02, ms=1)
cb = plt.colorbar(out,
ax=ax0,
ticks=np.linspace(-2, 1, 4),
format="$10^{%.1f}$")
cb.set_ticklabels(["0.01", "0.1", "1", "10"])
cb.set_label("Conductivity (S/m)")
ax0.plot(river_path[:, 0], river_path[:, 1], 'k-', lw=0.5)
# plot observed and predicted data
freq_ind = 0
axs = [ax1, ax2]
temp_dobs = dobs_re[:, freq_ind].copy()
ax1.plot(river_path[:, 0], river_path[:, 1], 'k-', lw=0.5)
out = Utils.plot2Ddata(resolve["xy"][()],
temp_dobs / abs(bp) * 1e6,
ncontour=100,
scale="log",
dataloc=False,
ax=ax1,
contourOpts={"cmap": "viridis"})
vmin, vmax = out[0].get_clim()
cb = plt.colorbar(out[0],
ticks=np.linspace(vmin, vmax, 3),
ax=ax1,
format="%.1e",
fraction=0.046,
pad=0.04)
cb.set_label("Bz (ppm)")
temp_dpred = dpred_re[:, freq_ind].copy()
# temp_dpred[mask_:_data] = np.nan
ax2.plot(river_path[:, 0], river_path[:, 1], 'k-', lw=0.5)
Utils.plot2Ddata(resolve["xy"][()],
temp_dpred / abs(bp) * 1e6,
ncontour=100,
scale="log",
dataloc=False,
contourOpts={
"vmin": 10**vmin,
"vmax": 10**vmax,
"cmap": "viridis"
},
ax=ax2)
cb = plt.colorbar(out[0],
ticks=np.linspace(vmin, vmax, 3),
ax=ax2,
format="%.1e",
fraction=0.046,
pad=0.04)
cb.set_label("Bz (ppm)")
for i, ax in enumerate([ax0, ax1, ax2]):
xticks = [460000, 463000]
yticks = [6195000, 6198000, 6201000]
xloc, yloc = 462100.0, 6196500.0
ax.set_xticks(xticks)
ax.set_yticks(yticks)
# ax.plot(xloc, yloc, 'wo')
ax.plot(river_path[:, 0], river_path[:, 1], 'k', lw=0.5)
ax.set_aspect("equal")
ax.plot(resolve["xy"][:, 0],
resolve["xy"][:, 1],
'k.',
alpha=0.02,
ms=1)
ax.set_yticklabels([str(f) for f in yticks])
ax.set_ylabel("Northing (m)")
ax.set_xlabel("Easting (m)")
ax.set_title(title[i])
plt.tight_layout()
if plotIt:
plt.show()
if saveFig is True:
fig.savefig("obspred_resolve.png", dpi=200)
resolve.close()
if cleanup:
os.remove(downloads)
shutil.rmtree(directory)
# We will assume a vertical inducing field H0 = (50000., 90., 0.) # The magnetization is set along a different direction (induced + remanence) M = np.array([45., 90.]) # Create grid of points for topography # Lets create a simple Gaussian topo and set the active cells [xx, yy] = np.meshgrid(np.linspace(-200, 200, 50), np.linspace(-200, 200, 50)) b = 100 A = 50 zz = A * np.exp(-0.5 * ((xx / b)**2. + (yy / b)**2.)) # We would usually load a topofile topo = np.c_[Utils.mkvc(xx), Utils.mkvc(yy), Utils.mkvc(zz)] # Create and array of observation points xr = np.linspace(-100., 100., 20) yr = np.linspace(-100., 100., 20) X, Y = np.meshgrid(xr, yr) Z = A * np.exp(-0.5 * ((X / b)**2. + (Y / b)**2.)) + 5 # Create a MAGsurvey xyzLoc = np.c_[Utils.mkvc(X.T), Utils.mkvc(Y.T), Utils.mkvc(Z.T)] rxLoc = PF.BaseMag.RxObs(xyzLoc) srcField = PF.BaseMag.SrcField([rxLoc], param=H0) survey = PF.BaseMag.LinearSurvey(srcField) # Here how the topography looks with a quick interpolation, just a Gaussian... tri = sp.spatial.Delaunay(topo)
def MagneticDipoleWholeSpace(XYZ, srcLoc, sig, f, moment=1., orientation='X', mu = mu_0):
"""
Analytical solution for a dipole in a whole-space.
Equation 2.57 of Ward and Hohmann
TODOs:
- set it up to instead take a mesh & survey
- add E-fields
- handle multiple frequencies
- add divide by zero safety
.. plot::
from SimPEG import EM
import matplotlib.pyplot as plt
from scipy.constants import mu_0
freqs = np.logspace(-2,5,100)
Bx, By, Bz = EM.Analytics.FDEM.MagneticDipoleWholeSpace([0,100,0], [0,0,0], 1e-2, freqs, moment=1, orientation='Z')
plt.loglog(freqs, np.abs(Bz.real)/mu_0, 'b')
plt.loglog(freqs, np.abs(Bz.imag)/mu_0, 'r')
plt.legend(('real','imag'))
plt.show()
"""
XYZ = Utils.asArray_N_x_Dim(XYZ, 3)
dx = XYZ[:,0]-srcLoc[0]
dy = XYZ[:,1]-srcLoc[1]
dz = XYZ[:,2]-srcLoc[2]
r = np.sqrt( dx**2. + dy**2. + dz**2.)
k = np.sqrt( -1j*2.*np.pi*f*mu*sig )
kr = k*r
front = moment / (4.*pi * r**3.) * np.exp(-1j*kr)
mid = -kr**2. + 3.*1j*kr + 3.
if orientation.upper() == 'X':
Hx = front*( (dx/r)**2. * mid + (kr**2. - 1j*kr - 1.) )
Hy = front*( (dx*dy/r**2.) * mid )
Hz = front*( (dx*dz/r**2.) * mid )
elif orientation.upper() == 'Y':
Hx = front*( (dy*dx/r**2.) * mid )
Hy = front*( (dy/r)**2. * mid + (kr**2. - 1j*kr - 1.) )
Hz = front*( (dy*dz/r**2.) * mid )
elif orientation.upper() == 'Z':
Hx = front*( (dx*dz/r**2.) * mid )
Hy = front*( (dy*dz/r**2.) * mid )
Hz = front*( (dz/r)**2. * mid + (kr**2. - 1j*kr - 1.) )
Bx = mu*Hx
By = mu*Hy
Bz = mu*Hz
if Bx.ndim is 1:
Bx = Utils.mkvc(Bx,2)
if By.ndim is 1:
By = Utils.mkvc(By,2)
if Bz.ndim is 1:
Bz = Utils.mkvc(Bz,2)
return Bx, By, Bz
def evalDeriv(self, src, mesh, f, v, adjoint=False):
"""
The derivative of the projection wrt u
:param MTsrc src: MT source
:param TensorMesh mesh: Mesh defining the topology of the problem
:param MTfields f: MT fields object of the source
:param numpy.ndarray v: Random vector of size
"""
real_or_imag = self.projComp
if not adjoint:
if self.projType is 'Z1D':
Pex = mesh.getInterpolationMat(self.locs[:,-1],'Fx')
Pbx = mesh.getInterpolationMat(self.locs[:,-1],'Ex')
# ex = Pex*mkvc(f[src,'e_1d'],2)
# bx = Pbx*mkvc(f[src,'b_1d'],2)/mu_0
dP_de = -mkvc(Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0))*(Pex*v),2)
dP_db = mkvc( Utils.sdiag(Pex*mkvc(f[src,'e_1d'],2))*(Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)).T*Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)))*(Pbx*f._bDeriv_u(src,v)/mu_0),2)
PDeriv_complex = np.sum(np.hstack((dP_de,dP_db)),1)
elif self.projType is 'Z2D':
raise NotImplementedError('Has not been implement for 2D impedance tensor')
elif self.projType is 'Z3D':
if self.locs.ndim == 3:
eFLocs = self.locs[:,:,0]
bFLocs = self.locs[:,:,1]
else:
eFLocs = self.locs
bFLocs = self.locs
# Get the projection
Pex = mesh.getInterpolationMat(eFLocs,'Ex')
Pey = mesh.getInterpolationMat(eFLocs,'Ey')
Pbx = mesh.getInterpolationMat(bFLocs,'Fx')
Pby = mesh.getInterpolationMat(bFLocs,'Fy')
# Get the fields at location
# px: x-polaration and py: y-polaration.
ex_px = Pex*f[src,'e_px']
ey_px = Pey*f[src,'e_px']
ex_py = Pex*f[src,'e_py']
ey_py = Pey*f[src,'e_py']
hx_px = Pbx*f[src,'b_px']/mu_0
hy_px = Pby*f[src,'b_px']/mu_0
hx_py = Pbx*f[src,'b_py']/mu_0
hy_py = Pby*f[src,'b_py']/mu_0
# Derivatives as lambda functions
# The size of the diratives should be nD,nU
ex_px_u = lambda vec: Pex*f._e_pxDeriv_u(src,vec)
ey_px_u = lambda vec: Pey*f._e_pxDeriv_u(src,vec)
ex_py_u = lambda vec: Pex*f._e_pyDeriv_u(src,vec)
ey_py_u = lambda vec: Pey*f._e_pyDeriv_u(src,vec)
# NOTE: Think b_p?Deriv_u should return a 2*nF size matrix
hx_px_u = lambda vec: Pbx*f._b_pxDeriv_u(src,vec)/mu_0
hy_px_u = lambda vec: Pby*f._b_pxDeriv_u(src,vec)/mu_0
hx_py_u = lambda vec: Pbx*f._b_pyDeriv_u(src,vec)/mu_0
hy_py_u = lambda vec: Pby*f._b_pyDeriv_u(src,vec)/mu_0
# Update the input vector
sDiag = lambda t: Utils.sdiag(mkvc(t,2))
# Define the components of the derivative
Hd = sDiag(1./(sDiag(hx_px)*hy_py - sDiag(hx_py)*hy_px))
Hd_uV = sDiag(hy_py)*hx_px_u(v) + sDiag(hx_px)*hy_py_u(v) - sDiag(hx_py)*hy_px_u(v) - sDiag(hy_px)*hx_py_u(v)
# Calculate components
if 'zxx' in self.rxType:
Zij = sDiag(Hd*( sDiag(ex_px)*hy_py - sDiag(ex_py)*hy_px ))
ZijN_uV = sDiag(hy_py)*ex_px_u(v) + sDiag(ex_px)*hy_py_u(v) - sDiag(ex_py)*hy_px_u(v) - sDiag(hy_px)*ex_py_u(v)
elif 'zxy' in self.rxType:
Zij = sDiag(Hd*(-sDiag(ex_px)*hx_py + sDiag(ex_py)*hx_px ))
ZijN_uV = -sDiag(hx_py)*ex_px_u(v) - sDiag(ex_px)*hx_py_u(v) + sDiag(ex_py)*hx_px_u(v) + sDiag(hx_px)*ex_py_u(v)
elif 'zyx' in self.rxType:
Zij = sDiag(Hd*( sDiag(ey_px)*hy_py - sDiag(ey_py)*hy_px ))
ZijN_uV = sDiag(hy_py)*ey_px_u(v) + sDiag(ey_px)*hy_py_u(v) - sDiag(ey_py)*hy_px_u(v) - sDiag(hy_px)*ey_py_u(v)
elif 'zyy' in self.rxType:
Zij = sDiag(Hd*(-sDiag(ey_px)*hx_py + sDiag(ey_py)*hx_px ))
ZijN_uV = -sDiag(hx_py)*ey_px_u(v) - sDiag(ey_px)*hx_py_u(v) + sDiag(ey_py)*hx_px_u(v) + sDiag(hx_px)*ey_py_u(v)
# Calculate the complex derivative
PDeriv_complex = Hd * (ZijN_uV - Zij * Hd_uV )
elif self.projType is 'T3D':
if self.locs.ndim == 3:
eFLocs = self.locs[:,:,0]
bFLocs = self.locs[:,:,1]
else:
eFLocs = self.locs
bFLocs = self.locs
# Get the projection
Pbx = mesh.getInterpolationMat(bFLocs,'Fx')
Pby = mesh.getInterpolationMat(bFLocs,'Fy')
Pbz = mesh.getInterpolationMat(bFLocs,'Fz')
# Get the fields at location
# px: x-polaration and py: y-polaration.
bx_px = Pbx*f[src,'b_px']
by_px = Pby*f[src,'b_px']
bz_px = Pbz*f[src,'b_px']
bx_py = Pbx*f[src,'b_py']
by_py = Pby*f[src,'b_py']
bz_py = Pbz*f[src,'b_py']
# Derivatives as lambda functions
# NOTE: Think b_p?Deriv_u should return a 2*nF size matrix
bx_px_u = lambda vec: Pbx*f._b_pxDeriv_u(src,vec)
by_px_u = lambda vec: Pby*f._b_pxDeriv_u(src,vec)
bz_px_u = lambda vec: Pbz*f._b_pxDeriv_u(src,vec)
bx_py_u = lambda vec: Pbx*f._b_pyDeriv_u(src,vec)
by_py_u = lambda vec: Pby*f._b_pyDeriv_u(src,vec)
bz_py_u = lambda vec: Pbz*f._b_pyDeriv_u(src,vec)
# Update the input vector
sDiag = lambda t: Utils.sdiag(mkvc(t,2))
# Define the components of the derivative
Hd = sDiag(1./(sDiag(bx_px)*by_py - sDiag(bx_py)*by_px))
Hd_uV = sDiag(by_py)*bx_px_u(v) + sDiag(bx_px)*by_py_u(v) - sDiag(bx_py)*by_px_u(v) - sDiag(by_px)*bx_py_u(v)
if 'tzx' in self.rxType:
Tij = sDiag(Hd*( - sDiag(by_px)*bz_py + sDiag(by_py)*bz_px ))
TijN_uV = -sDiag(by_px)*bz_py_u(v) - sDiag(bz_py)*by_px_u(v) + sDiag(by_py)*bz_px_u(v) + sDiag(bz_px)*by_py_u(v)
elif 'tzy' in self.rxType:
Tij = sDiag(Hd*( sDiag(bx_px)*bz_py - sDiag(bx_py)*bz_px ))
TijN_uV = sDiag(bz_py)*bx_px_u(v) + sDiag(bx_px)*bz_py_u(v) - sDiag(bx_py)*bz_px_u(v) - sDiag(bz_px)*bx_py_u(v)
# Calculate the complex derivative
PDeriv_complex = Hd * (TijN_uV - Tij * Hd_uV )
# Extract the real number for the real/imag components.
Pv = np.array(getattr(PDeriv_complex, real_or_imag))
elif adjoint:
# Note: The v vector is real and the return should be complex
if self.projType is 'Z1D':
Pex = mesh.getInterpolationMat(self.locs[:,-1],'Fx')
Pbx = mesh.getInterpolationMat(self.locs[:,-1],'Ex')
# ex = Pex*mkvc(f[src,'e_1d'],2)
# bx = Pbx*mkvc(f[src,'b_1d'],2)/mu_0
dP_deTv = -mkvc(Pex.T*Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0)).T*v,2)
db_duv = Pbx.T/mu_0*Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0))*(Utils.sdiag(1./(Pbx*mkvc(f[src,'b_1d'],2)/mu_0))).T*Utils.sdiag(Pex*mkvc(f[src,'e_1d'],2)).T*v
dP_dbTv = mkvc(f._bDeriv_u(src,db_duv,adjoint=True),2)
PDeriv_real = np.sum(np.hstack((dP_deTv,dP_dbTv)),1)
elif self.projType is 'Z2D':
raise NotImplementedError('Has not be implement for 2D impedance tensor')
elif self.projType is 'Z3D':
if self.locs.ndim == 3:
eFLocs = self.locs[:,:,0]
bFLocs = self.locs[:,:,1]
else:
eFLocs = self.locs
bFLocs = self.locs
# Get the projection
Pex = mesh.getInterpolationMat(eFLocs,'Ex')
Pey = mesh.getInterpolationMat(eFLocs,'Ey')
Pbx = mesh.getInterpolationMat(bFLocs,'Fx')
Pby = mesh.getInterpolationMat(bFLocs,'Fy')
# Get the fields at location
# px: x-polaration and py: y-polaration.
aex_px = mkvc(mkvc(f[src,'e_px'],2).T*Pex.T)
aey_px = mkvc(mkvc(f[src,'e_px'],2).T*Pey.T)
aex_py = mkvc(mkvc(f[src,'e_py'],2).T*Pex.T)
aey_py = mkvc(mkvc(f[src,'e_py'],2).T*Pey.T)
ahx_px = mkvc(mkvc(f[src,'b_px'],2).T/mu_0*Pbx.T)
ahy_px = mkvc(mkvc(f[src,'b_px'],2).T/mu_0*Pby.T)
ahx_py = mkvc(mkvc(f[src,'b_py'],2).T/mu_0*Pbx.T)
ahy_py = mkvc(mkvc(f[src,'b_py'],2).T/mu_0*Pby.T)
# Derivatives as lambda functions
aex_px_u = lambda vec: f._e_pxDeriv_u(src,Pex.T*vec,adjoint=True)
aey_px_u = lambda vec: f._e_pxDeriv_u(src,Pey.T*vec,adjoint=True)
aex_py_u = lambda vec: f._e_pyDeriv_u(src,Pex.T*vec,adjoint=True)
aey_py_u = lambda vec: f._e_pyDeriv_u(src,Pey.T*vec,adjoint=True)
ahx_px_u = lambda vec: f._b_pxDeriv_u(src,Pbx.T*vec,adjoint=True)/mu_0
ahy_px_u = lambda vec: f._b_pxDeriv_u(src,Pby.T*vec,adjoint=True)/mu_0
ahx_py_u = lambda vec: f._b_pyDeriv_u(src,Pbx.T*vec,adjoint=True)/mu_0
ahy_py_u = lambda vec: f._b_pyDeriv_u(src,Pby.T*vec,adjoint=True)/mu_0
# Update the input vector
# Define shortcuts
sDiag = lambda t: Utils.sdiag(mkvc(t,2))
sVec = lambda t: Utils.sp.csr_matrix(mkvc(t,2))
# Define the components of the derivative
aHd = sDiag(1./(sDiag(ahx_px)*ahy_py - sDiag(ahx_py)*ahy_px))
aHd_uV = lambda x: ahx_px_u(sDiag(ahy_py)*x) + ahx_px_u(sDiag(ahy_py)*x) - ahy_px_u(sDiag(ahx_py)*x) - ahx_py_u(sDiag(ahy_px)*x)
# Need to fix this to reflect the adjoint
if 'zxx' in self.rxType:
Zij = sDiag(aHd*( sDiag(ahy_py)*aex_px - sDiag(ahy_px)*aex_py))
ZijN_uV = lambda x: aex_px_u(sDiag(ahy_py)*x) + ahy_py_u(sDiag(aex_px)*x) - ahy_px_u(sDiag(aex_py)*x) - aex_py_u(sDiag(ahy_px)*x)
elif 'zxy' in self.rxType:
Zij = sDiag(aHd*(-sDiag(ahx_py)*aex_px + sDiag(ahx_px)*aex_py))
ZijN_uV = lambda x:-aex_px_u(sDiag(ahx_py)*x) - ahx_py_u(sDiag(aex_px)*x) + ahx_px_u(sDiag(aex_py)*x) + aex_py_u(sDiag(ahx_px)*x)
elif 'zyx' in self.rxType:
Zij = sDiag(aHd*( sDiag(ahy_py)*aey_px - sDiag(ahy_px)*aey_py))
ZijN_uV = lambda x: aey_px_u(sDiag(ahy_py)*x) + ahy_py_u(sDiag(aey_px)*x) - ahy_px_u(sDiag(aey_py)*x) - aey_py_u(sDiag(ahy_px)*x)
elif 'zyy' in self.rxType:
Zij = sDiag(aHd*(-sDiag(ahx_py)*aey_px + sDiag(ahx_px)*aey_py))
ZijN_uV = lambda x:-aey_px_u(sDiag(ahx_py)*x) - ahx_py_u(sDiag(aey_px)*x) + ahx_px_u(sDiag(aey_py)*x) + aey_py_u(sDiag(ahx_px)*x)
# Calculate the complex derivative
PDeriv_real = ZijN_uV(aHd*v) - aHd_uV(Zij.T*aHd*v)#
# NOTE: Need to reshape the output to go from 2*nU array to a (nU,2) matrix for each polarization
# PDeriv_real = np.hstack((mkvc(PDeriv_real[:len(PDeriv_real)/2],2),mkvc(PDeriv_real[len(PDeriv_real)/2::],2)))
PDeriv_real = PDeriv_real.reshape((2,mesh.nE)).T
elif self.projType is 'T3D':
if self.locs.ndim == 3:
bFLocs = self.locs[:,:,1]
else:
bFLocs = self.locs
# Get the projection
Pbx = mesh.getInterpolationMat(bFLocs,'Fx')
Pby = mesh.getInterpolationMat(bFLocs,'Fy')
Pbz = mesh.getInterpolationMat(bFLocs,'Fz')
# Get the fields at location
# px: x-polaration and py: y-polaration.
abx_px = mkvc(mkvc(f[src,'b_px'],2).T*Pbx.T)
aby_px = mkvc(mkvc(f[src,'b_px'],2).T*Pby.T)
abz_px = mkvc(mkvc(f[src,'b_px'],2).T*Pbz.T)
abx_py = mkvc(mkvc(f[src,'b_py'],2).T*Pbx.T)
aby_py = mkvc(mkvc(f[src,'b_py'],2).T*Pby.T)
abz_py = mkvc(mkvc(f[src,'b_py'],2).T*Pbz.T)
# Derivatives as lambda functions
abx_px_u = lambda vec: f._b_pxDeriv_u(src,Pbx.T*vec,adjoint=True)
aby_px_u = lambda vec: f._b_pxDeriv_u(src,Pby.T*vec,adjoint=True)
abz_px_u = lambda vec: f._b_pxDeriv_u(src,Pbz.T*vec,adjoint=True)
abx_py_u = lambda vec: f._b_pyDeriv_u(src,Pbx.T*vec,adjoint=True)
aby_py_u = lambda vec: f._b_pyDeriv_u(src,Pby.T*vec,adjoint=True)
abz_py_u = lambda vec: f._b_pyDeriv_u(src,Pbz.T*vec,adjoint=True)
# Update the input vector
# Define shortcuts
sDiag = lambda t: Utils.sdiag(mkvc(t,2))
sVec = lambda t: Utils.sp.csr_matrix(mkvc(t,2))
# Define the components of the derivative
aHd = sDiag(1./(sDiag(abx_px)*aby_py - sDiag(abx_py)*aby_px))
aHd_uV = lambda x: abx_px_u(sDiag(aby_py)*x) + abx_px_u(sDiag(aby_py)*x) - aby_px_u(sDiag(abx_py)*x) - abx_py_u(sDiag(aby_px)*x)
# Need to fix this to reflect the adjoint
if 'tzx' in self.rxType:
Tij = sDiag(aHd*( -sDiag(abz_py)*aby_px + sDiag(abz_px)*aby_py))
TijN_uV = lambda x: -abz_py_u(sDiag(aby_px)*x) - aby_px_u(sDiag(abz_py)*x) + aby_py_u(sDiag(abz_px)*x) + abz_px_u(sDiag(aby_py)*x)
elif 'tzy' in self.rxType:
Tij = sDiag(aHd*( sDiag(abz_py)*abx_px - sDiag(abz_px)*abx_py))
TijN_uV = lambda x: abx_px_u(sDiag(abz_py)*x) + abz_py_u(sDiag(abx_px)*x) - abx_py_u(sDiag(abz_px)*x) - abz_px_u(sDiag(abx_py)*x)
# Calculate the complex derivative
PDeriv_real = TijN_uV(aHd*v) - aHd_uV(Tij.T*aHd*v)#
# NOTE: Need to reshape the output to go from 2*nU array to a (nU,2) matrix for each polarization
# PDeriv_real = np.hstack((mkvc(PDeriv_real[:len(PDeriv_real)/2],2),mkvc(PDeriv_real[len(PDeriv_real)/2::],2)))
PDeriv_real = PDeriv_real.reshape((2,mesh.nE)).T
# Extract the data
if real_or_imag == 'imag':
Pv = 1j*PDeriv_real
elif real_or_imag == 'real':
Pv = PDeriv_real.astype(complex)
return Pv
overb = (mesh.gridCC[:, 1] > -overburden_extent) & (mesh.gridCC[:, 1] <= 0)
mtrue[overb] = ln_over * np.ones_like(mtrue[overb]) + norm(
noisemean, noisevar).rvs(np.prod((mtrue[overb]).shape))
csph = (np.sqrt((mesh.gridCC[:, 1] - z0)**2. +
(mesh.gridCC[:, 0] - x0)**2.)) < r0
mtrue[csph] = ln_sigc * np.ones_like(mtrue[csph]) + norm(
noisemean, noisevar).rvs(np.prod((mtrue[csph]).shape))
#Define the sphere limit
rsph = (np.sqrt((mesh.gridCC[:, 1] - z1)**2. +
(mesh.gridCC[:, 0] - x1)**2.)) < r1
mtrue[rsph] = ln_sigr * np.ones_like(mtrue[rsph]) + norm(
noisemean, noisevar).rvs(np.prod((mtrue[rsph]).shape))
mtrue = Utils.mkvc(mtrue)
mesh.plotGrid()
plt.gca().set_xlim([-10, 10])
plt.gca().set_ylim([-10, 0])
xyzlim = np.r_[[[-10., 10.], [-10., 1.]]]
actind, meshCore = Utils.meshutils.ExtractCoreMesh(xyzlim, mesh)
plt.hist(mtrue[actind], bins=50, normed=True)
fig0 = plt.figure()
ax0 = fig0.add_subplot(111)
mm = meshCore.plotImage(mtrue[actind], ax=ax0)
plt.colorbar(mm[0])
ax0.set_aspect("equal")
#plt.show()
def run(plotIt=True):
"""
Mesh: Plotting with defining range
==================================
When using a large Mesh with the cylindrical code, it is advantageous
to define a :code:`range_x` and :code:`range_y` when plotting with
vectors. In this case, only the region inside of the range is
interpolated. In particular, you often want to ignore padding cells.
"""
# ## Model Parameters
#
# We define a
# - resistive halfspace and
# - conductive sphere
# - radius of 30m
# - center is 50m below the surface
# electrical conductivities in S/m
sig_halfspace = 1e-6
sig_sphere = 1e0
sig_air = 1e-8
# depth to center, radius in m
sphere_z = -50.
sphere_radius = 30.
# ## Survey Parameters
#
# - Transmitter and receiver 20m above the surface
# - Receiver offset from transmitter by 8m horizontally
# - 25 frequencies, logaritmically between $10$ Hz and $10^5$ Hz
boom_height = 20.
rx_offset = 8.
freqs = np.r_[1e1, 1e5]
# source and receiver location in 3D space
src_loc = np.r_[0., 0., boom_height]
rx_loc = np.atleast_2d(np.r_[rx_offset, 0., boom_height])
# print the min and max skin depths to make sure mesh is fine enough and
# extends far enough
def skin_depth(sigma, f):
return 500. / np.sqrt(sigma * f)
print('Minimum skin depth (in sphere): {:.2e} m '.format(
skin_depth(sig_sphere, freqs.max())))
print('Maximum skin depth (in background): {:.2e} m '.format(
skin_depth(sig_halfspace, freqs.min())))
# ## Mesh
#
# Here, we define a cylindrically symmetric tensor mesh.
#
# ### Mesh Parameters
#
# For the mesh, we will use a cylindrically symmetric tensor mesh. To
# construct a tensor mesh, all that is needed is a vector of cell widths in
# the x and z-directions. We will define a core mesh region of uniform cell
# widths and a padding region where the cell widths expand "to infinity".
# x-direction
csx = 2 # core mesh cell width in the x-direction
ncx = np.ceil(
1.2 * sphere_radius / csx
) # number of core x-cells (uniform mesh slightly beyond sphere radius)
npadx = 50 # number of x padding cells
# z-direction
csz = 1 # core mesh cell width in the z-direction
ncz = np.ceil(
1.2 * (boom_height - (sphere_z - sphere_radius)) / csz
) # number of core z-cells (uniform cells slightly below bottom of sphere)
npadz = 52 # number of z padding cells
# padding factor (expand cells to infinity)
pf = 1.3
# cell spacings in the x and z directions
hx = Utils.meshTensor([(csx, ncx), (csx, npadx, pf)])
hz = Utils.meshTensor([(csz, npadz, -pf), (csz, ncz), (csz, npadz, pf)])
# define a SimPEG mesh
mesh = Mesh.CylMesh([hx, 1, hz],
x0=np.r_[0., 0., -hz.sum() / 2. - boom_height])
# ### Plot the mesh
#
# Below, we plot the mesh. The cyl mesh is rotated around x=0. Ensure that
# each dimension extends beyond the maximum skin depth.
#
# Zoom in by changing the xlim and zlim.
# X and Z limits we want to plot to. Try
xlim = np.r_[0., 2.5e6]
zlim = np.r_[-2.5e6, 2.5e6]
fig, ax = plt.subplots(1, 1)
mesh.plotGrid(ax=ax)
ax.set_title('Simulation Mesh')
ax.set_xlim(xlim)
ax.set_ylim(zlim)
print('The maximum skin depth is (in background): {:.2e} m. '
'Does the mesh go sufficiently past that?'.format(
skin_depth(sig_halfspace, freqs.min())))
# ## Put Model on Mesh
#
# Now that the model parameters and mesh are defined, we can define
# electrical conductivity on the mesh.
#
# The electrical conductivity is defined at cell centers when using the
# finite volume method. So here, we define a vector that contains an
# electrical conductivity value for every cell center.
# create a vector that has one entry for every cell center
sigma = sig_air * np.ones(
mesh.nC) # start by defining the conductivity of the air everwhere
sigma[mesh.gridCC[:, 2] <
0.] = sig_halfspace # assign halfspace cells below the earth
# indices of the sphere (where (x-x0)**2 + (z-z0)**2 <= R**2)
sphere_ind = ((mesh.gridCC[:, 0]**2 +
(mesh.gridCC[:, 2] - sphere_z)**2) <= sphere_radius**2)
sigma[sphere_ind] = sig_sphere # assign the conductivity of the sphere
# Plot a cross section of the conductivity model
fig, ax = plt.subplots(1, 1)
cb = plt.colorbar(mesh.plotImage(np.log10(sigma), ax=ax, mirror=True)[0])
# plot formatting and titles
cb.set_label('$\log_{10}\sigma$', fontsize=13)
ax.axis('equal')
ax.set_xlim([-120., 120.])
ax.set_ylim([-100., 30.])
ax.set_title('Conductivity Model')
# ## Set up the Survey
#
# Here, we define sources and receivers. For this example, the receivers
# are magnetic flux recievers, and are only looking at the secondary field
# (eg. if a bucking coil were used to cancel the primary). The source is a
# vertical magnetic dipole with unit moment.
# Define the receivers, we will sample the real secondary magnetic flux
# density as well as the imaginary magnetic flux density
bz_r = FDEM.Rx.Point_bSecondary(
locs=rx_loc, orientation='z',
component='real') # vertical real b-secondary
bz_i = FDEM.Rx.Point_b(
locs=rx_loc, orientation='z',
component='imag') # vertical imag b (same as b-secondary)
rxList = [bz_r, bz_i] # list of receivers
# Define the list of sources - one source for each frequency. The source is
# a point dipole oriented in the z-direction
srcList = [
FDEM.Src.MagDipole(rxList, f, src_loc, orientation='z') for f in freqs
]
print(
'There are {nsrc} sources (same as the number of frequencies - {nfreq}). '
'Each source has {nrx} receivers sampling the resulting b-fields'.
format(nsrc=len(srcList), nfreq=len(freqs), nrx=len(rxList)))
# ## Set up Forward Simulation
#
# A forward simulation consists of a paired SimPEG problem and Survey.
# For this example, we use the E-formulation of Maxwell's equations,
# solving the second-order system for the electric field, which is defined
# on the cell edges of the mesh. This is the `prob` variable below. The
# `survey` takes the source list which is used to construct the RHS for the
# problem. The source list also contains the receiver information, so the
# `survey` knows how to sample fields and fluxes that are produced by
# solving the `prob`.
# define a problem - the statement of which discrete pde system we want to
# solve
prob = FDEM.Problem3D_e(mesh, sigmaMap=Maps.IdentityMap(mesh))
prob.solver = Solver
survey = FDEM.Survey(srcList)
# tell the problem and survey about each other - so the RHS can be
# constructed for the problem and the
# resulting fields and fluxes can be sampled by the receiver.
prob.pair(survey)
# ### Solve the forward simulation
#
# Here, we solve the problem for the fields everywhere on the mesh.
fields = prob.fields(sigma)
# ### Plot the fields
#
# Lets look at the physics!
# log-scale the colorbar
from matplotlib.colors import LogNorm
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
def plotMe(field, ax):
plt.colorbar(mesh.plotImage(field,
vType='F',
view='vec',
range_x=[-100., 100.],
range_y=[-180., 60.],
pcolorOpts={
'norm': LogNorm(),
'cmap': plt.get_cmap('viridis')
},
streamOpts={'color': 'k'},
ax=ax,
mirror=True)[0],
ax=ax)
plotMe(fields[srcList[0], 'bSecondary'].real, ax[0])
ax[0].set_title('Real B-Secondary, {}Hz'.format(freqs[0]))
plotMe(fields[srcList[1], 'bSecondary'].real, ax[1])
ax[1].set_title('Real B-Secondary, {}Hz'.format(freqs[1]))
plt.tight_layout()
if plotIt:
plt.show()
invProb = InvProblem.BaseInvProblem(dmis, regT, opt)
# Directives for Inversions
beta = Directives.BetaEstimate_ByEig(beta0_ratio=1e+1)
Target = Directives.TargetMisfit()
betaSched = Directives.BetaSchedule(coolingFactor=5., coolingRate=2)
inv = Inversion.BaseInversion(invProb, directiveList=[beta, Target, betaSched])
# Run Inversion
minv = inv.run(m0)
# Final Plot
############
fig, ax = plt.subplots(2, 2, figsize=(12, 6))
ax = Utils.mkvc(ax)
cyl0v = getCylinderPoints(x0, z0, r0)
cyl1v = getCylinderPoints(x1, z1, r1)
cyl0h = getCylinderPoints(x0, y0, r0)
cyl1h = getCylinderPoints(x1, y1, r1)
clim = [(mtrue[actind]).min(), (mtrue[actind]).max()]
dat = meshCore.plotSlice(((mtrue[actind])),
ax=ax[0],
normal='Y',
clim=clim,
ind=int(ncy / 2))
ax[0].set_title('Ground Truth, Vertical')