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 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_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_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_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 setUpClass(self):
print('\n------- Testing Primary Secondary Source HJ -> EB --------\n')
# receivers
self.rxlist = []
for rxtype in ['b', 'e']:
rx = getattr(FDEM.Rx, 'Point_{}'.format(rxtype))
for orientation in ['x', 'y', 'z']:
for comp in ['real', 'imag']:
self.rxlist.append(rx(rx_locs, component=comp,
orientation=orientation))
# primary
self.primaryProblem = FDEM.Problem3D_j(meshp, sigmaMap=primaryMapping)
self.primaryProblem.solver = Solver
s_e = np.zeros(meshp.nF)
inds = meshp.nFx + Utils.closestPoints(meshp, src_loc, gridLoc='Fz')
s_e[inds] = 1./csz
primarySrc = FDEM.Src.RawVec_e(
self.rxlist, freq=freq, s_e=s_e/meshp.area
)
self.primarySurvey = FDEM.Survey([primarySrc])
# Secondary Problem
self.secondaryProblem = FDEM.Problem3D_e(meshs, sigmaMap=mapping)
self.secondaryProblem.Solver = Solver
self.secondarySrc = FDEM.Src.PrimSecMappedSigma(
self.rxlist, freq, self.primaryProblem,
self.primarySurvey, primaryMap2Meshs
)
self.secondarySurvey = FDEM.Survey([self.secondarySrc])
self.secondaryProblem.pair(self.secondarySurvey)
# Full 3D problem to compare with
self.problem3D = FDEM.Problem3D_e(meshs, sigmaMap=mapping)
self.problem3D.Solver = Solver
s_e3D = np.zeros(meshs.nE)
inds = (meshs.nEx + meshs.nEy +
Utils.closestPoints(meshs, src_loc, gridLoc='Ez'))
s_e3D[inds] = [1./(len(inds))] * len(inds)
self.problem3D.model = model
src3D = FDEM.Src.RawVec_e(self.rxlist, freq=freq, s_e=s_e3D)
self.survey3D = FDEM.Survey([src3D])
self.problem3D.pair(self.survey3D)
# solve and store fields
print(' solving primary - secondary')
self.fields_primsec = self.secondaryProblem.fields(model)
print(' ... done')
self.fields_primsec = self.secondaryProblem.fields(model)
print(' solving 3D')
self.fields_3D = self.problem3D.fields(model)
print(' ... done')
return None
def setUpClass(self):
print('\n------- Testing Primary Secondary Source HJ -> EB --------\n')
# receivers
self.rxlist = []
for rxtype in ['b', 'e']:
rx = getattr(FDEM.Rx, 'Point_{}'.format(rxtype))
for orientation in ['x', 'y', 'z']:
for comp in ['real', 'imag']:
self.rxlist.append(rx(rx_locs, component=comp,
orientation=orientation))
# primary
self.primaryProblem = FDEM.Problem3D_j(meshp, sigmaMap=primaryMapping)
self.primaryProblem.solver = Solver
s_e = np.zeros(meshp.nF)
inds = meshp.nFx + Utils.closestPoints(meshp, src_loc, gridLoc='Fz')
s_e[inds] = 1./csz
primarySrc = FDEM.Src.RawVec_e(
self.rxlist, freq=freq, s_e=s_e/meshp.area
)
self.primarySurvey = FDEM.Survey([primarySrc])
# Secondary Problem
self.secondaryProblem = FDEM.Problem3D_e(meshs, sigmaMap=mapping)
self.secondaryProblem.Solver = Solver
self.secondarySrc = FDEM.Src.PrimSecMappedSigma(
self.rxlist, freq, self.primaryProblem,
self.primarySurvey, primaryMap2Meshs
)
self.secondarySurvey = FDEM.Survey([self.secondarySrc])
self.secondaryProblem.pair(self.secondarySurvey)
# Full 3D problem to compare with
self.problem3D = FDEM.Problem3D_e(meshs, sigmaMap=mapping)
self.problem3D.Solver = Solver
s_e3D = np.zeros(meshs.nE)
inds = (meshs.nEx + meshs.nEy +
Utils.closestPoints(meshs, src_loc, gridLoc='Ez'))
s_e3D[inds] = [1./(len(inds))] * len(inds)
self.problem3D.model = model
src3D = FDEM.Src.RawVec_e(self.rxlist, freq=freq, s_e=s_e3D)
self.survey3D = FDEM.Survey([src3D])
self.problem3D.pair(self.survey3D)
# solve and store fields
print(' solving primary - secondary')
self.fields_primsec = self.secondaryProblem.fields(model)
print(' ... done')
self.fields_primsec = self.secondaryProblem.fields(model)
print(' solving 3D')
self.fields_3D = self.problem3D.fields(model)
print(' ... done')
return None
def setUp(self):
mesh = Mesh.TensorMesh([20, 20, 20], "CCN")
sigma = np.ones(mesh.nC) * 1. / 100.
actind = mesh.gridCC[:, 2] < -0.2
# actMap = Maps.InjectActiveCells(mesh, actind, 0.)
xyzM = Utils.ndgrid(
np.ones_like(mesh.vectorCCx[:-1]) * -0.4,
np.ones_like(mesh.vectorCCy) * -0.4, np.r_[-0.3])
xyzN = Utils.ndgrid(mesh.vectorCCx[1:], mesh.vectorCCy, np.r_[-0.3])
problem = SP.Problem_CC(mesh,
sigma=sigma,
qMap=Maps.IdentityMap(mesh),
Solver=PardisoSolver)
rx = SP.Rx.Dipole(xyzN, xyzM)
src = SP.Src.StreamingCurrents([rx],
L=np.ones(mesh.nC),
mesh=mesh,
modelType="CurrentSource")
survey = SP.Survey([src])
survey.pair(problem)
q = np.zeros(mesh.nC)
inda = Utils.closestPoints(mesh, np.r_[-0.5, 0., -0.8])
indb = Utils.closestPoints(mesh, np.r_[0.5, 0., -0.8])
q[inda] = 1.
q[indb] = -1.
mSynth = q.copy()
survey.makeSyntheticData(mSynth)
# Now set up the problem to do some minimization
dmis = DataMisfit.l2_DataMisfit(survey)
reg = Regularization.Simple(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=1e-2)
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 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 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 setUp(self):
mesh = Mesh.TensorMesh([20, 20, 20], "CCN")
sigma = np.ones(mesh.nC)*1./100.
actind = mesh.gridCC[:, 2] < -0.2
# actMap = Maps.InjectActiveCells(mesh, actind, 0.)
xyzM = Utils.ndgrid(np.ones_like(mesh.vectorCCx[:-1])*-0.4, np.ones_like(mesh.vectorCCy)*-0.4, np.r_[-0.3])
xyzN = Utils.ndgrid(mesh.vectorCCx[1:], mesh.vectorCCy, np.r_[-0.3])
problem = SP.Problem_CC(mesh, sigma=sigma, qMap=Maps.IdentityMap(mesh), Solver=PardisoSolver)
rx = SP.Rx.Dipole(xyzN, xyzM)
src = SP.Src.StreamingCurrents([rx], L=np.ones(mesh.nC), mesh=mesh,
modelType="CurrentSource")
survey = SP.Survey([src])
survey.pair(problem)
q = np.zeros(mesh.nC)
inda = Utils.closestPoints(mesh, np.r_[-0.5, 0., -0.8])
indb = Utils.closestPoints(mesh, np.r_[0.5, 0., -0.8])
q[inda] = 1.
q[indb] = -1.
mSynth = q.copy()
survey.makeSyntheticData(mSynth)
# Now set up the problem to do some minimization
dmis = DataMisfit.l2_DataMisfit(survey)
reg = Regularization.Simple(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=1e-2)
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 solve_2D_potentials(rho1, rho2, h, A, B):
"""
Here we solve the 2D DC problem for potentials (using SimPEG Mesg Class)
"""
sigma = 1. / rho2 * np.ones(mesh.nC)
sigma[mesh.gridCC[:, 1] >= -h] = 1. / rho1 # since the model is 2D
q = np.zeros(mesh.nC)
a = Utils.closestPoints(mesh, A[:2])
b = Utils.closestPoints(mesh, B[:2])
q[a] = 1. / mesh.vol[a]
q[b] = -1. / mesh.vol[b]
# q = q * 1./mesh.vol
A = (mesh.cellGrad.T * Utils.sdiag(1. / (mesh.dim * mesh.aveF2CC.T *
(1. / sigma))) * mesh.cellGrad)
Ainv = SolverLU(A)
V = Ainv * q
return V
def drapeTopotoLoc(mesh, topo, pts):
"""
Drape
"""
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()
airind = Utils.surface2ind_topo(mesh, topo)
meshtemp, topoCC = gettopoCC(mesh, ~airind)
inds = Utils.closestPoints(meshtemp, pts)
return np.c_[pts, topoCC[inds]]
def drapeTopotoLoc(mesh, topo, pts):
"""
Drape
"""
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()
airind = Utils.surface2ind_topo(mesh, topo)
meshtemp, topoCC = gettopoCC(mesh, ~airind)
inds = Utils.closestPoints(meshtemp, pts)
return np.c_[pts, topoCC[inds]]
def get_Surface_Potentials(mtrue, survey, src, field_obj):
phi = field_obj["phi"]
# CCLoc = mesh.gridCC
XLoc = np.unique(mesh.gridCC[:, 0])
surfaceInd, zsurfaceLoc = get_Surface(mtrue, XLoc)
phiSurface = phi[surfaceInd]
phiScale = 0.0
if survey == "Pole-Dipole" or survey == "Pole-Pole":
refInd = Utils.closestPoints(mesh, [xmax + 60.0, 0.0], gridLoc="CC")
# refPoint = CCLoc[refInd]
# refSurfaceInd = np.where(xSurface == refPoint[0])
# phiScale = np.median(phiSurface)
phiScale = phi[refInd]
phiSurface = phiSurface - phiScale
return XLoc, phiSurface, phiScale
def get_Surface_Potentials(survey, src, field_obj):
phi = field_obj['phi']
CCLoc = mesh.gridCC
zsurfaceLoc = np.max(CCLoc[:, 1])
surfaceInd = np.where(CCLoc[:, 1] == zsurfaceLoc)
xSurface = CCLoc[surfaceInd, 0].T
phiSurface = phi[surfaceInd]
phiScale = 0.
if (survey == "Pole-Dipole" or survey == "Pole-Pole"):
refInd = Utils.closestPoints(mesh, [xmax + 60., 0.], gridLoc='CC')
# refPoint = CCLoc[refInd]
# refSurfaceInd = np.where(xSurface == refPoint[0])
# phiScale = np.median(phiSurface)
phiScale = phi[refInd]
phiSurface = phiSurface - phiScale
return xSurface, phiSurface, phiScale
def setUp(self):
cs = 10.
ncx, ncy, ncz = 30., 30., 30.
npad = 10.
hx = [(cs, npad, -1.5), (cs, ncx), (cs, npad, 1.5)]
hy = [(cs, npad, -1.5), (cs, ncy), (cs, npad, 1.5)]
hz = [(cs, npad, -1.5), (cs, ncz), (cs, npad, 1.5)]
self.mesh = Mesh.TensorMesh([hx, hy, hz], 'CCC')
mapping = Maps.ExpMap(self.mesh)
self.freq = 1.
self.prob_e = FDEM.Problem3D_e(self.mesh, mapping=mapping)
self.prob_b = FDEM.Problem3D_b(self.mesh, mapping=mapping)
self.prob_h = FDEM.Problem3D_h(self.mesh, mapping=mapping)
self.prob_j = FDEM.Problem3D_j(self.mesh, mapping=mapping)
loc = np.r_[0., 0., 0.]
self.loc = Utils.mkvc(
self.mesh.gridCC[Utils.closestPoints(self.mesh, loc, 'CC'), :])
def setUp(self):
cs = 10.
ncx, ncy, ncz = 30., 30., 30.
npad = 10.
hx = [(cs, npad, -1.5), (cs, ncx), (cs, npad, 1.5)]
hy = [(cs, npad, -1.5), (cs, ncy), (cs, npad, 1.5)]
hz = [(cs, npad, -1.5), (cs, ncz), (cs, npad, 1.5)]
self.mesh = Mesh.TensorMesh([hx, hy, hz], 'CCC')
mapping = Maps.ExpMap(self.mesh)
self.freq = 1.
self.prob_e = FDEM.Problem3D_e(self.mesh, sigmaMap=mapping)
self.prob_b = FDEM.Problem3D_b(self.mesh, sigmaMap=mapping)
self.prob_h = FDEM.Problem3D_h(self.mesh, sigmaMap=mapping)
self.prob_j = FDEM.Problem3D_j(self.mesh, sigmaMap=mapping)
loc = np.r_[0., 0., 0.]
self.loc = Utils.mkvc(
self.mesh.gridCC[Utils.closestPoints(self.mesh, loc, 'CC'), :]
)
def getFDEMProblem(fdemType, comp, SrcList, freq, useMu=False, verbose=False):
cs = 10.
ncx, ncy, ncz = 0, 0, 0
npad = 8
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], ['C', 'C', 'C'])
if useMu is True:
mapping = [('sigma', Maps.ExpMap(mesh)),
('mu', Maps.IdentityMap(mesh))]
else:
mapping = Maps.ExpMap(mesh)
x = np.array(
[np.linspace(-5. * cs, -2. * cs, 3),
np.linspace(5. * cs, 2. * cs, 3)]
) + cs / 4. #don't sample right by the source, slightly off alignment from either staggered grid
XYZ = Utils.ndgrid(x, x, np.linspace(-2. * cs, 2. * cs, 5))
Rx0 = getattr(EM.FDEM.Rx, 'Point_' + comp[0])
if comp[2] == 'r':
real_or_imag = 'real'
elif comp[2] == 'i':
real_or_imag = 'imag'
rx0 = Rx0(XYZ, comp[1], 'imag')
Src = []
for SrcType in SrcList:
if SrcType is 'MagDipole':
Src.append(
EM.FDEM.Src.MagDipole([rx0], freq=freq, loc=np.r_[0., 0., 0.]))
elif SrcType is 'MagDipole_Bfield':
Src.append(
EM.FDEM.Src.MagDipole_Bfield([rx0],
freq=freq,
loc=np.r_[0., 0., 0.]))
elif SrcType is 'CircularLoop':
Src.append(
EM.FDEM.Src.CircularLoop([rx0],
freq=freq,
loc=np.r_[0., 0., 0.]))
elif SrcType is 'RawVec':
if fdemType is 'e' or fdemType is 'b':
S_m = np.zeros(mesh.nF)
S_e = np.zeros(mesh.nE)
S_m[Utils.closestPoints(mesh, [0., 0., 0.], 'Fz') +
np.sum(mesh.vnF[:1])] = 1e-3
S_e[Utils.closestPoints(mesh, [0., 0., 0.], 'Ez') +
np.sum(mesh.vnE[:1])] = 1e-3
Src.append(
EM.FDEM.Src.RawVec([rx0], freq, S_m,
mesh.getEdgeInnerProduct() * S_e))
elif fdemType is 'h' or fdemType is 'j':
S_m = np.zeros(mesh.nE)
S_e = np.zeros(mesh.nF)
S_m[Utils.closestPoints(mesh, [0., 0., 0.], 'Ez') +
np.sum(mesh.vnE[:1])] = 1e-3
S_e[Utils.closestPoints(mesh, [0., 0., 0.], 'Fz') +
np.sum(mesh.vnF[:1])] = 1e-3
Src.append(
EM.FDEM.Src.RawVec([rx0], freq,
mesh.getEdgeInnerProduct() * S_m, S_e))
if verbose:
print(' Fetching {0!s} problem'.format((fdemType)))
if fdemType == 'e':
survey = EM.FDEM.Survey(Src)
prb = EM.FDEM.Problem3D_e(mesh, mapping=mapping)
elif fdemType == 'b':
survey = EM.FDEM.Survey(Src)
prb = EM.FDEM.Problem3D_b(mesh, mapping=mapping)
elif fdemType == 'j':
survey = EM.FDEM.Survey(Src)
prb = EM.FDEM.Problem3D_j(mesh, mapping=mapping)
elif fdemType == 'h':
survey = EM.FDEM.Survey(Src)
prb = EM.FDEM.Problem3D_h(mesh, mapping=mapping)
else:
raise NotImplementedError()
prb.pair(survey)
try:
from pymatsolver import PardisoSolver
prb.Solver = PardisoSolver
except ImportError:
prb.Solver = SolverLU
# prb.solverOpts = dict(check_accuracy=True)
return prb
ndata = rxLoc.shape[0]
#%% Subdivide the forward data locations into tiles
PF.Magnetics.plot_obs_2D(rxLoc)
xmin = np.min(rxLoc[:, 0])
xmax = np.max(rxLoc[:, 0])
ymin = np.min(rxLoc[:, 1])
ymax = np.max(rxLoc[:, 1])
var = np.c_[np.r_[xmin, ymin], np.r_[xmax, ymin], np.r_[xmin, ymax]]
var = np.c_[var.T, np.ones(3) * mesh.vectorCCz[-1]]
# First put a tile on both corners of the mesh
indx = Utils.closestPoints(mesh, var)
endl = np.c_[mesh.gridCC[indx, 0], mesh.gridCC[indx, 1]]
dx = np.median(mesh.hx)
# Add intermediate tiles until the min_Olap is respected
# First in the x-direction
lx = np.floor((max_mcell / mesh.nCz)**0.5)
ntile = 2
Olap = -1
while Olap < min_Olap:
ntile += 1
# Set location of SW corners
def run(loc=None, sig=None, radi=None, param=None, stype='dpdp', plotIt=True):
"""
DC Forward Simulation
=====================
Forward model conductive spheres in a half-space and plot a pseudo-section
Created by @fourndo on Mon Feb 01 19:28:06 2016
"""
assert stype in ['pdp', 'dpdp'], "Source type (stype) must be pdp or dpdp (pole dipole or dipole dipole)"
if loc is None:
loc = np.c_[[-50.,0.,-50.],[50.,0.,-50.]]
if sig is None:
sig = np.r_[1e-2,1e-1,1e-3]
if radi is None:
radi = np.r_[25.,25.]
if param is None:
param = np.r_[30.,30.,5]
# First we need to create a mesh and a model.
# This is our mesh
dx = 5.
hxind = [(dx,15,-1.3), (dx, 75), (dx,15,1.3)]
hyind = [(dx,15,-1.3), (dx, 10), (dx,15,1.3)]
hzind = [(dx,15,-1.3),(dx, 15)]
mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')
# Set background conductivity
model = np.ones(mesh.nC) * sig[0]
# First anomaly
ind = Utils.ModelBuilder.getIndicesSphere(loc[:,0],radi[0],mesh.gridCC)
model[ind] = sig[1]
# Second anomaly
ind = Utils.ModelBuilder.getIndicesSphere(loc[:,1],radi[1],mesh.gridCC)
model[ind] = sig[2]
# Get index of the center
indy = int(mesh.nCy/2)
# Plot the model for reference
# Define core mesh extent
xlim = 200
zlim = 125
# Specify the survey type: "pdp" | "dpdp"
# Then specify the end points of the survey. Let's keep it simple for now and survey above the anomalies, top of the mesh
ends = [(-175,0),(175,0)]
ends = np.c_[np.asarray(ends),np.ones(2).T*mesh.vectorNz[-1]]
# Snap the endpoints to the grid. Easier to create 2D section.
indx = Utils.closestPoints(mesh, ends )
locs = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*mesh.vectorNz[-1]]
# We will handle the geometry of the survey for you and create all the combination of tx-rx along line
# [Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, stype, param[0], param[1], param[2])
survey, Tx, Rx = DC.gen_DCIPsurvey(locs, mesh, stype, param[0], param[1], param[2])
# Define some global geometry
dl_len = np.sqrt( np.sum((locs[0,:] - locs[1,:])**2) )
dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
dl_y = ( Tx[-1][1,1] - Tx[0][1,0] ) / dl_len
azm = np.arctan(dl_y/dl_x)
#Set boundary conditions
mesh.setCellGradBC('neumann')
# Define the differential operators needed for the DC problem
Div = mesh.faceDiv
Grad = mesh.cellGrad
Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
A = Div*Msig*Grad
# Change one corner to deal with nullspace
A[0,0] = 1
A = sp.csc_matrix(A)
# We will solve the system iteratively, so a pre-conditioner is helpful
# This is simply a Jacobi preconditioner (inverse of the main diagonal)
dA = A.diagonal()
P = sp.spdiags(1/dA,0,A.shape[0],A.shape[0])
# Now we can solve the system for all the transmitters
# We want to store the data
data = []
# There is probably a more elegant way to do this, but we can just for-loop through the transmitters
for ii in range(len(Tx)):
start_time = time.time() # Let's time the calculations
#print("Transmitter %i / %i\r" % (ii+1,len(Tx)))
# Select dipole locations for receiver
rxloc_M = np.asarray(Rx[ii][:,0:3])
rxloc_N = np.asarray(Rx[ii][:,3:])
# For usual cases "dpdp" or "gradient"
if stype == 'pdp':
# Create an "inifinity" pole
tx = np.squeeze(Tx[ii][:,0:1])
tinf = tx + np.array([dl_x,dl_y,0])*dl_len*2
inds = Utils.closestPoints(mesh, np.c_[tx,tinf].T)
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1] / mesh.vol[inds] )
else:
inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T )
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T*( [-1,1] / mesh.vol[inds] )
# Iterative Solve
Ainvb = sp.linalg.bicgstab(P*A,P*RHS, tol=1e-5)
# We now have the potential everywhere
phi = Utils.mkvc(Ainvb[0])
# Solve for phi on pole locations
P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
# Compute the potential difference
dtemp = (P1*phi - P2*phi)*np.pi
data.append( dtemp )
print '\rTransmitter {0} of {1} -> Time:{2} sec'.format(ii,len(Tx),time.time()- start_time),
print 'Transmitter {0} of {1}'.format(ii,len(Tx))
print 'Forward completed'
# Let's just convert the 3D format into 2D (distance along line) and plot
# [Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(survey, np.ones(survey.nSrc))
survey2D = DC.convertObs_DC3D_to_2D(survey, np.ones(survey.nSrc))
survey2D.dobs =np.hstack(data)
# Here is an example for the first tx-rx array
if plotIt:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.subplot(2,1,1, aspect='equal')
mesh.plotSlice(np.log10(model), ax =ax, normal = 'Y', ind = indy,grid=True)
ax.set_title('E-W section at '+str(mesh.vectorCCy[indy])+' m')
plt.gca().set_aspect('equal', adjustable='box')
plt.scatter(Tx[0][0,:],Tx[0][2,:],s=40,c='g', marker='v')
plt.scatter(Rx[0][:,0::3],Rx[0][:,2::3],s=40,c='y')
plt.xlim([-xlim,xlim])
plt.ylim([-zlim,mesh.vectorNz[-1]+dx])
ax = plt.subplot(2,1,2, aspect='equal')
# Plot the location of the spheres for reference
circle1=plt.Circle((loc[0,0]-Tx[0][0,0],loc[2,0]),radi[0],color='w',fill=False, lw=3)
circle2=plt.Circle((loc[0,1]-Tx[0][0,0],loc[2,1]),radi[1],color='k',fill=False, lw=3)
ax.add_artist(circle1)
ax.add_artist(circle2)
# Add the speudo section
DC.plot_pseudoSection(survey2D,ax,stype)
# plt.scatter(Tx2d[0][:],Tx[0][2,:],s=40,c='g', marker='v')
# plt.scatter(Rx2d[0][:],Rx[0][:,2::3],s=40,c='y')
# plt.plot(np.r_[Tx2d[0][0],Rx2d[-1][-1,-1]],np.ones(2)*mesh.vectorNz[-1], color='k')
plt.ylim([-zlim,mesh.vectorNz[-1]+dx])
plt.show()
return fig, ax
# Autogenerated at 2018-01-31T11:25:30.354645 on version 0.0.4
import casingSimulations
import numpy as np
# Set up the simulation
sim = casingSimulations.run.SimulationTDEM(
modelParameters='ModelParameters.json',
meshGenerator='MeshParameters.json',
src='Source.json',
fields_filename='fields.npy')
# run the simulation
fields = sim.run()
# where we are going to evaluate the fields
from SimPEG import Utils
x_locs = np.r_[5, 11, 51, 101, 151, 201, 251, 301]
y_locs = np.r_[np.pi / 2.]
z_locs = np.r_[sim.src.src_a[2]]
points = Utils.closestPoints(sim.meshGenerator.mesh,
Utils.ndgrid(x_locs, y_locs, z_locs), 'Fx')
j_compare = fields[:, 'j', :]
e_compare = sim.prob.MfI * sim.prob.MfRho * j_compare
jx_compare = j_compare[points, :]
ex_compare = e_compare[points, :]
np.save('jx_compare.npy', jx_compare)
np.save('ex_compare.npy', ex_compare)
if stype == 'dipole-dipole':
axs.scatter(tx[0, 0], tx[0, 2], c='r', s=75, marker='v')
axs.scatter(tx[0, 1], tx[1, 2], c='b', s=75, marker='v')
else:
axs.scatter(tx[0], tx[2], c='r', s=75, marker='v')
#cfm1=get_current_fig_manager().window
#%%
# Add z coordinate to all survey... assume flat
nz = mesh.vectorNz
var = np.c_[np.asarray(srvy_end), np.ones(2).T * nz[-1]]
# Snap the endpoints to the grid. Easier to create 2D section.
indx = Utils.closestPoints(mesh, var)
endl = np.c_[mesh.gridCC[indx, 0], mesh.gridCC[indx, 1], np.ones(2).T * nz[-1]]
survey = StaticUtils.gen_DCIPsurvey(endl, mesh, stype, a, b, n)
#survey = DC.SurveyDC.Survey(srcList)
Tx = StaticUtils.getSrc_locs(survey)
dl_len = np.sqrt(np.sum((endl[0, :] - endl[1, :])**2))
dl_x = (Tx[-1][0] - Tx[0][0]) / dl_len
dl_y = (Tx[-1][1] - Tx[0][1]) / dl_len
azm = np.arctan(dl_y / dl_x)
# Plot stations along line
if stype == 'gradient':
Rx = survey.srcList[0].rxList[0].locs
def setupProblem(
mesh, muMod, sigmaMod, prbtype='e', invertMui=False,
sigmaInInversion=False, freq=1.
):
rxcomp = ['real', 'imag']
loc = Utils.ndgrid(
[mesh.vectorCCx, np.r_[0.], mesh.vectorCCz]
)
if prbtype in ['e', 'b']:
rxfields_y = ['e', 'j']
rxfields_xz = ['b', 'h']
elif prbtype in ['h', 'j']:
rxfields_y = ['b', 'h']
rxfields_xz = ['e', 'j']
rxList_edge = [
getattr(FDEM.Rx, 'Point_{f}'.format(f=f))(
loc, component=comp, orientation=orient
)
for f in rxfields_y
for comp in rxcomp
for orient in ['y']
]
rxList_face = [
getattr(FDEM.Rx, 'Point_{f}'.format(f=f))(
loc, component=comp, orientation=orient
)
for f in rxfields_xz
for comp in rxcomp
for orient in ['x', 'z']
]
rxList = rxList_edge + rxList_face
src_loc = np.r_[0., 0., 0.]
if prbtype in ['e', 'b']:
src = FDEM.Src.MagDipole(
rxList=rxList, loc=src_loc, freq=freq
)
elif prbtype in ['h', 'j']:
ind = Utils.closestPoints(mesh, src_loc, 'Fz') + mesh.vnF[0]
vec = np.zeros(mesh.nF)
vec[ind] = 1.
src = FDEM.Src.RawVec_e(rxList=rxList, freq=freq, s_e=vec)
survey = FDEM.Survey([src])
if sigmaInInversion:
wires = Maps.Wires(
('mu', mesh.nC),
('sigma', mesh.nC)
)
muMap = Maps.MuRelative(mesh) * wires.mu
sigmaMap = Maps.ExpMap(mesh) * wires.sigma
if invertMui:
muiMap = Maps.ReciprocalMap(mesh)*muMap
prob = getattr(FDEM, 'Problem3D_{}'.format(prbtype))(
mesh, muiMap=muiMap, sigmaMap=sigmaMap
)
# m0 = np.hstack([1./muMod, sigmaMod])
else:
prob = getattr(FDEM, 'Problem3D_{}'.format(prbtype))(
mesh, muMap=muMap, sigmaMap=sigmaMap
)
m0 = np.hstack([muMod, sigmaMod])
else:
muMap = Maps.MuRelative(mesh)
if invertMui:
muiMap = Maps.ReciprocalMap(mesh) * muMap
prob = getattr(FDEM, 'Problem3D_{}'.format(prbtype))(
mesh, sigma=sigmaMod, muiMap=muiMap
)
# m0 = 1./muMod
else:
prob = getattr(FDEM, 'Problem3D_{}'.format(prbtype))(
mesh, sigma=sigmaMod, muMap=muMap
)
m0 = muMod
prob.pair(survey)
return m0, prob, survey
def getFDEMProblem(fdemType, comp, SrcList, freq, useMu=False, verbose=False):
cs = 10.
ncx, ncy, ncz = 0, 0, 0
npad = 8
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],['C','C','C'])
if useMu is True:
mapping = [('sigma', Maps.ExpMap(mesh)), ('mu', Maps.IdentityMap(mesh))]
else:
mapping = Maps.ExpMap(mesh)
x = np.array([np.linspace(-5.*cs,-2.*cs,3),np.linspace(5.*cs,2.*cs,3)]) + cs/4. #don't sample right by the source, slightly off alignment from either staggered grid
XYZ = Utils.ndgrid(x,x,np.linspace(-2.*cs,2.*cs,5))
Rx0 = getattr(EM.FDEM.Rx, 'Point_' + comp[0])
if comp[2] == 'r':
real_or_imag = 'real'
elif comp[2] == 'i':
real_or_imag = 'imag'
rx0 = Rx0(XYZ, comp[1], 'imag')
Src = []
for SrcType in SrcList:
if SrcType is 'MagDipole':
Src.append(EM.FDEM.Src.MagDipole([rx0], freq=freq, loc=np.r_[0.,0.,0.]))
elif SrcType is 'MagDipole_Bfield':
Src.append(EM.FDEM.Src.MagDipole_Bfield([rx0], freq=freq, loc=np.r_[0.,0.,0.]))
elif SrcType is 'CircularLoop':
Src.append(EM.FDEM.Src.CircularLoop([rx0], freq=freq, loc=np.r_[0.,0.,0.]))
elif SrcType is 'RawVec':
if fdemType is 'e' or fdemType is 'b':
S_m = np.zeros(mesh.nF)
S_e = np.zeros(mesh.nE)
S_m[Utils.closestPoints(mesh,[0.,0.,0.],'Fz') + np.sum(mesh.vnF[:1])] = 1e-3
S_e[Utils.closestPoints(mesh,[0.,0.,0.],'Ez') + np.sum(mesh.vnE[:1])] = 1e-3
Src.append(EM.FDEM.Src.RawVec([rx0], freq, S_m, mesh.getEdgeInnerProduct()*S_e))
elif fdemType is 'h' or fdemType is 'j':
S_m = np.zeros(mesh.nE)
S_e = np.zeros(mesh.nF)
S_m[Utils.closestPoints(mesh,[0.,0.,0.],'Ez') + np.sum(mesh.vnE[:1])] = 1e-3
S_e[Utils.closestPoints(mesh,[0.,0.,0.],'Fz') + np.sum(mesh.vnF[:1])] = 1e-3
Src.append(EM.FDEM.Src.RawVec([rx0], freq, mesh.getEdgeInnerProduct()*S_m, S_e))
if verbose:
print(' Fetching {0!s} problem'.format((fdemType)))
if fdemType == 'e':
survey = EM.FDEM.Survey(Src)
prb = EM.FDEM.Problem3D_e(mesh, mapping=mapping)
elif fdemType == 'b':
survey = EM.FDEM.Survey(Src)
prb = EM.FDEM.Problem3D_b(mesh, mapping=mapping)
elif fdemType == 'j':
survey = EM.FDEM.Survey(Src)
prb = EM.FDEM.Problem3D_j(mesh, mapping=mapping)
elif fdemType == 'h':
survey = EM.FDEM.Survey(Src)
prb = EM.FDEM.Problem3D_h(mesh, mapping=mapping)
else:
raise NotImplementedError()
prb.pair(survey)
try:
from pymatsolver import PardisoSolver
prb.Solver = PardisoSolver
except ImportError:
prb.Solver = SolverLU
# prb.solverOpts = dict(check_accuracy=True)
return prb
def run(loc=None, sig=None, radi=None, param=None, surveyType='dipole-dipole',
unitType='appConductivity', plotIt=True):
assert surveyType in ['pole-dipole', 'dipole-dipole'], (
"Source type (surveyType) must be pdp or dpdp "
"(pole dipole or dipole dipole)"
)
assert unitType in ['appResistivity', 'appConductivity', 'volt'], (
"Unit type (unitType) must be appResistivity or "
"appConductivity or volt (potential)"
)
if loc is None:
loc = np.c_[[-50., 0., -50.], [50., 0., -50.]]
if sig is None:
sig = np.r_[1e-2, 1e-1, 1e-3]
if radi is None:
radi = np.r_[25., 25.]
if param is None:
param = np.r_[30., 30., 5]
if surveyType == "pole-dipole":
surveyType = "pole-dipole"
elif surveyType == "dipole-dipole":
surveyType = "dipole-dipole"
else:
raise NotImplementedError()
# First we need to create a mesh and a model.
# This is our mesh
dx = 5.
hxind = [(dx, 15, -1.3), (dx, 75), (dx, 15, 1.3)]
hyind = [(dx, 15, -1.3), (dx, 10), (dx, 15, 1.3)]
hzind = [(dx, 15, -1.3), (dx, 15)]
mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')
# Set background conductivity
model = np.ones(mesh.nC) * sig[0]
# First anomaly
ind = Utils.ModelBuilder.getIndicesSphere(loc[:, 0], radi[0], mesh.gridCC)
model[ind] = sig[1]
# Second anomaly
ind = Utils.ModelBuilder.getIndicesSphere(loc[:, 1], radi[1], mesh.gridCC)
model[ind] = sig[2]
# Get index of the center
indy = int(mesh.nCy/2)
# Plot the model for reference
# Define core mesh extent
xlim = 200
zlim = 100
# Then specify the end points of the survey. Let's keep it simple for now
# and survey above the anomalies, top of the mesh
ends = [(-175, 0), (175, 0)]
ends = np.c_[np.asarray(ends), np.ones(2).T*mesh.vectorNz[-1]]
# Snap the endpoints to the grid. Easier to create 2D section.
indx = Utils.closestPoints(mesh, ends)
locs = np.c_[
mesh.gridCC[indx, 0],
mesh.gridCC[indx, 1],
np.ones(2).T*mesh.vectorNz[-1]
]
# We will handle the geometry of the survey for you and create all the
# combination of tx-rx along line
survey = gen_DCIPsurvey(
locs, mesh, surveyType, param[0], param[1], param[2]
)
Tx = survey.srcList
Rx = [src.rxList[0] for src in Tx]
# Define some global geometry
dl_len = np.sqrt(np.sum((locs[0, :] - locs[1, :])**2))
dl_x = (Tx[-1].loc[0][1] - Tx[0].loc[0][0]) / dl_len
dl_y = (Tx[-1].loc[1][1] - Tx[0].loc[1][0]) / dl_len
# Set boundary conditions
mesh.setCellGradBC('neumann')
# Define the linear system needed for the DC problem. We assume an infitite
# line source for simplicity.
Div = mesh.faceDiv
Grad = mesh.cellGrad
Msig = Utils.sdiag(1./(mesh.aveF2CC.T*(1./model)))
A = Div*Msig*Grad
# Change one corner to deal with nullspace
A[0, 0] = 1
A = sp.csc_matrix(A)
# We will solve the system iteratively, so a pre-conditioner is helpful
# This is simply a Jacobi preconditioner (inverse of the main diagonal)
dA = A.diagonal()
P = sp.spdiags(1/dA, 0, A.shape[0], A.shape[0])
# Now we can solve the system for all the transmitters
# We want to store the data
data = []
# There is probably a more elegant way to do this,
# but we can just for-loop through the transmitters
for ii in range(len(Tx)):
start_time = time.time() # Let's time the calculations
# print("Transmitter %i / %i\r" % (ii+1, len(Tx)))
# Select dipole locations for receiver
rxloc_M = np.asarray(Rx[ii].locs[0])
rxloc_N = np.asarray(Rx[ii].locs[1])
# For usual cases 'dipole-dipole' or "gradient"
if surveyType == 'pole-dipole':
# Create an "inifinity" pole
tx = np.squeeze(Tx[ii].loc[:, 0:1])
tinf = tx + np.array([dl_x, dl_y, 0])*dl_len*2
inds = Utils.closestPoints(mesh, np.c_[tx, tinf].T)
RHS = (
mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T *
([-1] / mesh.vol[inds])
)
else:
inds = Utils.closestPoints(mesh, np.asarray(Tx[ii].loc))
RHS = (
mesh.getInterpolationMat(np.asarray(Tx[ii].loc), 'CC').T *
([-1, 1] / mesh.vol[inds])
)
# Iterative Solve
Ainvb = sp.linalg.bicgstab(P*A, P*RHS, tol=1e-5)
# We now have the potential everywhere
phi = Utils.mkvc(Ainvb[0])
# Solve for phi on pole locations
P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
# Compute the potential difference
dtemp = (P1*phi - P2*phi)*np.pi
data.append(dtemp)
print ('\rTransmitter {0} of {1} -> Time:{2} sec'.format(
ii, len(Tx), time.time() - start_time)
)
print ('Transmitter {0} of {1}'.format(ii, len(Tx)))
print ('Forward completed')
# Let's just convert the 3D format into 2D (distance along line) and plot
survey2D = convertObs_DC3D_to_2D(survey, np.ones(survey.nSrc), 'Xloc')
survey2D.dobs = np.hstack(data)
if not plotIt:
return
fig = plt.figure(figsize=(7, 7))
ax = plt.subplot(2, 1, 1, aspect='equal')
# Plot the location of the spheres for reference
circle1 = plt.Circle(
(loc[0, 0], loc[2, 0]), radi[0], color='w', fill=False, lw=3
)
circle2 = plt.Circle(
(loc[0, 1], loc[2, 1]), radi[1], color='k', fill=False, lw=3
)
ax.add_artist(circle1)
ax.add_artist(circle2)
dat = mesh.plotSlice(
np.log10(model), ax=ax, normal='Y',
ind=indy, grid=True, clim=np.log10([sig.min(), sig.max()])
)
ax.set_title('3-D model')
plt.gca().set_aspect('equal', adjustable='box')
plt.scatter(Tx[0].loc[0][0], Tx[0].loc[0][2], s=40, c='g', marker='v')
plt.scatter(Rx[0].locs[0][:, 0], Rx[0].locs[0][:, 1], s=40, c='y')
plt.xlim([-xlim, xlim])
plt.ylim([-zlim, mesh.vectorNz[-1]+dx])
pos = ax.get_position()
ax.set_position([pos.x0, pos.y0 + 0.025, pos.width, pos.height])
pos = ax.get_position()
# the parameters are the specified position you set
cbarax = fig.add_axes(
[pos.x0, pos.y0 + 0.025, pos.width, pos.height * 0.04]
)
cb = fig.colorbar(
dat[0],
cax=cbarax,
orientation="horizontal",
ax=ax,
ticks=np.linspace(np.log10(sig.min()), np.log10(sig.max()), 3),
format="$10^{%.1f}$"
)
cb.set_label("Conductivity (S/m)", size=12)
cb.ax.tick_params(labelsize=12)
# Second plot for the predicted apparent resistivity data
ax2 = plt.subplot(2, 1, 2, aspect='equal')
# Plot the location of the spheres for reference
circle1 = plt.Circle(
(loc[0, 0], loc[2, 0]), radi[0], color='w', fill=False, lw=3
)
circle2 = plt.Circle(
(loc[0, 1], loc[2, 1]), radi[1], color='k', fill=False, lw=3
)
ax2.add_artist(circle1)
ax2.add_artist(circle2)
# Add the pseudo section
dat = plot_pseudoSection(
survey2D, ax2, surveyType=surveyType, dataType=unitType
)
ax2.set_title('Apparent Conductivity data')
plt.ylim([-zlim, mesh.vectorNz[-1]+dx])
return fig, ax
def dc_resistivity(
log_sigma_background=1., # Conductivity of the background, S/m
log_sigma_block=2, # Conductivity of the block, S/m
plot_type='potential' # "conductivity", "potential", or "current"
):
from pylab import rcParams
rcParams['figure.figsize'] = 10, 10
# Define a unit-cell mesh
mesh = Mesh.TensorMesh([100, 100]) # setup a mesh on which to solve
# model parameters
sigma_background = 10**log_sigma_background
sigma_block = 10**log_sigma_block
# add a block to our model
x_block = np.r_[0.4, 0.6]
y_block = np.r_[0.4, 0.6]
# assign them on the mesh
# create a physical property model
sigma = sigma_background * np.ones(mesh.nC)
block_indices = ((mesh.gridCC[:, 0] >= x_block[0]) & # left boundary
(mesh.gridCC[:, 0] <= x_block[1]) & # right boundary
(mesh.gridCC[:, 1] >= y_block[0]) & # bottom boundary
(mesh.gridCC[:, 1] <= y_block[1])) # top boundary
# add the block to the physical property model
sigma[block_indices] = sigma_block
# Define a source
a_loc, b_loc = np.r_[0.2, 0.5], np.r_[0.8, 0.5]
source_locs = [a_loc, b_loc]
# locate it on the mesh
source_loc_inds = Utils.closestPoints(mesh, source_locs)
a_loc_mesh = mesh.gridCC[source_loc_inds[0], :]
b_loc_mesh = mesh.gridCC[source_loc_inds[1], :]
if plot_type == 'conductivity':
plt.colorbar(mesh.plotImage(sigma)[0])
plt.plot(a_loc_mesh[0], a_loc_mesh[1], 'wv', markersize=8)
plt.plot(b_loc_mesh[0], b_loc_mesh[1], 'w^', markersize=8)
plt.title('electrical conductivity, $\sigma$')
return
# Assemble and solve the DC resistivity problem
Div = mesh.faceDiv
Sigma = mesh.getFaceInnerProduct(sigma, invProp=True, invMat=True)
Vol = Utils.sdiag(mesh.vol)
# assemble the system matrix
A = Vol * Div * Sigma * Div.T * Vol
# right hand side
q = np.zeros(mesh.nC)
q[source_loc_inds] = np.r_[+1, -1]
# solve the DC resistivity problem
Ainv = Solver(A) # create a matrix that behaves like A inverse
phi = Ainv * q
if plot_type == 'potential':
plt.colorbar(mesh.plotImage(phi)[0])
plt.title('Electric Potential, $\phi$')
return
if plot_type == 'current':
j = Sigma * mesh.faceDiv.T * Utils.sdiag(mesh.vol) * phi
plt.colorbar(
mesh.plotImage(j, vType='F', view='vec', streamOpts={'color':
'w'})[0])
plt.title('Current, $j$')
return
#gin = plt.ginput(2, timeout = 0)
#==============================================================================
# if not gin:
# print 'SimPED - Simulation has ended with return'
# break
#==============================================================================
# Add z coordinate to all survey... assume flat
nz = mesh.vectorNz
var = np.c_[np.asarray(gin),np.ones(2).T*nz[-1]]
# Snap the endpoints to the grid. Easier to create 2D section.
indx = Utils.closestPoints(mesh, var )
endl = np.c_[mesh.gridCC[indx,0],mesh.gridCC[indx,1],np.ones(2).T*nz[-1]]
[Tx, Rx] = DC.gen_DCIPsurvey(endl, mesh, stype, a, b, n)
dl_len = np.sqrt( np.sum((endl[0,:] - endl[1,:])**2) )
dl_x = ( Tx[-1][0,1] - Tx[0][0,0] ) / dl_len
dl_y = ( Tx[-1][1,1] - Tx[0][1,0] ) / dl_len
azm = np.arctan(dl_y/dl_x)
# Plot stations along line
plt.scatter(Tx[0][0,:],Tx[0][1,:],s=20,c='g')
plt.scatter(Rx[0][:,0::3],Rx[0][:,1::3],s=20,c='y')
#%% Forward model data
data = []#np.zeros( nstn*nrx )
def test_CylMeshEBDipoles(self):
print ("Testing CylMesh Electric and Magnetic Dipoles in a wholespace-"
" Analytic: J-formulation")
sigmaback = 1.
mur = 2.
freq = 1.
skdpth = 500./np.sqrt(sigmaback*freq)
csx, ncx, npadx = 5, 50, 25
csz, ncz, npadz = 5, 50, 25
hx = Utils.meshTensor([(csx,ncx), (csx,npadx,1.3)])
hz = Utils.meshTensor([(csz,npadz,-1.3), (csz,ncz), (csz,npadz,1.3)])
mesh = Mesh.CylMesh([hx, 1, hz], [0., 0., -hz.sum()/2]) # define the cylindrical mesh
if plotIt:
mesh.plotGrid()
# make sure mesh is big enough
self.assertTrue(mesh.hz.sum() > skdpth*2.)
self.assertTrue(mesh.hx.sum() > skdpth*2.)
SigmaBack = sigmaback*np.ones((mesh.nC))
MuBack = mur*mu_0*np.ones((mesh.nC))
# set up source
# test electric dipole
src_loc = np.r_[0., 0., 0.]
s_ind = Utils.closestPoints(mesh, src_loc, 'Fz') + mesh.nFx
de = np.zeros(mesh.nF, dtype=complex)
de[s_ind] = 1./csz
de_p = [EM.FDEM.Src.RawVec_e([], freq, de/mesh.area)]
dm_p = [EM.FDEM.Src.MagDipole([], freq, src_loc)]
# Pair the problem and survey
surveye = EM.FDEM.Survey(de_p)
surveym = EM.FDEM.Survey(dm_p)
mapping = [('sigma', Maps.IdentityMap(mesh)),
('mu', Maps.IdentityMap(mesh))]
prbe = EM.FDEM.Problem3D_h(mesh, mapping=mapping)
prbm = EM.FDEM.Problem3D_e(mesh, mapping=mapping)
prbe.pair(surveye) # pair problem and survey
prbm.pair(surveym)
# solve
fieldsBackE = prbe.fields(np.r_[SigmaBack, MuBack]) # Done
fieldsBackM = prbm.fields(np.r_[SigmaBack, MuBack]) # Done
rlim = [20., 500.]
lookAtTx = de_p
r = mesh.vectorCCx[np.argmin(np.abs(mesh.vectorCCx-rlim[0])) : np.argmin(np.abs(mesh.vectorCCx-rlim[1]))]
z = 100.
# where we choose to measure
XYZ = Utils.ndgrid(r, np.r_[0.], np.r_[z])
Pf = mesh.getInterpolationMat(XYZ, 'CC')
Zero = sp.csr_matrix(Pf.shape)
Pfx,Pfz = sp.hstack([Pf,Zero]),sp.hstack([Zero,Pf])
jn = fieldsBackE[de_p,'j']
bn = fieldsBackM[dm_p,'b']
Rho = Utils.sdiag(1./SigmaBack)
Rho = sp.block_diag([Rho,Rho])
en = Rho*mesh.aveF2CCV*jn
bn = mesh.aveF2CCV*bn
ex, ez = Pfx*en, Pfz*en
bx, bz = Pfx*bn, Pfz*bn
# get analytic solution
exa, eya, eza = EM.Analytics.FDEM.ElectricDipoleWholeSpace(XYZ, src_loc, sigmaback, freq,orientation='Z',mu= mur*mu_0)
exa, eya, eza = Utils.mkvc(exa, 2), Utils.mkvc(eya, 2), Utils.mkvc(eza, 2)
bxa, bya, bza = EM.Analytics.FDEM.MagneticDipoleWholeSpace(XYZ, src_loc, sigmaback, freq,orientation='Z',mu= mur*mu_0)
bxa, bya, bza = Utils.mkvc(bxa, 2), Utils.mkvc(bya, 2), Utils.mkvc(bza, 2)
print(' comp, anayltic, numeric, num - ana, (num - ana)/ana')
print(' ex:', np.linalg.norm(exa), np.linalg.norm(ex), np.linalg.norm(exa-ex), np.linalg.norm(exa-ex)/np.linalg.norm(exa))
print(' ez:', np.linalg.norm(eza), np.linalg.norm(ez), np.linalg.norm(eza-ez), np.linalg.norm(eza-ez)/np.linalg.norm(eza))
print(' bx:', np.linalg.norm(bxa), np.linalg.norm(bx), np.linalg.norm(bxa-bx), np.linalg.norm(bxa-bx)/np.linalg.norm(bxa))
print(' bz:', np.linalg.norm(bza), np.linalg.norm(bz), np.linalg.norm(bza-bz), np.linalg.norm(bza-bz)/np.linalg.norm(bza))
if plotIt is True:
# Edipole
plt.subplot(221)
plt.plot(r,ex.real,'o',r,exa.real,linewidth=2)
plt.grid(which='both')
plt.title('Ex Real')
plt.xlabel('r (m)')
plt.subplot(222)
plt.plot(r,ex.imag,'o',r,exa.imag,linewidth=2)
plt.grid(which='both')
plt.title('Ex Imag')
plt.legend(['Num','Ana'],bbox_to_anchor=(1.5,0.5))
plt.xlabel('r (m)')
plt.subplot(223)
plt.plot(r,ez.real,'o',r,eza.real,linewidth=2)
plt.grid(which='both')
plt.title('Ez Real')
plt.xlabel('r (m)')
plt.subplot(224)
plt.plot(r,ez.imag,'o',r,eza.imag,linewidth=2)
plt.grid(which='both')
plt.title('Ez Imag')
plt.xlabel('r (m)')
plt.tight_layout()
# Bdipole
plt.subplot(221)
plt.plot(r,bx.real,'o',r,bxa.real,linewidth=2)
plt.grid(which='both')
plt.title('Bx Real')
plt.xlabel('r (m)')
plt.subplot(222)
plt.plot(r,bx.imag,'o',r,bxa.imag,linewidth=2)
plt.grid(which='both')
plt.title('Bx Imag')
plt.legend(['Num','Ana'],bbox_to_anchor=(1.5,0.5))
plt.xlabel('r (m)')
plt.subplot(223)
plt.plot(r,bz.real,'o',r,bza.real,linewidth=2)
plt.grid(which='both')
plt.title('Bz Real')
plt.xlabel('r (m)')
plt.subplot(224)
plt.plot(r,bz.imag,'o',r,bza.imag,linewidth=2)
plt.grid(which='both')
plt.title('Bz Imag')
plt.xlabel('r (m)')
plt.tight_layout()
self.assertTrue(np.linalg.norm(exa-ex)/np.linalg.norm(exa) < tol_EBdipole)
self.assertTrue(np.linalg.norm(eza-ez)/np.linalg.norm(eza) < tol_EBdipole)
self.assertTrue(np.linalg.norm(bxa-bx)/np.linalg.norm(bxa) < tol_EBdipole)
self.assertTrue(np.linalg.norm(bza-bz)/np.linalg.norm(bza) < tol_EBdipole)
def simulate_prism(
self,
component,
inclination,
declination,
length,
dx,
B0,
kappa,
depth,
profile,
fixed_scale,
show_halfwidth,
prism_dx,
prism_dy,
prism_dz,
prism_inclination,
prism_declination,
):
self.component = component
self.inclination = inclination
self.declination = declination
self.length = length
self.dx = dx
self.B0 = B0
self.kappa = kappa
self.depth = depth
self.profile = profile
self.fixed_scale = fixed_scale
self.show_halfwidth = show_halfwidth
# prism parameter
self.prism = self.get_prism(
prism_dx,
prism_dy,
prism_dz,
0,
0,
-depth,
prism_inclination,
prism_declination,
)
nx = ny = int(length / dx)
hx = np.ones(nx) * dx
hy = np.ones(ny) * dx
self.mesh = Mesh.TensorMesh((hx, hy), "CC")
z = np.r_[1.0]
B = np.r_[B0, inclination, declination]
# Project to the direction of earth field
if component == "Bt":
uType = "tf"
elif component == "Bx":
uType = "bx"
elif component == "By":
uType = "by"
elif component == "Bz":
uType = "bz"
xyz = Utils.ndgrid(self.mesh.vectorCCx, self.mesh.vectorCCy, z)
self.survey = createMagSurvey(np.c_[xyz, np.ones(self.mesh.nC)], B)
self.prob = problem()
self.prob.prism = self.prism
self.prob.survey = self.survey
self.prob.susc = kappa
self.prob.uType, self.prob.mType = uType, "induced"
self.data = self.prob.fields()[0]
# Compute profile
if (profile == "North") or (profile == "None"):
self.xy_profile = np.c_[np.zeros(self.mesh.nCx),
self.mesh.vectorCCx]
elif profile == "East":
self.xy_profile = np.c_[self.mesh.vectorCCx,
np.zeros(self.mesh.nCx)]
self.inds_profile = Utils.closestPoints(self.mesh, self.xy_profile)
self.data_profile = self.data[self.inds_profile]
def test_CylMeshEBDipoles(self):
print(
"Testing CylMesh Electric and Magnetic Dipoles in a wholespace-"
" Analytic: J-formulation")
sigmaback = 1.
mur = 2.
freq = 1.
skdpth = 500. / np.sqrt(sigmaback * freq)
csx, ncx, npadx = 5, 50, 25
csz, ncz, npadz = 5, 50, 25
hx = Utils.meshTensor([(csx, ncx), (csx, npadx, 1.3)])
hz = Utils.meshTensor([(csz, npadz, -1.3), (csz, ncz),
(csz, npadz, 1.3)])
mesh = Mesh.CylMesh(
[hx, 1, hz],
[0., 0., -hz.sum() / 2]) # define the cylindrical mesh
if plotIt:
mesh.plotGrid()
# make sure mesh is big enough
self.assertTrue(mesh.hz.sum() > skdpth * 2.)
self.assertTrue(mesh.hx.sum() > skdpth * 2.)
SigmaBack = sigmaback * np.ones((mesh.nC))
MuBack = mur * mu_0 * np.ones((mesh.nC))
# set up source
# test electric dipole
src_loc = np.r_[0., 0., 0.]
s_ind = Utils.closestPoints(mesh, src_loc, 'Fz') + mesh.nFx
de = np.zeros(mesh.nF, dtype=complex)
de[s_ind] = 1. / csz
de_p = [EM.FDEM.Src.RawVec_e([], freq, de / mesh.area)]
dm_p = [EM.FDEM.Src.MagDipole([], freq, src_loc)]
# Pair the problem and survey
surveye = EM.FDEM.Survey(de_p)
surveym = EM.FDEM.Survey(dm_p)
mapping = [('sigma', Maps.IdentityMap(mesh)),
('mu', Maps.IdentityMap(mesh))]
prbe = EM.FDEM.Problem3D_h(mesh, mapping=mapping)
prbm = EM.FDEM.Problem3D_e(mesh, mapping=mapping)
prbe.pair(surveye) # pair problem and survey
prbm.pair(surveym)
# solve
fieldsBackE = prbe.fields(np.r_[SigmaBack, MuBack]) # Done
fieldsBackM = prbm.fields(np.r_[SigmaBack, MuBack]) # Done
rlim = [20., 500.]
lookAtTx = de_p
r = mesh.vectorCCx[np.argmin(np.abs(mesh.vectorCCx - rlim[0])):np.
argmin(np.abs(mesh.vectorCCx - rlim[1]))]
z = 100.
# where we choose to measure
XYZ = Utils.ndgrid(r, np.r_[0.], np.r_[z])
Pf = mesh.getInterpolationMat(XYZ, 'CC')
Zero = sp.csr_matrix(Pf.shape)
Pfx, Pfz = sp.hstack([Pf, Zero]), sp.hstack([Zero, Pf])
jn = fieldsBackE[de_p, 'j']
bn = fieldsBackM[dm_p, 'b']
Rho = Utils.sdiag(1. / SigmaBack)
Rho = sp.block_diag([Rho, Rho])
en = Rho * mesh.aveF2CCV * jn
bn = mesh.aveF2CCV * bn
ex, ez = Pfx * en, Pfz * en
bx, bz = Pfx * bn, Pfz * bn
# get analytic solution
exa, eya, eza = EM.Analytics.FDEM.ElectricDipoleWholeSpace(
XYZ, src_loc, sigmaback, freq, orientation='Z', mu=mur * mu_0)
exa, eya, eza = Utils.mkvc(exa, 2), Utils.mkvc(eya,
2), Utils.mkvc(eza, 2)
bxa, bya, bza = EM.Analytics.FDEM.MagneticDipoleWholeSpace(
XYZ, src_loc, sigmaback, freq, orientation='Z', mu=mur * mu_0)
bxa, bya, bza = Utils.mkvc(bxa, 2), Utils.mkvc(bya,
2), Utils.mkvc(bza, 2)
print ' comp, anayltic, numeric, num - ana, (num - ana)/ana'
print ' ex:', np.linalg.norm(exa), np.linalg.norm(ex), np.linalg.norm(
exa - ex), np.linalg.norm(exa - ex) / np.linalg.norm(exa)
print ' ez:', np.linalg.norm(eza), np.linalg.norm(ez), np.linalg.norm(
eza - ez), np.linalg.norm(eza - ez) / np.linalg.norm(eza)
print ' bx:', np.linalg.norm(bxa), np.linalg.norm(bx), np.linalg.norm(
bxa - bx), np.linalg.norm(bxa - bx) / np.linalg.norm(bxa)
print ' bz:', np.linalg.norm(bza), np.linalg.norm(bz), np.linalg.norm(
bza - bz), np.linalg.norm(bza - bz) / np.linalg.norm(bza)
if plotIt is True:
# Edipole
plt.subplot(221)
plt.plot(r, ex.real, 'o', r, exa.real, linewidth=2)
plt.grid(which='both')
plt.title('Ex Real')
plt.xlabel('r (m)')
plt.subplot(222)
plt.plot(r, ex.imag, 'o', r, exa.imag, linewidth=2)
plt.grid(which='both')
plt.title('Ex Imag')
plt.legend(['Num', 'Ana'], bbox_to_anchor=(1.5, 0.5))
plt.xlabel('r (m)')
plt.subplot(223)
plt.plot(r, ez.real, 'o', r, eza.real, linewidth=2)
plt.grid(which='both')
plt.title('Ez Real')
plt.xlabel('r (m)')
plt.subplot(224)
plt.plot(r, ez.imag, 'o', r, eza.imag, linewidth=2)
plt.grid(which='both')
plt.title('Ez Imag')
plt.xlabel('r (m)')
plt.tight_layout()
# Bdipole
plt.subplot(221)
plt.plot(r, bx.real, 'o', r, bxa.real, linewidth=2)
plt.grid(which='both')
plt.title('Bx Real')
plt.xlabel('r (m)')
plt.subplot(222)
plt.plot(r, bx.imag, 'o', r, bxa.imag, linewidth=2)
plt.grid(which='both')
plt.title('Bx Imag')
plt.legend(['Num', 'Ana'], bbox_to_anchor=(1.5, 0.5))
plt.xlabel('r (m)')
plt.subplot(223)
plt.plot(r, bz.real, 'o', r, bza.real, linewidth=2)
plt.grid(which='both')
plt.title('Bz Real')
plt.xlabel('r (m)')
plt.subplot(224)
plt.plot(r, bz.imag, 'o', r, bza.imag, linewidth=2)
plt.grid(which='both')
plt.title('Bz Imag')
plt.xlabel('r (m)')
plt.tight_layout()
self.assertTrue(
np.linalg.norm(exa - ex) / np.linalg.norm(exa) < tol_EBdipole)
self.assertTrue(
np.linalg.norm(eza - ez) / np.linalg.norm(eza) < tol_EBdipole)
self.assertTrue(
np.linalg.norm(bxa - bx) / np.linalg.norm(bxa) < tol_EBdipole)
self.assertTrue(
np.linalg.norm(bza - bz) / np.linalg.norm(bza) < tol_EBdipole)
def simulate_dipole(
self,
component,
target,
inclination,
declination,
length,
dx,
moment,
depth,
profile,
fixed_scale,
show_halfwidth,
):
self.component = component
self.target = target
self.inclination = inclination
self.declination = declination
self.length = length
self.dx = dx
self.moment = moment
self.depth = depth
self.profile = profile
self.fixed_scale = fixed_scale
self.show_halfwidth = show_halfwidth
nT = 1e9
nx = ny = int(length / dx)
hx = np.ones(nx) * dx
hy = np.ones(ny) * dx
self.mesh = Mesh.TensorMesh((hx, hy), "CC")
z = np.r_[1.0]
orientation = self.id_to_cartesian(inclination, declination)
if self.target == "Dipole":
md = em.static.MagneticDipoleWholeSpace(location=np.r_[0, 0,
-depth],
orientation=orientation,
moment=moment)
xyz = Utils.ndgrid(self.mesh.vectorCCx, self.mesh.vectorCCy, z)
b_vec = md.magnetic_flux_density(xyz)
elif self.target == "Monopole (+)":
md = em.static.MagneticPoleWholeSpace(location=np.r_[0, 0, -depth],
orientation=orientation,
moment=moment)
xyz = Utils.ndgrid(self.mesh.vectorCCx, self.mesh.vectorCCy, z)
b_vec = md.magnetic_flux_density(xyz)
elif self.target == "Monopole (-)":
md = em.static.MagneticPoleWholeSpace(location=np.r_[0, 0, -depth],
orientation=orientation,
moment=moment)
xyz = Utils.ndgrid(self.mesh.vectorCCx, self.mesh.vectorCCy, z)
b_vec = -md.magnetic_flux_density(xyz)
# Project to the direction of earth field
if component == "Bt":
rx_orientation = orientation.copy()
elif component == "Bg":
rx_orientation = orientation.copy()
xyz_up = Utils.ndgrid(self.mesh.vectorCCx, self.mesh.vectorCCy,
z + 1.0)
b_vec -= md.magnetic_flux_density(xyz_up)
elif component == "Bx":
rx_orientation = self.id_to_cartesian(0, 0)
elif component == "By":
rx_orientation = self.id_to_cartesian(0, 90)
elif component == "Bz":
rx_orientation = self.id_to_cartesian(90, 0)
self.data = self.dot_product(b_vec, rx_orientation) * nT
# Compute profile
if (profile == "North") or (profile == "None"):
self.xy_profile = np.c_[np.zeros(self.mesh.nCx),
self.mesh.vectorCCx]
elif profile == "East":
self.xy_profile = np.c_[self.mesh.vectorCCx,
np.zeros(self.mesh.nCx)]
self.inds_profile = Utils.closestPoints(self.mesh, self.xy_profile)
self.data_profile = self.data[self.inds_profile]
def run(loc=None,
sig=None,
radi=None,
param=None,
surveyType='dipole-dipole',
unitType='appConductivity',
plotIt=True):
assert surveyType in ['pole-dipole', 'dipole-dipole'
], ("Source type (surveyType) must be pdp or dpdp "
"(pole dipole or dipole dipole)")
assert unitType in ['appResistivity', 'appConductivity', 'volt'
], ("Unit type (unitType) must be appResistivity or "
"appConductivity or volt (potential)")
if loc is None:
loc = np.c_[[-50., 0., -50.], [50., 0., -50.]]
if sig is None:
sig = np.r_[1e-2, 1e-1, 1e-3]
if radi is None:
radi = np.r_[25., 25.]
if param is None:
param = np.r_[30., 30., 5]
if surveyType == "pole-dipole":
surveyType = "pole-dipole"
elif surveyType == "dipole-dipole":
surveyType = "dipole-dipole"
else:
raise NotImplementedError()
# First we need to create a mesh and a model.
# This is our mesh
dx = 5.
hxind = [(dx, 15, -1.3), (dx, 75), (dx, 15, 1.3)]
hyind = [(dx, 15, -1.3), (dx, 10), (dx, 15, 1.3)]
hzind = [(dx, 15, -1.3), (dx, 15)]
mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')
# Set background conductivity
model = np.ones(mesh.nC) * sig[0]
# First anomaly
ind = Utils.ModelBuilder.getIndicesSphere(loc[:, 0], radi[0], mesh.gridCC)
model[ind] = sig[1]
# Second anomaly
ind = Utils.ModelBuilder.getIndicesSphere(loc[:, 1], radi[1], mesh.gridCC)
model[ind] = sig[2]
# Get index of the center
indy = int(mesh.nCy / 2)
# Plot the model for reference
# Define core mesh extent
xlim = 200
zlim = 100
# Then specify the end points of the survey. Let's keep it simple for now
# and survey above the anomalies, top of the mesh
ends = [(-175, 0), (175, 0)]
ends = np.c_[np.asarray(ends), np.ones(2).T * mesh.vectorNz[-1]]
# Snap the endpoints to the grid. Easier to create 2D section.
indx = Utils.closestPoints(mesh, ends)
locs = np.c_[mesh.gridCC[indx, 0], mesh.gridCC[indx, 1],
np.ones(2).T * mesh.vectorNz[-1]]
# We will handle the geometry of the survey for you and create all the
# combination of tx-rx along line
survey = gen_DCIPsurvey(locs, mesh, surveyType, param[0], param[1],
param[2])
Tx = survey.srcList
Rx = [src.rxList[0] for src in Tx]
# Define some global geometry
dl_len = np.sqrt(np.sum((locs[0, :] - locs[1, :])**2))
dl_x = (Tx[-1].loc[0][1] - Tx[0].loc[0][0]) / dl_len
dl_y = (Tx[-1].loc[1][1] - Tx[0].loc[1][0]) / dl_len
# Set boundary conditions
mesh.setCellGradBC('neumann')
# Define the linear system needed for the DC problem. We assume an infitite
# line source for simplicity.
Div = mesh.faceDiv
Grad = mesh.cellGrad
Msig = Utils.sdiag(1. / (mesh.aveF2CC.T * (1. / model)))
A = Div * Msig * Grad
# Change one corner to deal with nullspace
A[0, 0] = 1
A = sp.csc_matrix(A)
# We will solve the system iteratively, so a pre-conditioner is helpful
# This is simply a Jacobi preconditioner (inverse of the main diagonal)
dA = A.diagonal()
P = sp.spdiags(1 / dA, 0, A.shape[0], A.shape[0])
# Now we can solve the system for all the transmitters
# We want to store the data
data = []
# There is probably a more elegant way to do this,
# but we can just for-loop through the transmitters
for ii in range(len(Tx)):
start_time = time.time() # Let's time the calculations
# print("Transmitter %i / %i\r" % (ii+1, len(Tx)))
# Select dipole locations for receiver
rxloc_M = np.asarray(Rx[ii].locs[0])
rxloc_N = np.asarray(Rx[ii].locs[1])
# For usual cases 'dipole-dipole' or "gradient"
if surveyType == 'pole-dipole':
# Create an "inifinity" pole
tx = np.squeeze(Tx[ii].loc[:, 0:1])
tinf = tx + np.array([dl_x, dl_y, 0]) * dl_len * 2
inds = Utils.closestPoints(mesh, np.c_[tx, tinf].T)
RHS = (mesh.getInterpolationMat(np.asarray(Tx[ii]).T, 'CC').T *
([-1] / mesh.vol[inds]))
else:
inds = Utils.closestPoints(mesh, np.asarray(Tx[ii].loc))
RHS = (mesh.getInterpolationMat(np.asarray(Tx[ii].loc), 'CC').T *
([-1, 1] / mesh.vol[inds]))
# Iterative Solve
Ainvb = sp.linalg.bicgstab(P * A, P * RHS, tol=1e-5)
# We now have the potential everywhere
phi = Utils.mkvc(Ainvb[0])
# Solve for phi on pole locations
P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
# Compute the potential difference
dtemp = (P1 * phi - P2 * phi) * np.pi
data.append(dtemp)
print('\rTransmitter {0} of {1} -> Time:{2} sec'.format(
ii, len(Tx),
time.time() - start_time))
print('Transmitter {0} of {1}'.format(ii, len(Tx)))
print('Forward completed')
# Let's just convert the 3D format into 2D (distance along line) and plot
survey2D = convertObs_DC3D_to_2D(survey, np.ones(survey.nSrc), 'Xloc')
survey2D.dobs = np.hstack(data)
if not plotIt:
return
fig = plt.figure(figsize=(7, 7))
ax = plt.subplot(2, 1, 1, aspect='equal')
# Plot the location of the spheres for reference
circle1 = plt.Circle((loc[0, 0], loc[2, 0]),
radi[0],
color='w',
fill=False,
lw=3)
circle2 = plt.Circle((loc[0, 1], loc[2, 1]),
radi[1],
color='k',
fill=False,
lw=3)
ax.add_artist(circle1)
ax.add_artist(circle2)
dat = mesh.plotSlice(np.log10(model),
ax=ax,
normal='Y',
ind=indy,
grid=True,
clim=np.log10([sig.min(), sig.max()]))
ax.set_title('3-D model')
plt.gca().set_aspect('equal', adjustable='box')
plt.scatter(Tx[0].loc[0][0], Tx[0].loc[0][2], s=40, c='g', marker='v')
plt.scatter(Rx[0].locs[0][:, 0], Rx[0].locs[0][:, 1], s=40, c='y')
plt.xlim([-xlim, xlim])
plt.ylim([-zlim, mesh.vectorNz[-1] + dx])
pos = ax.get_position()
ax.set_position([pos.x0, pos.y0 + 0.025, pos.width, pos.height])
pos = ax.get_position()
# the parameters are the specified position you set
cbarax = fig.add_axes(
[pos.x0, pos.y0 + 0.025, pos.width, pos.height * 0.04])
cb = fig.colorbar(dat[0],
cax=cbarax,
orientation="horizontal",
ax=ax,
ticks=np.linspace(np.log10(sig.min()),
np.log10(sig.max()), 3),
format="$10^{%.1f}$")
cb.set_label("Conductivity (S/m)", size=12)
cb.ax.tick_params(labelsize=12)
# Second plot for the predicted apparent resistivity data
ax2 = plt.subplot(2, 1, 2, aspect='equal')
# Plot the location of the spheres for reference
circle1 = plt.Circle((loc[0, 0], loc[2, 0]),
radi[0],
color='w',
fill=False,
lw=3)
circle2 = plt.Circle((loc[0, 1], loc[2, 1]),
radi[1],
color='k',
fill=False,
lw=3)
ax2.add_artist(circle1)
ax2.add_artist(circle2)
# Add the pseudo section
dat = plot_pseudoSection(survey2D,
ax2,
surveyType=surveyType,
dataType=unitType)
ax2.set_title('Apparent Conductivity data')
plt.ylim([-zlim, mesh.vectorNz[-1] + dx])
return fig, ax
def run(loc=None, sig=None, radi=None, param=None, stype='dpdp', plotIt=True):
"""
DC Forward Simulation
=====================
Forward model conductive spheres in a half-space and plot a pseudo-section
Created by @fourndo on Mon Feb 01 19:28:06 2016
"""
assert stype in [
'pdp', 'dpdp'
], "Source type (stype) must be pdp or dpdp (pole dipole or dipole dipole)"
if loc is None:
loc = np.c_[[-50., 0., -50.], [50., 0., -50.]]
if sig is None:
sig = np.r_[1e-2, 1e-1, 1e-3]
if radi is None:
radi = np.r_[25., 25.]
if param is None:
param = np.r_[30., 30., 5]
# First we need to create a mesh and a model.
# This is our mesh
dx = 5.
hxind = [(dx, 15, -1.3), (dx, 75), (dx, 15, 1.3)]
hyind = [(dx, 15, -1.3), (dx, 10), (dx, 15, 1.3)]
hzind = [(dx, 15, -1.3), (dx, 15)]
mesh = Mesh.TensorMesh([hxind, hyind, hzind], 'CCN')
# Set background conductivity
model = np.ones(mesh.nC) * sig[0]
# First anomaly
ind = Utils.ModelBuilder.getIndicesSphere(loc[:, 0], radi[0], mesh.gridCC)
model[ind] = sig[1]
# Second anomaly
ind = Utils.ModelBuilder.getIndicesSphere(loc[:, 1], radi[1], mesh.gridCC)
model[ind] = sig[2]
# Get index of the center
indy = int(mesh.nCy / 2)
# Plot the model for reference
# Define core mesh extent
xlim = 200
zlim = 125
# Specify the survey type: "pdp" | "dpdp"
# Then specify the end points of the survey. Let's keep it simple for now and survey above the anomalies, top of the mesh
ends = [(-175, 0), (175, 0)]
ends = np.c_[np.asarray(ends), np.ones(2).T * mesh.vectorNz[-1]]
# Snap the endpoints to the grid. Easier to create 2D section.
indx = Utils.closestPoints(mesh, ends)
locs = np.c_[mesh.gridCC[indx, 0], mesh.gridCC[indx, 1],
np.ones(2).T * mesh.vectorNz[-1]]
# We will handle the geometry of the survey for you and create all the combination of tx-rx along line
# [Tx, Rx] = DC.gen_DCIPsurvey(locs, mesh, stype, param[0], param[1], param[2])
survey, Tx, Rx = DC.gen_DCIPsurvey(locs, mesh, stype, param[0], param[1],
param[2])
# Define some global geometry
dl_len = np.sqrt(np.sum((locs[0, :] - locs[1, :])**2))
dl_x = (Tx[-1][0, 1] - Tx[0][0, 0]) / dl_len
dl_y = (Tx[-1][1, 1] - Tx[0][1, 0]) / dl_len
azm = np.arctan(dl_y / dl_x)
#Set boundary conditions
mesh.setCellGradBC('neumann')
# Define the differential operators needed for the DC problem
Div = mesh.faceDiv
Grad = mesh.cellGrad
Msig = Utils.sdiag(1. / (mesh.aveF2CC.T * (1. / model)))
A = Div * Msig * Grad
# Change one corner to deal with nullspace
A[0, 0] = 1
A = sp.csc_matrix(A)
# We will solve the system iteratively, so a pre-conditioner is helpful
# This is simply a Jacobi preconditioner (inverse of the main diagonal)
dA = A.diagonal()
P = sp.spdiags(1 / dA, 0, A.shape[0], A.shape[0])
# Now we can solve the system for all the transmitters
# We want to store the data
data = []
# There is probably a more elegant way to do this, but we can just for-loop through the transmitters
for ii in range(len(Tx)):
start_time = time.time() # Let's time the calculations
#print("Transmitter %i / %i\r" % (ii+1,len(Tx)))
# Select dipole locations for receiver
rxloc_M = np.asarray(Rx[ii][:, 0:3])
rxloc_N = np.asarray(Rx[ii][:, 3:])
# For usual cases "dpdp" or "gradient"
if stype == 'pdp':
# Create an "inifinity" pole
tx = np.squeeze(Tx[ii][:, 0:1])
tinf = tx + np.array([dl_x, dl_y, 0]) * dl_len * 2
inds = Utils.closestPoints(mesh, np.c_[tx, tinf].T)
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T,
'CC').T * ([-1] / mesh.vol[inds])
else:
inds = Utils.closestPoints(mesh, np.asarray(Tx[ii]).T)
RHS = mesh.getInterpolationMat(np.asarray(Tx[ii]).T,
'CC').T * ([-1, 1] / mesh.vol[inds])
# Iterative Solve
Ainvb = sp.linalg.bicgstab(P * A, P * RHS, tol=1e-5)
# We now have the potential everywhere
phi = Utils.mkvc(Ainvb[0])
# Solve for phi on pole locations
P1 = mesh.getInterpolationMat(rxloc_M, 'CC')
P2 = mesh.getInterpolationMat(rxloc_N, 'CC')
# Compute the potential difference
dtemp = (P1 * phi - P2 * phi) * np.pi
data.append(dtemp)
print '\rTransmitter {0} of {1} -> Time:{2} sec'.format(
ii, len(Tx),
time.time() - start_time),
print 'Transmitter {0} of {1}'.format(ii, len(Tx))
print 'Forward completed'
# Let's just convert the 3D format into 2D (distance along line) and plot
# [Tx2d, Rx2d] = DC.convertObs_DC3D_to_2D(survey, np.ones(survey.nSrc))
survey2D = DC.convertObs_DC3D_to_2D(survey, np.ones(survey.nSrc))
survey2D.dobs = np.hstack(data)
# Here is an example for the first tx-rx array
if plotIt:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = plt.subplot(2, 1, 1, aspect='equal')
mesh.plotSlice(np.log10(model), ax=ax, normal='Y', ind=indy, grid=True)
ax.set_title('E-W section at ' + str(mesh.vectorCCy[indy]) + ' m')
plt.gca().set_aspect('equal', adjustable='box')
plt.scatter(Tx[0][0, :], Tx[0][2, :], s=40, c='g', marker='v')
plt.scatter(Rx[0][:, 0::3], Rx[0][:, 2::3], s=40, c='y')
plt.xlim([-xlim, xlim])
plt.ylim([-zlim, mesh.vectorNz[-1] + dx])
ax = plt.subplot(2, 1, 2, aspect='equal')
# Plot the location of the spheres for reference
circle1 = plt.Circle((loc[0, 0] - Tx[0][0, 0], loc[2, 0]),
radi[0],
color='w',
fill=False,
lw=3)
circle2 = plt.Circle((loc[0, 1] - Tx[0][0, 0], loc[2, 1]),
radi[1],
color='k',
fill=False,
lw=3)
ax.add_artist(circle1)
ax.add_artist(circle2)
# Add the speudo section
DC.plot_pseudoSection(survey2D, ax, stype)
# plt.scatter(Tx2d[0][:],Tx[0][2,:],s=40,c='g', marker='v')
# plt.scatter(Rx2d[0][:],Rx[0][:,2::3],s=40,c='y')
# plt.plot(np.r_[Tx2d[0][0],Rx2d[-1][-1,-1]],np.ones(2)*mesh.vectorNz[-1], color='k')
plt.ylim([-zlim, mesh.vectorNz[-1] + dx])
plt.show()
return fig, ax
def dc_resistivity(
log_sigma_background=1., # Conductivity of the background, S/m
log_sigma_block=2, # Conductivity of the block, S/m
plot_type='potential' # "conductivity", "potential", or "current"
):
from pylab import rcParams
rcParams['figure.figsize'] = 10, 10
# Define a unit-cell mesh
mesh = Mesh.TensorMesh([100, 100]) # setup a mesh on which to solve
# model parameters
sigma_background = 10**log_sigma_background
sigma_block = 10**log_sigma_block
# add a block to our model
x_block = np.r_[0.4, 0.6]
y_block = np.r_[0.4, 0.6]
# assign them on the mesh
# create a physical property model
sigma = sigma_background * np.ones(mesh.nC)
block_indices = ((mesh.gridCC[:, 0] >= x_block[0]) & # left boundary
(mesh.gridCC[:, 0] <= x_block[1]) & # right boundary
(mesh.gridCC[:, 1] >= y_block[0]) & # bottom boundary
(mesh.gridCC[:, 1] <= y_block[1])) # top boundary
# add the block to the physical property model
sigma[block_indices] = sigma_block
# Define a source
a_loc, b_loc = np.r_[0.2, 0.5], np.r_[0.8, 0.5]
source_locs = [a_loc, b_loc]
# locate it on the mesh
source_loc_inds = Utils.closestPoints(mesh, source_locs)
a_loc_mesh = mesh.gridCC[source_loc_inds[0], :]
b_loc_mesh = mesh.gridCC[source_loc_inds[1], :]
if plot_type == 'conductivity':
plt.colorbar(mesh.plotImage(sigma)[0])
plt.plot(a_loc_mesh[0], a_loc_mesh[1], 'wv', markersize=8)
plt.plot(b_loc_mesh[0], b_loc_mesh[1], 'w^', markersize=8)
plt.title('electrical conductivity, $\sigma$')
return
# Assemble and solve the DC resistivity problem
Div = mesh.faceDiv
Sigma = mesh.getFaceInnerProduct(sigma, invProp=True, invMat=True)
Vol = Utils.sdiag(mesh.vol)
# assemble the system matrix
A = Vol * Div * Sigma * Div.T * Vol
# right hand side
q = np.zeros(mesh.nC)
q[source_loc_inds] = np.r_[+1, -1]
# solve the DC resistivity problem
Ainv = Solver(A) # create a matrix that behaves like A inverse
phi = Ainv * q
if plot_type == 'potential':
plt.colorbar(mesh.plotImage(phi)[0])
plt.title('Electric Potential, $\phi$')
return
if plot_type == 'current':
j = Sigma * mesh.faceDiv.T * Utils.sdiag(mesh.vol) * phi
plt.colorbar(mesh.plotImage(
j,
vType='F',
view='vec',
streamOpts={'color': 'w'}
)[0])
plt.title('Current, $j$')
return
def simulate_two_monopole(self, component, inclination, declination,
length, dx, moment, depth_n, depth_p, profile,
fixed_scale, show_halfwidth):
self.component = component
self.inclination = inclination
self.declination = declination
self.length = length
self.dx = dx
self.moment = moment
self.depth = depth_n
self.depth = depth_p
self.profile = profile
self.fixed_scale = fixed_scale
self.show_halfwidth = show_halfwidth
nT = 1e9
nx = ny = int(length / dx)
hx = np.ones(nx) * dx
hy = np.ones(ny) * dx
self.mesh = Mesh.TensorMesh((hx, hy), 'CC')
z = np.r_[1.]
orientation = self.id_to_cartesian(inclination, declination)
md_n = em.static.MagneticPoleWholeSpace(location=np.r_[0, 0, -depth_n],
orientation=orientation,
moment=moment)
md_p = em.static.MagneticPoleWholeSpace(location=np.r_[0, 0, -depth_p],
orientation=orientation,
moment=moment)
xyz = Utils.ndgrid(self.mesh.vectorCCx, self.mesh.vectorCCy, z)
b_vec = -md_n.magnetic_flux_density(xyz) + md_p.magnetic_flux_density(
xyz)
# Project to the direction of earth field
if component == 'Bt':
rx_orientation = orientation.copy()
elif component == 'Bx':
rx_orientation = self.id_to_cartesian(0, 0)
elif component == 'By':
rx_orientation = self.id_to_cartesian(0, 90)
elif component == 'Bz':
rx_orientation = self.id_to_cartesian(90, 0)
elif component == 'Bg':
rx_orientation = orientation.copy()
xyz_up = Utils.ndgrid(self.mesh.vectorCCx, self.mesh.vectorCCy,
z + 1.)
b_vec -= -md_n.magnetic_flux_density(xyz_up)
b_vec -= md_p.magnetic_flux_density(xyz_up)
self.data = self.dot_product(b_vec, rx_orientation) * nT
# Compute profile
if (profile == "North") or (profile == "None"):
self.xy_profile = np.c_[np.zeros(self.mesh.nCx),
self.mesh.vectorCCx]
elif profile == "East":
self.xy_profile = np.c_[self.mesh.vectorCCx,
np.zeros(self.mesh.nCx)]
self.inds_profile = Utils.closestPoints(self.mesh, self.xy_profile)
self.data_profile = self.data[self.inds_profile]
