def run_simulation(fname="tdem_gs_half.h5",
sigma_block=0.01,
sigma_halfspace=0.01):
from SimPEG.EM import TDEM, Analytics, mu_0
import numpy as np
from SimPEG import Mesh, Maps, Utils, EM, Survey
from pymatsolver import Pardiso
cs = 20
ncx, ncy, ncz = 20, 20, 20
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)]
mesh = Mesh.TensorMesh([hx, hy, hz], "CCC")
sigma = np.ones(mesh.nC) * sigma_halfspace
blk_ind = Utils.ModelBuilder.getIndicesBlock(np.r_[-40, -40, -160],
np.r_[40, 40,
-80], mesh.gridCC)
sigma[mesh.gridCC[:, 2] > 0.0] = 1e-8
sigma[blk_ind] = sigma_block
xmin, xmax = -200.0, 200.0
ymin, ymax = -200.0, 200.0
x = mesh.vectorCCx[np.logical_and(mesh.vectorCCx > xmin,
mesh.vectorCCx < xmax)]
y = mesh.vectorCCy[np.logical_and(mesh.vectorCCy > ymin,
mesh.vectorCCy < ymax)]
xyz = Utils.ndgrid(x, y, np.r_[-1.0])
px = np.r_[-200.0, 200.0]
py = np.r_[0.0, 0.0]
pz = np.r_[0.0, 0.0]
srcLoc = np.c_[px, py, pz]
from scipy.interpolate import interp1d
prb = TDEM.Problem3D_b(mesh, sigma=sigma, verbose=True)
prb.Solver = Pardiso
prb.solverOpts = {"is_symmetric": False}
prb.timeSteps = [(1e-3, 10), (2e-5, 10), (1e-4, 10), (5e-4, 10),
(1e-3, 10)]
t0 = 0.01 + 1e-4
out = EM.Utils.VTEMFun(prb.times, 0.01, t0, 200)
wavefun = interp1d(prb.times, out)
waveform = EM.TDEM.Src.RawWaveform(offTime=t0, waveFct=wavefun)
input_currents = wavefun(prb.times)
times = np.logspace(-4, -2, 21)
rx_ex = TDEM.Rx.Point_e(xyz, times + t0, orientation="x")
rx_ey = TDEM.Rx.Point_e(xyz, times + t0, orientation="y")
rx_by = TDEM.Rx.Point_e(xyz, times + t0, orientation="y")
rxList = [rx_ex, rx_ey, rx_by]
src = TDEM.Src.LineCurrent(rxList, loc=srcLoc, waveform=waveform)
survey = TDEM.Survey([src])
survey.pair(prb)
f = prb.fields(sigma)
xyzlim = np.array([[xmin, xmax], [ymin, ymax], [-400, 0.0]])
actinds, meshCore = Utils.ExtractCoreMesh(xyzlim, mesh)
Pex = mesh.getInterpolationMat(meshCore.gridCC, locType="Ex")
Pey = mesh.getInterpolationMat(meshCore.gridCC, locType="Ey")
Pez = mesh.getInterpolationMat(meshCore.gridCC, locType="Ez")
Pfx = mesh.getInterpolationMat(meshCore.gridCC, locType="Fx")
Pfy = mesh.getInterpolationMat(meshCore.gridCC, locType="Fy")
Pfz = mesh.getInterpolationMat(meshCore.gridCC, locType="Fz")
sigma_core = sigma[actinds]
def getEBJcore(src0):
B0 = np.r_[Pfx * f[src0, "b"], Pfy * f[src0, "b"], Pfz * f[src0, "b"]]
E0 = np.r_[Pex * f[src0, "e"], Pey * f[src0, "e"], Pez * f[src0, "e"]]
J0 = Utils.sdiag(np.r_[sigma_core, sigma_core, sigma_core]) * E0
return E0, B0, J0
E, B, J = getEBJcore(src)
tdem_gs = {
"E": E,
"B": B,
"J": J,
"sigma": sigma_core,
"mesh": meshCore.serialize(),
"time": prb.times - t0,
"input_currents": input_currents,
}
dd.io.save(fname, tdem_gs)
def run(plotIt=True, saveFig=False):
# Set up cylindrically symmeric mesh
cs, ncx, ncz, npad = 10., 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 = Mesh.CylMesh([hx, 1, hz], '00C')
# Conductivity model
layerz = np.r_[-200., -100.]
layer = (mesh.vectorCCz >= layerz[0]) & (mesh.vectorCCz <= layerz[1])
active = mesh.vectorCCz < 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.], np.r_[0], np.r_[0.]])
bzr = FDEM.Rx.Point_bSecondary(rxlocs, 'z', 'real')
bzi = FDEM.Rx.Point_bSecondary(rxlocs, 'z', 'imag')
freqs = np.logspace(2, 3, 5)
srcLoc = np.array([0., 0., 0.])
print('min skin depth = ', 500. / np.sqrt(freqs.max() * sig_half),
'max skin depth = ', 500. / np.sqrt(freqs.min() * sig_half))
print('max x ', mesh.vectorCCx.max(), 'min z ', mesh.vectorCCz.min(),
'max z ', mesh.vectorCCz.max())
srcList = [
FDEM.Src.MagDipole([bzr, bzi], freq, srcLoc, orientation='Z')
for freq in freqs
]
surveyFD = FDEM.Survey(srcList)
prbFD = FDEM.Problem3D_b(mesh, sigmaMap=mapping, Solver=Solver)
prbFD.pair(surveyFD)
std = 0.03
surveyFD.makeSyntheticData(mtrue, std)
surveyFD.eps = np.linalg.norm(surveyFD.dtrue) * 1e-5
# FDEM inversion
np.random.seed(1)
dmisfit = DataMisfit.l2_DataMisfit(surveyFD)
regMesh = Mesh.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])
reg = Regularization.Simple(regMesh)
opt = Optimization.InexactGaussNewton(maxIterCG=10)
invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)
# Inversion Directives
beta = Directives.BetaSchedule(coolingFactor=4, coolingRate=3)
betaest = Directives.BetaEstimate_ByEig(beta0_ratio=2.)
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.
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.Point_b(rxlocs, times, 'z')
src = TDEM.Src.MagDipole(
[rx],
waveform=TDEM.Src.StepOffWaveform(),
loc=srcLoc # same src location as FDEM problem
)
surveyTD = TDEM.Survey([src])
prbTD = TDEM.Problem3D_b(mesh, sigmaMap=mapping, Solver=Solver)
prbTD.timeSteps = [(5e-5, 10), (1e-4, 10), (5e-4, 10)]
prbTD.pair(surveyTD)
std = 0.03
surveyTD.makeSyntheticData(mtrue, std)
surveyTD.std = std
surveyTD.eps = np.linalg.norm(surveyTD.dtrue) * 1e-5
# TDEM inversion
dmisfit = DataMisfit.l2_DataMisfit(surveyTD)
regMesh = Mesh.TensorMesh([mesh.hz[mapping.maps[-1].indActive]])
reg = Regularization.Simple(regMesh)
opt = Optimization.InexactGaussNewton(maxIterCG=10)
invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)
# directives
beta = Directives.BetaSchedule(coolingFactor=4, coolingRate=3)
betaest = Directives.BetaEstimate_ByEig(beta0_ratio=2.)
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.
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
ax0.semilogx(sigma[active],
mesh.vectorCCz[active],
'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, -surveyFD.dobs[::2], 'k-', lw=2, label="Obs (real)")
ax1.plot(freqs, -surveyFD.dobs[1::2], 'k--', lw=2, label="Obs (imag)")
dpredFD = surveyFD.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.,
label="Pred (imag)")
ax2.loglog(times, surveyTD.dobs, 'k-', lw=2, label='Obs')
ax2.loglog(times,
surveyTD.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)
[], loc=np.r_[0., 0., 0.], orientation="z", radius=100,
waveform=quarter_sine
)
src_list_magnetostatic = [src_magnetostatic]
src_list_ramp_on = [src_ramp_on]
###############################################################################
# Create the simulations
# ----------------------
#
# To simulate magnetic flux data, we use the b-formulation of Maxwell's
# equations
prob_magnetostatic = TDEM.Problem3D_b(
mesh=mesh, sigmaMap=Maps.IdentityMap(mesh), timeSteps=ramp,
Solver=Pardiso
)
prob_ramp_on = TDEM.Problem3D_b(
mesh=mesh, sigmaMap=Maps.IdentityMap(mesh), timeSteps=ramp,
Solver=Pardiso
)
survey_magnetostatic = TDEM.Survey(srcList=src_list_magnetostatic)
survey_ramp_on = TDEM.Survey(src_list_ramp_on)
prob_magnetostatic.pair(survey_magnetostatic)
prob_ramp_on.pair(survey_ramp_on)
###############################################################################
# Run the long on-time simulation
# -------------------------------
def test_permeable_sources(self):
target_mur = self.target_mur
target_l = self.target_l
target_r = self.target_r
sigma_back = self.sigma_back
model_names = self.model_names
mesh = self.mesh
radius_loop = self.radius_loop
# Assign physical properties on the mesh
def populate_target(mur):
mu_model = np.ones(mesh.nC)
x_inds = mesh.gridCC[:, 0] < target_r
z_inds = (
(mesh.gridCC[:, 2] <= 0) & (mesh.gridCC[:, 2] >= -target_l)
)
mu_model[x_inds & z_inds] = mur
return mu_0 * mu_model
mu_dict = {
key: populate_target(mu) for key, mu in
zip(model_names, target_mur)
}
sigma = np.ones(mesh.nC) * sigma_back
# Plot the models
if plotIt:
xlim = np.r_[-200, 200] # x-limits in meters
zlim = np.r_[-1.5*target_l, 10.] # z-limits in meters. (z-positive up)
fig, ax = plt.subplots(
1, len(model_names), figsize=(6*len(model_names), 5)
)
if len(model_names) == 1:
ax = [ax]
for a, key in zip(ax, model_names):
plt.colorbar(mesh.plotImage(
mu_dict[key], ax=a,
pcolorOpts={'norm': LogNorm()}, # plot on a log-scale
mirror=True
)[0], ax=a)
a.set_title('{}'.format(key), fontsize=13)
# cylMeshGen.mesh.plotGrid(ax=a, slice='theta') # uncomment to plot the mesh on top of this
a.set_xlim(xlim)
a.set_ylim(zlim)
plt.tight_layout()
plt.show()
ramp = [
(1e-5, 20), (1e-4, 20), (3e-4, 20), (1e-3, 20), (3e-3, 20),
(1e-2, 20), (3e-2, 20), (1e-1, 20), (3e-1, 20), (1, 50)
]
timeSteps = ramp
time_mesh = discretize.TensorMesh([ramp])
offTime = 10000
waveform = TDEM.Src.QuarterSineRampOnWaveform(
ramp_on=np.r_[1e-4, 20], ramp_off=offTime - np.r_[1e-4, 0]
)
if plotIt:
wave = np.r_[[waveform.eval(t) for t in time_mesh.gridN]]
plt.plot(time_mesh.gridN, wave)
plt.plot(time_mesh.gridN, np.zeros(time_mesh.nN), '-|', color='k')
plt.show()
src_magnetostatic = TDEM.Src.CircularLoop(
[], loc=np.r_[0., 0., 0.], orientation="z", radius=100,
)
src_ramp_on = TDEM.Src.CircularLoop(
[], loc=np.r_[0., 0., 0.], orientation="z", radius=100,
waveform=waveform
)
src_list = [src_magnetostatic]
src_list_late_ontime = [src_ramp_on]
prob = TDEM.Problem3D_b(
mesh=mesh, timeSteps=timeSteps, sigmaMap=Maps.IdentityMap(mesh),
Solver=Pardiso
)
prob_late_ontime = TDEM.Problem3D_b(
mesh=mesh, timeSteps=timeSteps, sigmaMap=Maps.IdentityMap(mesh),
Solver=Pardiso
)
survey = TDEM.Survey(srcList=src_list)
survey_late_ontime = TDEM.Survey(src_list_late_ontime)
prob.pair(survey)
prob_late_ontime.pair(survey_late_ontime)
fields_dict = {}
for key in model_names:
t = time.time()
print('--- Running {} ---'.format(key))
prob_late_ontime.mu = mu_dict[key]
fields_dict[key] = prob_late_ontime.fields(sigma)
print(" ... done. Elapsed time {}".format(time.time() - t))
print('\n')
b_magnetostatic = {}
b_late_ontime = {}
for key in model_names:
prob.mu = mu_dict[key]
prob.sigma = sigma
b_magnetostatic[key] = src_magnetostatic.bInitial(prob)
prob_late_ontime.mu = mu_dict[key]
b_late_ontime[key] = utils.mkvc(
fields_dict[key][:, 'b', -1]
)
if plotIt:
fig, ax = plt.subplots(
len(model_names), 2, figsize=(3*len(model_names), 5)
)
for i, key in enumerate(model_names):
ax[i][0].semilogy(
np.absolute(b_magnetostatic[key]),
label='magnetostatic'
)
ax[i][0].semilogy(
np.absolute(b_late_ontime[key]), label='late on-time'
)
ax[i][0].legend()
ax[i][1].semilogy(
np.absolute(b_magnetostatic[key] - b_late_ontime[key])
)
plt.tight_layout()
plt.show()
print("Testing TDEM with permeable targets")
passed = []
for key in model_names:
norm_magneotstatic = np.linalg.norm(b_magnetostatic[key])
norm_late_ontime = np.linalg.norm(b_late_ontime[key])
norm_diff = np.linalg.norm(
b_magnetostatic[key] - b_late_ontime[key]
)
passed_test = (
norm_diff / (0.5*(norm_late_ontime + norm_magneotstatic))
< TOL
)
print("\n{}".format(key))
print(
"||magnetostatic||: {:1.2e}, "
"||late on-time||: {:1.2e}, "
"||difference||: {:1.2e} passed?: {}".format(
norm_magneotstatic, norm_late_ontime, norm_diff,
passed_test
)
)
passed += [passed_test]
assert all(passed)
prob.sigma = 1e-4*np.ones(mesh.nC)
v = utils.mkvc(np.random.rand(mesh.nE))
w = utils.mkvc(np.random.rand(mesh.nF))
assert(
np.all(
mesh.getEdgeInnerProduct(1e-4*np.ones(mesh.nC))*v ==
prob.MeSigma*v
)
)
assert(
np.all(
mesh.getEdgeInnerProduct(
1e-4*np.ones(mesh.nC), invMat=True
)*v ==
prob.MeSigmaI*v
)
)
assert(
np.all(
mesh.getFaceInnerProduct(1./1e-4*np.ones(mesh.nC))*w ==
prob.MfRho*w
)
)
assert(
np.all(
mesh.getFaceInnerProduct(
1./1e-4*np.ones(mesh.nC), invMat=True
)*w ==
prob.MfRhoI*w
)
)
prob.rho = 1./1e-3*np.ones(mesh.nC)
v = utils.mkvc(np.random.rand(mesh.nE))
w = utils.mkvc(np.random.rand(mesh.nF))
assert(
np.all(
mesh.getEdgeInnerProduct(1e-3*np.ones(mesh.nC))*v ==
prob.MeSigma*v
)
)
assert(
np.all(
mesh.getEdgeInnerProduct(
1e-3*np.ones(mesh.nC), invMat=True
)*v ==
prob.MeSigmaI*v
)
)
assert(
np.all(
mesh.getFaceInnerProduct(1./1e-3*np.ones(mesh.nC))*w ==
prob.MfRho*w
)
)
assert(
np.all(
mesh.getFaceInnerProduct(
1./1e-3*np.ones(mesh.nC), invMat=True
)*w ==
prob.MfRhoI*w
)
)