def test_nC_residual(self):
# x-direction
cs, ncx, ncz, npad = 1.0, 10.0, 10.0, 20
hx = [(cs, ncx), (cs, npad, 1.3)]
# z direction
npad = 12
temp = np.logspace(np.log10(1.0), np.log10(12.0), 19)
temp_pad = temp[-1] * 1.3**np.arange(npad)
hz = np.r_[temp_pad[::-1], temp[::-1], temp, temp_pad]
mesh = discretize.CylMesh([hx, 1, hz], "00C")
active = mesh.vectorCCz < 0.0
active = mesh.vectorCCz < 0.0
actMap = maps.InjectActiveCells(mesh,
active,
np.log(1e-8),
nC=mesh.nCz)
mapping = maps.ExpMap(mesh) * maps.SurjectVertical1D(mesh) * actMap
regMesh = discretize.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])
reg = regularization.Simple(regMesh)
self.assertTrue(reg._nC_residual == regMesh.nC)
self.assertTrue(
all([fct._nC_residual == regMesh.nC for fct in reg.objfcts]))
def plot(self, ax=None):
if ax is None:
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
# generate a 2D mesh for plotting slices
mesh2D = discretize.CylMesh(
[self.mesh.hx, 1., self.mesh.hz], x0=self.mesh.x0
)
# plot Sigma
sigmaplt = self.sigma.reshape(self.mesh.vnC, order='F')
ax[0].set_title('$\sigma$')
plt.colorbar(
mesh2D.plotImage(
discretize.utils.mkvc(sigmaplt[:, 0, :]), ax=ax[0],
mirror=True, pcolorOpts={'norm': LogNorm()}
)[0], ax=ax[0],
)
# Plot mu
murplt = self.mur.reshape(self.mesh.vnC, order='F')
ax[1].set_title('$\mu_r$')
plt.colorbar(mesh2D.plotImage(
discretize.utils.mkvc(murplt[:, 0, :]), ax=ax[1], mirror=True)[0],
ax=ax[1]
)
plt.tight_layout()
return ax
def test_getInterpMatCartMesh_Faces(self):
Mr = discretize.TensorMesh([100, 100, 2], x0="CC0")
Mc = discretize.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):
maps2test2D = [M for M in dir(maps) if M not in MAPS_TO_EXCLUDE_2D]
maps2test3D = [M for M in dir(maps) if M not in MAPS_TO_EXCLUDE_3D]
self.maps2test2D = [
getattr(maps, M) for M in maps2test2D
if (inspect.isclass(getattr(maps, M))
and issubclass(getattr(maps, M), maps.IdentityMap))
]
self.maps2test3D = [
getattr(maps, M) for M in maps2test3D
if inspect.isclass(getattr(maps, M))
and issubclass(getattr(maps, M), maps.IdentityMap)
]
a = np.array([1, 1, 1])
b = np.array([1, 2])
self.mesh2 = discretize.TensorMesh([a, b], x0=np.array([3, 5]))
self.mesh3 = discretize.TensorMesh([a, b, [3, 4]],
x0=np.array([3, 5, 2]))
self.mesh22 = discretize.TensorMesh([b, a], x0=np.array([3, 5]))
self.meshCyl = discretize.CylMesh([10.0, 1.0, 10.0], x0="00C")
def test_getInterpMatCartMesh_Edges2Faces(self):
Mr = discretize.TensorMesh([100, 100, 2], x0="CC0")
Mc = discretize.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_simpleInter(self):
M = discretize.CylMesh([4, 1, 1])
locs = np.r_[0, 0, 0.5]
fx = np.array([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
self.assertTrue(np.all(fx == M.getInterpolationMat(locs, "Fx").todense()))
fz = np.array([[0.0, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0]])
self.assertTrue(np.all(fz == M.getInterpolationMat(locs, "Fz").todense()))
def setUp(self):
n = 2
self.mesh = discretize.CylMesh([n, 1, n])
self.face_vec = np.random.rand(self.mesh.nF)
self.edge_vec = np.random.rand(self.mesh.nE)
# make up a smooth function
self.x0 = 2 * self.mesh.gridCC[:, 0]**2 + self.mesh.gridCC[:, 2]**4
def test_errors(self):
h1 = np.random.rand(16)
h1 /= h1.sum()
h2 = np.random.rand(16)
h2 /= h2.sum()
mesh1D = discretize.TensorMesh([h1])
mesh2D = discretize.TensorMesh([h1, h1])
mesh3D = discretize.TensorMesh([h1, h1, h1])
hr = np.r_[1, 1, 0.5]
hz = np.r_[2, 1]
meshCyl = discretize.CylMesh([hr, 1, hz], np.r_[0.0, 0.0, 0.0])
mesh2 = discretize.TreeMesh([h2, h2])
mesh2.insert_cells([0.75, 0.75], [4])
with self.assertRaises(TypeError):
# Gives a wrong typed object to the function
volume_average(mesh1D, h1)
with self.assertRaises(NotImplementedError):
# Gives a wrong typed mesh
volume_average(meshCyl, mesh2)
with self.assertRaises(ValueError):
# Gives mismatching mesh dimensions
volume_average(mesh2D, mesh3D)
model1 = np.random.randn(mesh2D.nC)
bad_model1 = np.random.randn(3)
bad_model2 = np.random.rand(1)
# gives input values with incorrect lengths
with self.assertRaises(ValueError):
volume_average(mesh2D, mesh2, bad_model1)
with self.assertRaises(ValueError):
volume_average(mesh2D, mesh2, model1, bad_model2)
def test_getInterpMatCartMesh_Faces2Edges(self):
Mr = discretize.TensorMesh([100, 100, 2], x0="CC0")
Mc = discretize.CylMesh(
[np.ones(10) / 5, 1, 10], x0="0C0", cartesianOrigin=[-0.2, -0.2, 0]
)
self.assertTrue((Mc.cartesianOrigin == [-0.2, -0.2, 0]).all())
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_cartesianGrid(self):
mesh = discretize.CylMesh([1, 4, 1])
root2over2 = np.sqrt(2.0) / 2.0
# cell centers
cartCC = mesh.cartesianGrid("CC")
self.assertTrue(
np.allclose(cartCC[:, 0],
0.5 * root2over2 * np.r_[1.0, -1.0, -1.0, 1.0]))
self.assertTrue(
np.allclose(cartCC[:, 1],
0.5 * root2over2 * np.r_[1.0, 1.0, -1.0, -1.0]))
self.assertTrue(np.allclose(cartCC[:, 2], 0.5 * np.ones(4)))
# nodes
cartN = mesh.cartesianGrid("N")
self.assertTrue(
np.allclose(cartN[:, 0],
np.hstack(2 * [0.0, 1.0, 0.0, -1.0, 0.0])))
self.assertTrue(
np.allclose(cartN[:, 1],
np.hstack(2 * [0.0, 0.0, 1.0, 0.0, -1.0])))
self.assertTrue(
np.allclose(cartN[:, 2], np.hstack(5 * [0.0] + 5 * [1.0])))
def setUp(self):
n = 10
hx = np.random.rand(n)
hy = np.random.rand(n)
hy = hy * 2 * np.pi / hy.sum()
hz = np.random.rand(n)
self.mesh = discretize.CylMesh([hx, hy, hz])
def setUp(self):
hx = utils.meshTensor([(1, 1)])
htheta = utils.meshTensor([(1.0, 4)])
htheta = htheta * 2 * np.pi / htheta.sum()
hz = hx
self.mesh = discretize.CylMesh([hx, htheta, hz])
def test_getInterpMatCartMesh_Edges(self):
Mr = discretize.TensorMesh([100, 100, 2], x0='CC0')
Mc = discretize.CylMesh([np.ones(10) / 5, 1, 10],
x0='0C0',
cartesianOrigin=[-0.2, -0.2, 0])
Pe = Mc.getInterpolationMatCartMesh(Mr, 'E')
me = np.ones(Mc.nE)
ecart = Pe * me
excc = Mr.aveEx2CC * Mr.r(ecart, 'E', 'Ex')
eycc = Mr.aveEy2CC * Mr.r(ecart, 'E', 'Ey')
ezcc = Mr.aveEz2CC * 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]) - 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 plot_slice(mesh,
v,
ax=None,
clim=None,
pcolorOpts=None,
theta_ind=0,
cb_extend=None,
show_cb=True):
"""
Plot a cell centered property
:param numpy.array prop: cell centered property to plot
:param matplotlib.axes ax: axis
:param numpy.array clim: colorbar limits
:param dict pcolorOpts: dictionary of pcolor options
"""
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(6, 4))
if pcolorOpts is None:
pcolorOpts = {}
# generate a 2D mesh for plotting slices
mesh2D = discretize.CylMesh([mesh.hx, 1., mesh.hz], x0=mesh.x0)
vplt = v.reshape(mesh.vnC, order='F')
plotme = discretize.utils.mkvc(vplt[:, theta_ind, :])
if not mesh.isSymmetric:
theta_ind_mirror = (theta_ind + int(mesh.vnC[1] / 2) if
theta_ind < int(mesh.vnC[1] / 2) else theta_ind -
int(mesh.vnC[1] / 2))
mirror_data = discretize.utils.mkvc(vplt[:, theta_ind_mirror, :])
else:
mirror_data = plotme
out = mesh2D.plotImage(plotme,
ax=ax,
mirror=True,
mirror_data=mirror_data,
pcolorOpts=pcolorOpts,
clim=clim)
out += (ax, )
if show_cb:
cb = plt.colorbar(
out[0],
ax=ax,
extend=cb_extend if cb_extend is not None else "neither")
out += (cb, )
if clim is not None:
cb.set_clim(clim)
cb.update_ticks()
return out
def setupMeshModel():
cs = 10.0
nc = 20.0
npad = 15.0
hx = [(cs, nc), (cs, npad, 1.3)]
hz = [(cs, npad, -1.3), (cs, nc), (cs, npad, 1.3)]
mesh = discretize.CylMesh([hx, 1.0, hz], "0CC")
muMod = 1 + MuMax * np.random.randn(mesh.nC)
sigmaMod = np.random.randn(mesh.nC)
return mesh, muMod, sigmaMod
def plotLinesFx(mesh,
field,
pltType='semilogy',
ax=None,
theta_ind=0,
xlim=[0., 2500.],
zloc=0.,
real_or_imag='real',
color_ind=0,
color=None,
label=None,
linestyle='-',
alpha=None):
mesh2D = discretize.CylMesh([mesh.hx, 1., mesh.hz], x0=mesh.x0)
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
fplt = face3DthetaSlice(mesh, field, theta_ind=theta_ind)
fx = discretize.utils.mkvc(fplt[:mesh2D.vnF[0]].reshape(
[mesh2D.vnFx[0], mesh2D.vnFx[2]], order='F'))
xind = ((mesh2D.gridFx[:, 0] > xlim[0]) & (mesh2D.gridFx[:, 0] < xlim[1]))
zind = ((mesh2D.gridFx[:, 2] > -mesh2D.hz.min() + zloc) &
(mesh2D.gridFx[:, 2] < zloc))
pltind = xind & zind
fx = getattr(fx[pltind], real_or_imag)
x = mesh2D.gridFx[pltind, 0]
if pltType in ['semilogy', 'loglog']:
getattr(ax, pltType)(
x,
-fx,
'--',
color=color if color is not None else 'C{}'.format(color_ind))
getattr(ax, pltType)(
x,
fx.real,
linestyle,
color=color if color is not None else 'C{}'.format(color_ind),
label=label,
alpha=alpha)
ax.grid('both', linestyle=linestyle, linewidth=0.4, color=[0.8, 0.8, 0.8])
ax.set_xlabel('distance from well (m)')
plt.tight_layout()
return ax
def test_areas(self):
mesh = discretize.CylMesh([1, 2, 1])
areas = np.hstack([[np.pi]*2, [1]*2, [np.pi/2]*4])
self.assertTrue(np.all(mesh.area == areas))
edges = np.hstack([[1]*4, [np.pi]*4, [1]*3])
self.assertTrue(np.all(mesh.edge == edges))
mesh = discretize.CylMesh([2, 5, 3])
hangingF = np.hstack([
getattr(mesh, '_ishangingF{}'.format(dim))
for dim in ['x', 'y', 'z']
])
self.assertTrue(np.all(mesh._areaFull[~hangingF] == mesh.area))
hangingE = np.hstack([
getattr(mesh, '_ishangingE{}'.format(dim))
for dim in ['x', 'y', 'z']
])
self.assertTrue(np.all(mesh._edgeFull[~hangingE] == mesh.edge))
def test_areas(self):
mesh = discretize.CylMesh([1, 2, 1])
areas = np.hstack([[np.pi] * 2, [1] * 2, [np.pi / 2] * 4])
self.assertTrue(np.all(mesh.area == areas))
edges = np.hstack([[1] * 4, [np.pi] * 4, [1] * 3])
self.assertTrue(np.all(mesh.edge == edges))
mesh = discretize.CylMesh([2, 5, 3])
hangingF = np.hstack([
getattr(mesh, "_ishanging_faces_{}".format(dim))
for dim in ["x", "y", "z"]
])
self.assertTrue(np.all(mesh._face_areas_full[~hangingF] == mesh.area))
hangingE = np.hstack([
getattr(mesh, "_ishanging_edges_{}".format(dim))
for dim in ["x", "y", "z"]
])
self.assertTrue(
np.all(mesh._edge_lengths_full[~hangingE] == mesh.edge))
def test_indActive_nc_residual(self):
# x-direction
cs, ncx, ncz, npad = 1.0, 10.0, 10.0, 20
hx = [(cs, ncx), (cs, npad, 1.3)]
# z direction
npad = 12
temp = np.logspace(np.log10(1.0), np.log10(12.0), 19)
temp_pad = temp[-1] * 1.3**np.arange(npad)
hz = np.r_[temp_pad[::-1], temp[::-1], temp, temp_pad]
mesh = discretize.CylMesh([hx, 1, hz], "00C")
active = mesh.vectorCCz < 0.0
reg = regularization.Simple(mesh, indActive=active)
self.assertTrue(reg._nC_residual == len(active.nonzero()[0]))
def run(plotIt=True):
sig_halfspace = 1e-6
sig_sphere = 1e0
sig_air = 1e-8
sphere_z = -50.
sphere_radius = 30.
# x-direction
cs = 1
nc = np.ceil(2.5*(- (sphere_z-sphere_radius))/cs)
# define a mesh
mesh = discretize.CylMesh([[(cs, nc)], 1, [(cs, nc)]], x0='00C')
# Put the model on the mesh
sigma = sig_air*np.ones(mesh.nC) # start with air cells
sigma[mesh.gridCC[:, 2] < 0.] = sig_halfspace # cells below the earth
# indices of the sphere
sphere_ind = (
(mesh.gridCC[:, 0]**2 + (mesh.gridCC[:, 2] - sphere_z)**2) <=
sphere_radius**2
)
sigma[sphere_ind] = sig_sphere # sphere
if plotIt is False:
return
# Plot a cross section through the mesh
fig, ax = plt.subplots(2, 1)
# Set a nice colormap!
plt.set_cmap(plt.get_cmap('viridis'))
plt.colorbar(mesh.plotImage(np.log10(sigma), ax=ax[0])[0], ax=ax[0])
ax[0].set_title('mirror = False')
ax[0].axis('equal')
ax[0].set_xlim([-200., 200.])
plt.colorbar(
mesh.plotImage(np.log10(sigma), ax=ax[1], mirror=True)[0], ax=ax[1]
)
ax[1].set_title('mirror = True')
ax[1].axis('equal')
ax[1].set_xlim([-200., 200.])
plt.tight_layout()
def test_getInterpMatCartMesh_Cells2Nodes(self):
Mr = discretize.TensorMesh([100, 100, 2], x0="CC0")
Mc = discretize.CylMesh(
[np.ones(10) / 5, 1, 10], x0="0C0", cartesianOrigin=[-0.2, -0.2, 0]
)
mc = np.arange(Mc.nC)
xr = np.linspace(0, 0.4, 50)
xc = np.linspace(0, 0.4, 50) + 0.2
Pr = Mr.getInterpolationMat(
np.c_[xr, np.ones(50) * -0.2, np.ones(50) * 0.5], "N"
)
Pc = Mc.getInterpolationMat(np.c_[xc, np.zeros(50), np.ones(50) * 0.5], "CC")
Pc2r = Mc.getInterpolationMatCartMesh(Mr, "CC", locTypeTo="N")
assert np.abs(Pr * (Pc2r * mc) - Pc * mc).max() < 1e-3
def setUp(self):
cs = 10
nc = 20
npad = 10
mesh = discretize.CylMesh([
[(cs, nc), (cs, npad, 1.3)],
np.r_[2 * np.pi],
[(cs, npad, -1.3), (cs, nc), (cs, npad, 1.3)],
])
mesh.x0 = np.r_[0.0, 0.0, -mesh.hz[:npad + nc].sum()]
# receivers
rx_x = np.linspace(10, 200, 20)
rx_z = np.r_[-5]
rx_locs = utils.ndgrid([rx_x, np.r_[0], rx_z])
rx_list = [
dc.receivers.BaseRx(rx_locs, projField="e", orientation="x")
]
# sources
src_a = np.r_[0.0, 0.0, -5.0]
src_b = np.r_[55.0, 0.0, -5.0]
src_list = [
dc.sources.Dipole(rx_list, locationA=src_a, locationB=src_b)
]
self.mesh = mesh
self.survey = dc.survey.Survey(src_list)
self.sigma_map = maps.ExpMap(mesh) * maps.InjectActiveCells(
mesh, mesh.gridCC[:, 2] <= 0, np.log(1e-8))
self.prob = dc.simulation.Simulation3DCellCentered(
mesh=mesh,
survey=self.survey,
sigmaMap=self.sigma_map,
Solver=Pardiso,
bc_type="Dirichlet",
)
def setUp(self):
target_mur = [1, 50, 100, 200]
target_l = 500
target_r = 50
sigma_back = 1e-5
radius_loop = 100
model_names = ["target_{}".format(mur) for mur in target_mur]
# Set up a Cyl mesh
csx = 5.0 # cell size in the x-direction
csz = 5.0 # cell size in the z-direction
domainx = 100 # go out 500m from the well
# padding parameters
npadx, npadz = 15, 15 # number of padding cells
pfx = 1.4 # expansion factor for the padding to infinity
pfz = 1.4
ncz = int(target_l / csz)
mesh = discretize.CylMesh([
[(csx, int(domainx / csx)), (csx, npadx, pfx)],
1,
[(csz, npadz, -pfz), (csz, ncz), (csz, npadz, pfz)],
])
mesh.x0 = [0, 0, -mesh.hz[:npadz + ncz].sum()]
# Plot the mesh
if plotIt:
mesh.plotGrid()
plt.show()
self.radius_loop = radius_loop
self.target_mur = target_mur
self.target_l = target_l
self.target_r = target_r
self.sigma_back = sigma_back
self.model_names = model_names
self.mesh = mesh
# receivers
rx_x = np.linspace(-175.0, 175.0, 8)
rx_y = rx_x.copy()
rx_z = np.r_[175.0]
rx_locs = utils.ndgrid(rx_x, rx_y, rx_z)
# mesh
csx, ncx, npadx = 25.0, 16, 10
csz, ncz, npadz = 25.0, 8, 10
pf = 1.5
# primary mesh
hx = [(csx, ncx), (csx, npadx, pf)]
hz = [(csz, npadz, -pf), (csz, ncz), (csz, npadz, pf)]
meshp = discretize.CylMesh([hx, 1.0, hz], x0="0CC")
# secondary mesh
h = [(csz, npadz - 4, -pf), (csz, ncz), (csz, npadz - 4, pf)]
meshs = discretize.TensorMesh(3 * [h], x0="CCC")
# mappings
primaryMapping = (maps.ExpMap(meshp) * maps.SurjectFull(meshp) *
maps.Projection(nP=8, index=[0]))
mapping = (
maps.ExpMap(meshs) * maps.ParametricBlockInLayer(meshs) *
maps.Projection(nP=8, index=np.hstack([np.r_[0], np.arange(0, 8)])))
primaryMap2Meshs = (maps.ExpMap(meshs) * maps.SurjectFull(meshs) *
maps.Projection(nP=8, index=[0]))
csx = 5. # core cell size in the x-direction
csz = 5. # core cell size in the z-direction
domainx = 100 # use a uniform cell size out to a radius of 100m
# padding parameters
npadx, npadz = 15, 15 # number of padding cells
pfx = 1.4 # expansion factor for the padding to infinity in the x-direction
pfz = 1.4 # expansion factor for the padding to infinity in the z-direction
ncz = int(target_l/csz) # number of z cells in the core region
# create the cyl mesh
mesh = discretize.CylMesh([
[(csx, int(domainx/csx)), (csx, npadx, pfx)],
1,
[(csz, npadz, -pfz), (csz, ncz), (csz, npadz, pfz)]
])
# put the origin at the top of the target
mesh.x0 = [0, 0, -mesh.hz[:npadz + ncz].sum()]
# plot the mesh
mesh.plotGrid()
###############################################################################
# Assign physical properties on the mesh
mur_model = np.ones(mesh.nC)
def test_active_from_xyz(self):
# Create 3D topo
[xx, yy] = np.meshgrid(np.linspace(-200, 200, 50),
np.linspace(-200, 200, 50))
b = 50
A = 50
zz = A * np.exp(-0.5 * ((xx / b)**2.0 + (yy / b)**2.0))
h = [5.0, 5.0, 5.0]
# Test 1D Mesh
topo1D = zz[25, :].ravel()
mesh1D = discretize.TensorMesh([np.ones(10) * 20], x0="C")
indtopoCC = active_from_xyz(mesh1D,
topo1D,
grid_reference="CC",
method="nearest")
indtopoN = active_from_xyz(mesh1D,
topo1D,
grid_reference="N",
method="nearest")
self.assertEqual(indtopoCC.sum(), 3)
self.assertEqual(indtopoN.sum(), 2)
# Test 2D Tensor mesh
topo2D = np.c_[xx[25, :].ravel(), zz[25, :].ravel()]
mesh_tensor = discretize.TensorMesh([[(h[0], 24)], [(h[1], 20)]],
x0="CC")
indtopoCC = active_from_xyz(mesh_tensor,
topo2D,
grid_reference="CC",
method="nearest")
indtopoN = active_from_xyz(mesh_tensor,
topo2D,
grid_reference="N",
method="nearest")
self.assertEqual(indtopoCC.sum(), 434)
self.assertEqual(indtopoN.sum(), 412)
# test 2D Curvilinear mesh
nodes_x = mesh_tensor.gridN[:, 0].reshape(mesh_tensor.vnN, order="F")
nodes_y = mesh_tensor.gridN[:, 1].reshape(mesh_tensor.vnN, order="F")
mesh_curvi = discretize.CurvilinearMesh([nodes_x, nodes_y])
indtopoCC = active_from_xyz(mesh_curvi,
topo2D,
grid_reference="CC",
method="nearest")
indtopoN = active_from_xyz(mesh_curvi,
topo2D,
grid_reference="N",
method="nearest")
self.assertEqual(indtopoCC.sum(), 434)
self.assertEqual(indtopoN.sum(), 412)
# Test 2D Tree mesh
mesh_tree = mesh_builder_xyz(topo2D, h[:2], mesh_type="TREE")
mesh_tree = refine_tree_xyz(
mesh_tree,
topo2D,
method="surface",
octree_levels=[1],
octree_levels_padding=None,
finalize=True,
)
indtopoCC = active_from_xyz(mesh_tree,
topo2D,
grid_reference="CC",
method="nearest")
indtopoN = active_from_xyz(mesh_tree,
topo2D,
grid_reference="N",
method="nearest")
self.assertEqual(indtopoCC.sum(), 167)
self.assertEqual(indtopoN.sum(), 119)
# Test 3D Tensor meshes
topo3D = np.c_[xx.ravel(), yy.ravel(), zz.ravel()]
mesh_tensor = discretize.TensorMesh(
[[(h[0], 24)], [(h[1], 20)], [(h[2], 30)]], x0="CCC")
indtopoCC = active_from_xyz(mesh_tensor,
topo3D,
grid_reference="CC",
method="nearest")
indtopoN = active_from_xyz(mesh_tensor,
topo3D,
grid_reference="N",
method="nearest")
self.assertEqual(indtopoCC.sum(), 10496)
self.assertEqual(indtopoN.sum(), 10084)
# test 3D Curvilinear mesh
nodes_x = mesh_tensor.gridN[:, 0].reshape(mesh_tensor.vnN, order="F")
nodes_y = mesh_tensor.gridN[:, 1].reshape(mesh_tensor.vnN, order="F")
nodes_z = mesh_tensor.gridN[:, 2].reshape(mesh_tensor.vnN, order="F")
mesh_curvi = discretize.CurvilinearMesh([nodes_x, nodes_y, nodes_z])
indtopoCC = active_from_xyz(mesh_curvi,
topo3D,
grid_reference="CC",
method="nearest")
indtopoN = active_from_xyz(mesh_curvi,
topo3D,
grid_reference="N",
method="nearest")
self.assertEqual(indtopoCC.sum(), 10496)
self.assertEqual(indtopoN.sum(), 10084)
# Test 3D Tree mesh
mesh_tree = mesh_builder_xyz(topo3D, h, mesh_type="TREE")
mesh_tree = refine_tree_xyz(
mesh_tree,
topo3D,
method="surface",
octree_levels=[1],
octree_levels_padding=None,
finalize=True,
)
indtopoCC = active_from_xyz(mesh_tree,
topo3D,
grid_reference="CC",
method="nearest")
indtopoN = active_from_xyz(mesh_tree,
topo3D,
grid_reference="N",
method="nearest")
self.assertIn(indtopoCC.sum(), [6292, 6299])
self.assertIn(indtopoN.sum(), [4632, 4639])
# Test 3D CYL Mesh
ncr = 10 # number of mesh cells in r
ncz = 15 # number of mesh cells in z
dr = 15 # cell width r
dz = 10 # cell width z
npad_r = 4 # number of padding cells in r
npad_z = 4 # number of padding cells in z
exp_r = 1.25 # expansion rate of padding cells in r
exp_z = 1.25 # expansion rate of padding cells in z
hr = [(dr, ncr), (dr, npad_r, exp_r)]
hz = [(dz, npad_z, -exp_z), (dz, ncz), (dz, npad_z, exp_z)]
# A value of 1 is used to define the discretization in phi for this case.
mesh_cyl = discretize.CylMesh([hr, 1, hz], x0="00C")
indtopoCC = active_from_xyz(mesh_cyl,
topo3D,
grid_reference="CC",
method="nearest")
indtopoN = active_from_xyz(mesh_cyl,
topo3D,
grid_reference="N",
method="nearest")
self.assertEqual(indtopoCC.sum(), 183)
self.assertEqual(indtopoN.sum(), 171)
htheta = meshTensor([(1.0, 4)])
htheta = htheta * 2 * np.pi / htheta.sum()
mesh_cyl2 = discretize.CylMesh([hr, htheta, hz], x0="00C")
with self.assertRaises(NotImplementedError):
indtopoCC = active_from_xyz(mesh_cyl2,
topo3D,
grid_reference="CC",
method="nearest")
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 = 1.0, 10.0, 10.0, 20
hx = [(cs, ncx), (cs, npad, 1.3)]
npad = 12
temp = np.logspace(np.log10(1.0), np.log10(12.0), 19)
temp_pad = temp[-1] * 1.3**np.arange(npad)
hz = np.r_[temp_pad[::-1], temp[::-1], temp, temp_pad]
mesh = discretize.CylMesh([hx, 1, hz], "00C")
active = mesh.vectorCCz < 0.0
# 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
relative = 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,
relative=relative,
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 = 1.0 / (d + 100.0)**2.0
w = utils.sdiag(1.0 / 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)
inf = temp_dobs / abs(bp) * 1e6
print(inf.min(), inf.max())
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()
print(vmin, vmax)
cb = plt.colorbar(
out[0],
ticks=np.logspace(np.log10(vmin), np.log10(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": vmin,
"vmax": vmax,
"cmap": "viridis"
},
ax=ax2,
)
cb = plt.colorbar(
out[0],
ticks=np.logspace(np.log10(vmin), np.log10(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)
def run(plotIt=True, saveFig=False):
# Set up cylindrically symmeric mesh
cs, ncx, ncz, npad = 10.0, 15, 25, 13 # padded cyl mesh
hx = [(cs, ncx), (cs, npad, 1.3)]
hz = [(cs, npad, -1.3), (cs, ncz), (cs, npad, 1.3)]
mesh = discretize.CylMesh([hx, 1, hz], "00C")
# Conductivity model
layerz = np.r_[-200.0, -100.0]
layer = (mesh.vectorCCz >= layerz[0]) & (mesh.vectorCCz <= layerz[1])
active = mesh.vectorCCz < 0.0
sig_half = 1e-2 # Half-space conductivity
sig_air = 1e-8 # Air conductivity
sig_layer = 5e-2 # Layer conductivity
sigma = np.ones(mesh.nCz) * sig_air
sigma[active] = sig_half
sigma[layer] = sig_layer
# Mapping
actMap = maps.InjectActiveCells(mesh, active, np.log(1e-8), nC=mesh.nCz)
mapping = maps.ExpMap(mesh) * maps.SurjectVertical1D(mesh) * actMap
mtrue = np.log(sigma[active])
# ----- FDEM problem & survey ----- #
rxlocs = utils.ndgrid([np.r_[50.0], np.r_[0], np.r_[0.0]])
bzr = FDEM.Rx.PointMagneticFluxDensitySecondary(rxlocs, "z", "real")
bzi = FDEM.Rx.PointMagneticFluxDensitySecondary(rxlocs, "z", "imag")
freqs = np.logspace(2, 3, 5)
srcLoc = np.array([0.0, 0.0, 0.0])
print(
"min skin depth = ",
500.0 / np.sqrt(freqs.max() * sig_half),
"max skin depth = ",
500.0 / np.sqrt(freqs.min() * sig_half),
)
print(
"max x ",
mesh.vectorCCx.max(),
"min z ",
mesh.vectorCCz.min(),
"max z ",
mesh.vectorCCz.max(),
)
source_list = [
FDEM.Src.MagDipole([bzr, bzi], freq, srcLoc, orientation="Z") for freq in freqs
]
surveyFD = FDEM.Survey(source_list)
prbFD = FDEM.Simulation3DMagneticFluxDensity(
mesh, survey=surveyFD, sigmaMap=mapping, solver=Solver
)
rel_err = 0.03
dataFD = prbFD.make_synthetic_data(mtrue, relative_error=rel_err, add_noise=True)
dataFD.noise_floor = np.linalg.norm(dataFD.dclean) * 1e-5
# FDEM inversion
np.random.seed(1)
dmisfit = data_misfit.L2DataMisfit(simulation=prbFD, data=dataFD)
regMesh = discretize.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])
reg = regularization.Simple(regMesh)
opt = optimization.InexactGaussNewton(maxIterCG=10)
invProb = inverse_problem.BaseInvProblem(dmisfit, reg, opt)
# Inversion Directives
beta = directives.BetaSchedule(coolingFactor=4, coolingRate=3)
betaest = directives.BetaEstimate_ByEig(beta0_ratio=1.0, seed=518936)
target = directives.TargetMisfit()
directiveList = [beta, betaest, target]
inv = inversion.BaseInversion(invProb, directiveList=directiveList)
m0 = np.log(np.ones(mtrue.size) * sig_half)
reg.alpha_s = 5e-1
reg.alpha_x = 1.0
prbFD.counter = opt.counter = utils.Counter()
opt.remember("xc")
moptFD = inv.run(m0)
# TDEM problem
times = np.logspace(-4, np.log10(2e-3), 10)
print(
"min diffusion distance ",
1.28 * np.sqrt(times.min() / (sig_half * mu_0)),
"max diffusion distance ",
1.28 * np.sqrt(times.max() / (sig_half * mu_0)),
)
rx = TDEM.Rx.PointMagneticFluxDensity(rxlocs, times, "z")
src = TDEM.Src.MagDipole(
[rx],
waveform=TDEM.Src.StepOffWaveform(),
location=srcLoc, # same src location as FDEM problem
)
surveyTD = TDEM.Survey([src])
prbTD = TDEM.Simulation3DMagneticFluxDensity(
mesh, survey=surveyTD, sigmaMap=mapping, solver=Solver
)
prbTD.time_steps = [(5e-5, 10), (1e-4, 10), (5e-4, 10)]
rel_err = 0.03
dataTD = prbTD.make_synthetic_data(mtrue, relative_error=rel_err, add_noise=True)
dataTD.noise_floor = np.linalg.norm(dataTD.dclean) * 1e-5
# TDEM inversion
dmisfit = data_misfit.L2DataMisfit(simulation=prbTD, data=dataTD)
regMesh = discretize.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])
reg = regularization.Simple(regMesh)
opt = optimization.InexactGaussNewton(maxIterCG=10)
invProb = inverse_problem.BaseInvProblem(dmisfit, reg, opt)
# directives
beta = directives.BetaSchedule(coolingFactor=4, coolingRate=3)
betaest = directives.BetaEstimate_ByEig(beta0_ratio=1.0, seed=518936)
target = directives.TargetMisfit()
directiveList = [beta, betaest, target]
inv = inversion.BaseInversion(invProb, directiveList=directiveList)
m0 = np.log(np.ones(mtrue.size) * sig_half)
reg.alpha_s = 5e-1
reg.alpha_x = 1.0
prbTD.counter = opt.counter = utils.Counter()
opt.remember("xc")
moptTD = inv.run(m0)
# Plot the results
if plotIt:
plt.figure(figsize=(10, 8))
ax0 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax1 = plt.subplot2grid((2, 2), (0, 1))
ax2 = plt.subplot2grid((2, 2), (1, 1))
fs = 13 # fontsize
matplotlib.rcParams["font.size"] = fs
# Plot the model
# z_true = np.repeat(mesh.vectorCCz[active][1:], 2, axis=0)
# z_true = np.r_[mesh.vectorCCz[active][0], z_true, mesh.vectorCCz[active][-1]]
activeN = mesh.vectorNz <= 0.0 + cs / 2.0
z_true = np.repeat(mesh.vectorNz[activeN][1:-1], 2, axis=0)
z_true = np.r_[mesh.vectorNz[activeN][0], z_true, mesh.vectorNz[activeN][-1]]
sigma_true = np.repeat(sigma[active], 2, axis=0)
ax0.semilogx(sigma_true, z_true, "k-", lw=2, label="True")
ax0.semilogx(
np.exp(moptFD),
mesh.vectorCCz[active],
"bo",
ms=6,
markeredgecolor="k",
markeredgewidth=0.5,
label="FDEM",
)
ax0.semilogx(
np.exp(moptTD),
mesh.vectorCCz[active],
"r*",
ms=10,
markeredgecolor="k",
markeredgewidth=0.5,
label="TDEM",
)
ax0.set_ylim(-700, 0)
ax0.set_xlim(5e-3, 1e-1)
ax0.set_xlabel("Conductivity (S/m)", fontsize=fs)
ax0.set_ylabel("Depth (m)", fontsize=fs)
ax0.grid(which="both", color="k", alpha=0.5, linestyle="-", linewidth=0.2)
ax0.legend(fontsize=fs, loc=4)
# plot the data misfits - negative b/c we choose positive to be in the
# direction of primary
ax1.plot(freqs, -dataFD.dobs[::2], "k-", lw=2, label="Obs (real)")
ax1.plot(freqs, -dataFD.dobs[1::2], "k--", lw=2, label="Obs (imag)")
dpredFD = prbFD.dpred(moptTD)
ax1.loglog(
freqs,
-dpredFD[::2],
"bo",
ms=6,
markeredgecolor="k",
markeredgewidth=0.5,
label="Pred (real)",
)
ax1.loglog(
freqs, -dpredFD[1::2], "b+", ms=10, markeredgewidth=2.0, label="Pred (imag)"
)
ax2.loglog(times, dataTD.dobs, "k-", lw=2, label="Obs")
ax2.loglog(
times,
prbTD.dpred(moptTD),
"r*",
ms=10,
markeredgecolor="k",
markeredgewidth=0.5,
label="Pred",
)
ax2.set_xlim(times.min() - 1e-5, times.max() + 1e-4)
# Labels, gridlines, etc
ax2.grid(which="both", alpha=0.5, linestyle="-", linewidth=0.2)
ax1.grid(which="both", alpha=0.5, linestyle="-", linewidth=0.2)
ax1.set_xlabel("Frequency (Hz)", fontsize=fs)
ax1.set_ylabel("Vertical magnetic field (-T)", fontsize=fs)
ax2.set_xlabel("Time (s)", fontsize=fs)
ax2.set_ylabel("Vertical magnetic field (T)", fontsize=fs)
ax2.legend(fontsize=fs, loc=3)
ax1.legend(fontsize=fs, loc=3)
ax1.set_xlim(freqs.max() + 1e2, freqs.min() - 1e1)
ax0.set_title("(a) Recovered Models", fontsize=fs)
ax1.set_title("(b) FDEM observed vs. predicted", fontsize=fs)
ax2.set_title("(c) TDEM observed vs. predicted", fontsize=fs)
plt.tight_layout(pad=1.5)
if saveFig is True:
plt.savefig("example1.png", dpi=600)
def run(plotIt=True):
# ------------------ MODEL ------------------
sigmaair = 1e-8 # air
sigmaback = 1e-2 # background
sigmacasing = 1e6 # casing
sigmainside = sigmaback # inside the casing
casing_t = 0.006 # 1cm thickness
casing_l = 300 # length of the casing
casing_r = 0.1
casing_a = casing_r - casing_t / 2.0 # inner radius
casing_b = casing_r + casing_t / 2.0 # outer radius
casing_z = np.r_[-casing_l, 0.0]
# ------------------ SURVEY PARAMETERS ------------------
freqs = np.r_[1e-6] # [1e-1, 1, 5] # frequencies
dsz = -300 # down-hole z source location
src_loc = np.r_[0.0, 0.0, dsz]
inf_loc = np.r_[0.0, 0.0, 1e4]
print("Skin Depth: ", [(500.0 / np.sqrt(sigmaback * _)) for _ in freqs])
# ------------------ MESH ------------------
# fine cells near well bore
csx1, csx2 = 2e-3, 60.0
pfx1, pfx2 = 1.3, 1.3
ncx1 = np.ceil(casing_b / csx1 + 2)
# pad nicely to second cell size
npadx1 = np.floor(np.log(csx2 / csx1) / np.log(pfx1))
hx1a = utils.meshTensor([(csx1, ncx1)])
hx1b = utils.meshTensor([(csx1, npadx1, pfx1)])
dx1 = sum(hx1a) + sum(hx1b)
dx1 = np.floor(dx1 / csx2)
hx1b *= (dx1 * csx2 - sum(hx1a)) / sum(hx1b)
# second chunk of mesh
dx2 = 300.0 # uniform mesh out to here
ncx2 = np.ceil((dx2 - dx1) / csx2)
npadx2 = 45
hx2a = utils.meshTensor([(csx2, ncx2)])
hx2b = utils.meshTensor([(csx2, npadx2, pfx2)])
hx = np.hstack([hx1a, hx1b, hx2a, hx2b])
# z-direction
csz = 0.05
nza = 10
# cell size, number of core cells, number of padding cells in the
# x-direction
ncz, npadzu, npadzd = np.int(np.ceil(np.diff(casing_z)[0] / csz)) + 10, 68, 68
# vector of cell widths in the z-direction
hz = utils.meshTensor([(csz, npadzd, -1.3), (csz, ncz), (csz, npadzu, 1.3)])
# Mesh
mesh = discretize.CylMesh(
[hx, 1.0, hz], [0.0, 0.0, -np.sum(hz[: npadzu + ncz - nza])]
)
print(
"Mesh Extent xmax: {0:f},: zmin: {1:f}, zmax: {2:f}".format(
mesh.vectorCCx.max(), mesh.vectorCCz.min(), mesh.vectorCCz.max()
)
)
print("Number of cells", mesh.nC)
if plotIt is True:
fig, ax = plt.subplots(1, 1, figsize=(6, 4))
ax.set_title("Simulation Mesh")
mesh.plotGrid(ax=ax)
# Put the model on the mesh
sigWholespace = sigmaback * np.ones((mesh.nC))
sigBack = sigWholespace.copy()
sigBack[mesh.gridCC[:, 2] > 0.0] = sigmaair
sigCasing = sigBack.copy()
iCasingZ = (mesh.gridCC[:, 2] <= casing_z[1]) & (mesh.gridCC[:, 2] >= casing_z[0])
iCasingX = (mesh.gridCC[:, 0] >= casing_a) & (mesh.gridCC[:, 0] <= casing_b)
iCasing = iCasingX & iCasingZ
sigCasing[iCasing] = sigmacasing
if plotIt is True:
# plotting parameters
xlim = np.r_[0.0, 0.2]
zlim = np.r_[-350.0, 10.0]
clim_sig = np.r_[-8, 6]
# plot models
fig, ax = plt.subplots(1, 1, figsize=(4, 4))
im = mesh.plotImage(np.log10(sigCasing), ax=ax)[0]
im.set_clim(clim_sig)
plt.colorbar(im, ax=ax)
ax.grid(which="both")
ax.set_title("Log_10 (Sigma)")
ax.set_xlim(xlim)
ax.set_ylim(zlim)
# -------------- Sources --------------------
# Define Custom Current Sources
# surface source
sg_x = np.zeros(mesh.vnF[0], dtype=complex)
sg_y = np.zeros(mesh.vnF[1], dtype=complex)
sg_z = np.zeros(mesh.vnF[2], dtype=complex)
nza = 2 # put the wire two cells above the surface
# vertically directed wire
# hook it up to casing at the surface
sgv_indx = (mesh.gridFz[:, 0] > casing_a) & (mesh.gridFz[:, 0] < casing_a + csx1)
sgv_indz = (mesh.gridFz[:, 2] <= +csz * nza) & (mesh.gridFz[:, 2] >= -csz * 2)
sgv_ind = sgv_indx & sgv_indz
sg_z[sgv_ind] = -1.0
# horizontally directed wire
sgh_indx = (mesh.gridFx[:, 0] > casing_a) & (mesh.gridFx[:, 0] <= inf_loc[2])
sgh_indz = (mesh.gridFx[:, 2] > csz * (nza - 0.5)) & (
mesh.gridFx[:, 2] < csz * (nza + 0.5)
)
sgh_ind = sgh_indx & sgh_indz
sg_x[sgh_ind] = -1.0
# hook it up to casing at the surface
sgv2_indx = (mesh.gridFz[:, 0] >= mesh.gridFx[sgh_ind, 0].max()) & (
mesh.gridFz[:, 0] <= inf_loc[2] * 1.2
)
sgv2_indz = (mesh.gridFz[:, 2] <= +csz * nza) & (mesh.gridFz[:, 2] >= -csz * 2)
sgv2_ind = sgv2_indx & sgv2_indz
sg_z[sgv2_ind] = 1.0
# assemble the source
sg = np.hstack([sg_x, sg_y, sg_z])
sg_p = [FDEM.Src.RawVec_e([], _, sg / mesh.area) for _ in freqs]
# downhole source
dg_x = np.zeros(mesh.vnF[0], dtype=complex)
dg_y = np.zeros(mesh.vnF[1], dtype=complex)
dg_z = np.zeros(mesh.vnF[2], dtype=complex)
# vertically directed wire
dgv_indx = mesh.gridFz[:, 0] < csx1 # go through the center of the well
dgv_indz = (mesh.gridFz[:, 2] <= +csz * nza) & (mesh.gridFz[:, 2] > dsz + csz / 2.0)
dgv_ind = dgv_indx & dgv_indz
dg_z[dgv_ind] = -1.0
# couple to the casing downhole
dgh_indx = mesh.gridFx[:, 0] < casing_a + csx1
dgh_indz = (mesh.gridFx[:, 2] < dsz + csz) & (mesh.gridFx[:, 2] >= dsz)
dgh_ind = dgh_indx & dgh_indz
dg_x[dgh_ind] = 1.0
# horizontal part at surface
dgh2_indx = mesh.gridFx[:, 0] <= inf_loc[2] * 1.2
dgh2_indz = sgh_indz.copy()
dgh2_ind = dgh2_indx & dgh2_indz
dg_x[dgh2_ind] = -1.0
# vertical part at surface
dgv2_ind = sgv2_ind.copy()
dg_z[dgv2_ind] = 1.0
# assemble the source
dg = np.hstack([dg_x, dg_y, dg_z])
dg_p = [FDEM.Src.RawVec_e([], _, dg / mesh.area) for _ in freqs]
# ------------ Problem and Survey ---------------
survey = FDEM.Survey(sg_p + dg_p)
problem = FDEM.Simulation3DMagneticField(
mesh, survey=survey, sigmaMap=maps.IdentityMap(mesh), solver=Solver
)
# ------------- Solve ---------------------------
t0 = time.time()
fieldsCasing = problem.fields(sigCasing)
print("Time to solve 2 sources", time.time() - t0)
# Plot current
# current density
jn0 = fieldsCasing[dg_p, "j"]
jn1 = fieldsCasing[sg_p, "j"]
# current
in0 = [mesh.area * fieldsCasing[dg_p, "j"][:, i] for i in range(len(freqs))]
in1 = [mesh.area * fieldsCasing[sg_p, "j"][:, i] for i in range(len(freqs))]
in0 = np.vstack(in0).T
in1 = np.vstack(in1).T
# integrate to get z-current inside casing
inds_inx = (mesh.gridFz[:, 0] >= casing_a) & (mesh.gridFz[:, 0] <= casing_b)
inds_inz = (mesh.gridFz[:, 2] >= dsz) & (mesh.gridFz[:, 2] <= 0)
inds_fz = inds_inx & inds_inz
indsx = [False] * mesh.nFx
inds = list(indsx) + list(inds_fz)
in0_in = in0[np.r_[inds]]
in1_in = in1[np.r_[inds]]
z_in = mesh.gridFz[inds_fz, 2]
in0_in = in0_in.reshape([in0_in.shape[0] // 3, 3])
in1_in = in1_in.reshape([in1_in.shape[0] // 3, 3])
z_in = z_in.reshape([z_in.shape[0] // 3, 3])
I0 = in0_in.sum(1).real
I1 = in1_in.sum(1).real
z_in = z_in[:, 0]
if plotIt is True:
fig, ax = plt.subplots(1, 2, figsize=(12, 4))
ax[0].plot(z_in, np.absolute(I0), z_in, np.absolute(I1))
ax[0].legend(["top casing", "bottom casing"], loc="best")
ax[0].set_title("Magnitude of Vertical Current in Casing")
ax[1].semilogy(z_in, np.absolute(I0), z_in, np.absolute(I1))
ax[1].legend(["top casing", "bottom casing"], loc="best")
ax[1].set_title("Magnitude of Vertical Current in Casing")
ax[1].set_ylim([1e-2, 1.0])
def plotLinesFx(mesh,
srcList,
fields3D=None,
fieldsDC=None,
fieldType='e',
pltType='semilogy',
ax=None,
theta_ind=0,
xlim=[0., 2500.],
zloc=0.):
mesh2D = discretize.CylMesh([mesh.hx, 1., mesh.hz], x0=mesh.x0)
if ax is None:
fig, ax = plt.subplots(1, 2, figsize=(15, 6))
ax = discretize.utils.mkvc(ax)
for i, src in enumerate(srcList):
if fields3D is not None:
field = fields3D[src, fieldType]
fplt = utils.face3DthetaSlice(mesh, field, theta_ind=theta_ind)
fx = discretize.utils.mkvc(fplt[:mesh2D.vnF[0]].reshape(
[mesh2D.vnFx[0], mesh2D.vnFx[2]], order='F'))
xind = ((mesh2D.gridFx[:, 0] > xlim[0]) &
(mesh2D.gridFx[:, 0] < xlim[1]))
zind = ((mesh2D.gridFx[:, 2] > -mesh2D.hz.min()) &
(mesh2D.gridFx[:, 2] < 0.))
pltind = xind & zind
fx = fx[pltind]
x = mesh2D.gridFx[pltind, 0]
label = '{} Hz'.format(src.freq)
getattr(ax[0], pltType)(x, -fx.real, '--', color='C{}'.format(i))
getattr(ax[1], pltType)(x, -fx.imag, '--', color='C{}'.format(i))
getattr(ax[0], pltType)(x,
fx.real,
'-',
color='C{}'.format(i),
label='{} Hz'.format(src.freq))
getattr(ax[1], pltType)(x,
fx.imag,
'-',
color='C{}'.format(i),
label='{} Hz'.format(src.freq))
# plot the DC solution
fxDC = utils.face3DthetaSlice(mesh,
fieldsDC[:, fieldType],
theta_ind=theta_ind)
fxDC = discretize.utils.mkvc(fxDC[:mesh2D.vnF[0]].reshape(
[mesh2D.vnFx[0], mesh2D.vnFx[2]], order='F'))
fxDC = fxDC[pltind]
getattr(ax[0], pltType)(x, -fxDC, '--', color='k')
getattr(ax[0], pltType)(x, fxDC, '-', color='k', label='DC')
ax[0].legend()
ax[1].legend()
ax[0].set_title('${}_r$ real'.format(fieldType))
ax[1].set_title('${}_r$ imag'.format(fieldType))
# [a.set_xlim([2., 1000.]) for a in ax]
[
a.grid('both', linestyle='-', linewidth=0.4, color=[0.8, 0.8, 0.8])
for a in ax
]
[a.set_xlabel('distance from well (m)') for a in ax]
plt.tight_layout()
return ax
