finalize=False)
# Mesh refinement near sources and receivers.
electrode_locations = np.r_[dc_data.survey.locations_a,
dc_data.survey.locations_b,
dc_data.survey.locations_m,
dc_data.survey.locations_n, ]
unique_locations = np.unique(electrode_locations, axis=0)
mesh = refine_tree_xyz(mesh,
unique_locations,
octree_levels=[4, 6, 4],
method="radial",
finalize=False)
# Finalize the mesh
mesh.finalize()
#######################################################
# Project Electrodes to Discretized Topography
# --------------------------------------------
#
# It is important that electrodes are not modeled as being in the air. Even if the
# electrodes are properly located along surface topography, they may lie above
# the discretized topography. This step is carried out to ensure all electrodes
# lie on the discretized surface.
#
# Find cells that lie below surface topography
ind_active = surface2ind_topo(mesh, topo_xyz)
# Extract survey from data object
# Define base mesh (domain and finest discretization)
h = dh * np.ones(nbc)
mesh = TreeMesh([h, h])
# Define corner points for rectangular box
xp, yp = np.meshgrid([120., 240.], [80., 160.])
xy = np.c_[mkvc(xp), mkvc(yp)] # mkvc creates vectors
# Discretize to finest cell size within rectangular box
mesh = refine_tree_xyz(mesh,
xy,
octree_levels=[2, 2],
method='box',
finalize=False)
mesh.finalize() # Must finalize tree mesh before use
mesh.plotGrid(show_it=True)
###############################################
# Intermediate Example and Plotting
# ---------------------------------
#
# The widths of the base mesh cells do not need to be the same in x and y.
# However the number of base mesh cells in x and y each needs to be a power of 2.
#
# Here we show topography-based mesh refinement and refinement about a
# set of points. We also show some aspect of customizing plots. We use the
# keyword argument *octree_levels* to define the rate of cell width increase
# relative to our surface and the set of discrete points about which we are
# refining.
def dpred(self):
target = self.survey.source.target
collection = self.survey.source.collection
'''Mesh'''
# Conductivity in S/m (or resistivity in Ohm m)
background_conductivity = 1e-6
air_conductivity = 1e-8
# Permeability in H/m
background_permeability = mu_0
air_permeability = mu_0
dh = 0.1 # base cell width
dom_width = 20.0 # domain width
# num. base cells
nbc = 2**int(np.round(np.log(dom_width / dh) / np.log(2.0)))
# Define the base mesh
h = [(dh, nbc)]
mesh = TreeMesh([h, h, h], x0="CCC")
# Mesh refinement near transmitters and receivers
mesh = refine_tree_xyz(mesh,
collection.receiver_location,
octree_levels=[2, 4],
method="radial",
finalize=False)
# Refine core mesh region
xp, yp, zp = np.meshgrid([-1.5, 1.5], [-1.5, 1.5], [-6, -4])
xyz = np.c_[mkvc(xp), mkvc(yp), mkvc(zp)]
mesh = refine_tree_xyz(mesh,
xyz,
octree_levels=[0, 6],
method="box",
finalize=False)
mesh.finalize()
'''Maps'''
# Find cells that are active in the forward modeling (cells below surface)
ind_active = mesh.gridCC[:, 2] < 0
# Define mapping from model to active cells
active_sigma_map = maps.InjectActiveCells(mesh, ind_active,
air_conductivity)
active_mu_map = maps.InjectActiveCells(mesh, ind_active,
air_permeability)
# Define model. Models in SimPEG are vector arrays
N = int(ind_active.sum())
model = np.kron(
np.ones((N, 1)), np.c_[background_conductivity,
background_permeability])
ind_cylinder = self.getIndicesCylinder(
[target.position[0], target.position[1], target.position[2]],
target.radius, target.length, [target.pitch, target.roll],
mesh.gridCC)
ind_cylinder = ind_cylinder[ind_active]
model[ind_cylinder, :] = np.c_[target.conductivity,
target.permeability]
# Create model vector and wires
model = mkvc(model)
wire_map = maps.Wires(("sigma", N), ("mu", N))
# Use combo maps to map from model to mesh
sigma_map = active_sigma_map * wire_map.sigma
mu_map = active_mu_map * wire_map.mu
'''Simulation'''
simulation = fdem.simulation.Simulation3DMagneticFluxDensity(
mesh,
survey=self.survey.survey,
sigmaMap=sigma_map,
muMap=mu_map,
Solver=Solver)
'''Predict'''
# Compute predicted data for your model.
dpred = simulation.dpred(model)
dpred = dpred * 1e9
# Data are organized by frequency, transmitter location, then by receiver.
# We had nFreq transmitters and each transmitter had 2 receivers (real and
# imaginary component). So first we will pick out the real and imaginary
# data
bx_real = dpred[0:len(dpred):6]
bx_imag = dpred[1:len(dpred):6]
bx_total = np.sqrt(np.square(bx_real) + np.square(bx_imag))
by_real = dpred[2:len(dpred):6]
by_imag = dpred[3:len(dpred):6]
by_total = np.sqrt(np.square(by_real) + np.square(by_imag))
bz_real = dpred[4:len(dpred):6]
bz_imag = dpred[5:len(dpred):6]
bz_total = np.sqrt(np.square(bz_real) + np.square(bz_imag))
mag_data = np.c_[mkvc(bx_total), mkvc(by_total), mkvc(bz_total)]
if collection.SNR is not None:
mag_data = self.mag_data_add_noise(mag_data, collection.SNR)
data = np.c_[collection.receiver_location, mag_data]
# data = (data, )
return data # 只保留磁场强度数据，删除后面的两个参数
# Compute number of base mesh cells required in x and y
nbcx = 2**int(np.round(np.log(x_length / dx) / np.log(2.)))
nbcy = 2**int(np.round(np.log(y_length / dy) / np.log(2.)))
# Define the base mesh
hx = [(dx, nbcx)]
hy = [(dy, nbcy)]
M = TreeMesh([hx, hy], x0='CC')
# Refine mesh near points
xx = np.linspace(-10000, 10000, 3000)
yy = 400 * np.sin((2 * xx * np.pi) / 1000)
pts = np.c_[mkvc(xx), mkvc(yy)]
M = refine_tree_xyz(M,
pts,
octree_levels=[2, 2],
method='radial',
finalize=False)
M.finalize()
print("\n the mesh has {} cells".format(M))
ccMesh = M.gridCC
print('indices:', np.size(ccMesh))
# We can apply the plotGrid method and output to a specified axes object
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(111)
M.plotGrid(ax=ax)
ax.set_xbound(M.x0[0], M.x0[0] + np.sum(M.hx))
ax.set_ybound(M.x0[1], M.x0[1] + np.sum(M.hy))
ax.set_title('QuadTree Mesh')
def fdem_forward_simulation(detector, target, collection):
"""
Get FDEM simulation data using SimPEG.
Parameters
----------
detector : class Detector
target : class Target
collention : class Collection
Returns
-------
receiver_locations ： numpy.ndarray, shape(N * 3)
All acquisition locations of the detector. Each row represents an
acquisition location and the three columns represent x, y and z axis
locations of an acquisition location.
mag_data : numpy.ndarray, shape(N*3)
All secondary fields of acquisition locations.
mesh : SimPEG mesh
mapped_model : numpy.ndarray
References
----------
https://docs.simpeg.xyz/content/tutorials/01-models_mapping/plot_1_tensor_models.html#sphx-glr-content-tutorials-01-models-mapping-plot-1-tensor-models-py
http://discretize.simpeg.xyz/en/master/tutorials/mesh_generation/4_tree_mesh.html#sphx-glr-tutorials-mesh-generation-4-tree-mesh-py
https://docs.simpeg.xyz/content/tutorials/07-fdem/plot_fwd_2_fem_cyl.html#sphx-glr-content-tutorials-07-fdem-plot-fwd-2-fem-cyl-py
https://docs.simpeg.xyz/content/examples/05-fdem/plot_inv_fdem_loop_loop_2Dinversion.html#sphx-glr-content-examples-05-fdem-plot-inv-fdem-loop-loop-2dinversion-py
"""
# Frequencies being predicted
frequencies = [detector.frequency]
# Conductivity in S/m (or resistivity in Ohm m)
background_conductivity = 1e-6
air_conductivity = 1e-8
# Permeability in H/m
background_permeability = mu_0
air_permeability = mu_0
"""Survey"""
# Defining transmitter locations
acquisition_spacing = collection.spacing
acq_area_xmin, acq_area_xmax = collection.x_min, collection.x_max
acq_area_ymin, acq_area_ymax = collection.y_min, collection.y_max
Nx = int((acq_area_xmax - acq_area_xmin) / acquisition_spacing + 1)
Ny = int((acq_area_ymax - acq_area_ymin) / acquisition_spacing + 1)
xtx, ytx, ztx = np.meshgrid(np.linspace(acq_area_xmin, acq_area_xmax, Nx),
np.linspace(acq_area_ymin, acq_area_ymax, Ny),
[collection.height])
source_locations = np.c_[mkvc(xtx), mkvc(ytx), mkvc(ztx)]
ntx = np.size(xtx)
# Define receiver locations
xrx, yrx, zrx = np.meshgrid(np.linspace(acq_area_xmin, acq_area_xmax, Nx),
np.linspace(acq_area_ymin, acq_area_ymax, Ny),
[collection.height])
receiver_locations = np.c_[mkvc(xrx), mkvc(yrx), mkvc(zrx)]
# Create empty list to store sources
source_list = []
# Each unique location and frequency defines a new transmitter
for ii in range(len(frequencies)):
for jj in range(ntx):
# Define receivers of different type at each location
bxr_receiver = fdem.receivers.PointMagneticFluxDensitySecondary(
receiver_locations[jj, :], "x", "real")
bxi_receiver = fdem.receivers.PointMagneticFluxDensitySecondary(
receiver_locations[jj, :], "x", "imag")
byr_receiver = fdem.receivers.PointMagneticFluxDensitySecondary(
receiver_locations[jj, :], "y", "real")
byi_receiver = fdem.receivers.PointMagneticFluxDensitySecondary(
receiver_locations[jj, :], "y", "imag")
bzr_receiver = fdem.receivers.PointMagneticFluxDensitySecondary(
receiver_locations[jj, :], "z", "real")
bzi_receiver = fdem.receivers.PointMagneticFluxDensitySecondary(
receiver_locations[jj, :], "z", "imag")
receivers_list = [
bxr_receiver, bxi_receiver, byr_receiver, byi_receiver,
bzr_receiver, bzi_receiver
]
# Must define the transmitter properties and associated receivers
source_list.append(
fdem.sources.MagDipole(receivers_list,
frequencies[ii],
source_locations[jj],
orientation="z",
moment=detector.get_mag_moment()))
survey = fdem.Survey(source_list)
'''Mesh'''
dh = 0.1 # base cell width
dom_width = 20.0 # domain width
# num. base cells
nbc = 2**int(np.round(np.log(dom_width / dh) / np.log(2.0)))
# Define the base mesh
h = [(dh, nbc)]
mesh = TreeMesh([h, h, h], x0="CCC")
# Mesh refinement near transmitters and receivers
mesh = refine_tree_xyz(mesh,
receiver_locations,
octree_levels=[2, 4],
method="radial",
finalize=False)
# Refine core mesh region
xp, yp, zp = np.meshgrid([-1.5, 1.5], [-1.5, 1.5], [-6, -4])
xyz = np.c_[mkvc(xp), mkvc(yp), mkvc(zp)]
mesh = refine_tree_xyz(mesh,
xyz,
octree_levels=[0, 6],
method="box",
finalize=False)
mesh.finalize()
'''Maps'''
# Find cells that are active in the forward modeling (cells below surface)
ind_active = mesh.gridCC[:, 2] < 0
# Define mapping from model to active cells
active_sigma_map = maps.InjectActiveCells(mesh, ind_active,
air_conductivity)
active_mu_map = maps.InjectActiveCells(mesh, ind_active, air_permeability)
# Define model. Models in SimPEG are vector arrays
N = int(ind_active.sum())
model = np.kron(np.ones((N, 1)), np.c_[background_conductivity,
background_permeability])
ind_cylinder = getIndicesCylinder(
[target.position[0], target.position[1], target.position[2]],
target.radius, target.length, [target.pitch, target.roll], mesh.gridCC)
ind_cylinder = ind_cylinder[ind_active]
model[ind_cylinder, :] = np.c_[target.conductivity, target.permeability]
# Create model vector and wires
model = mkvc(model)
wire_map = maps.Wires(("sigma", N), ("mu", N))
# Use combo maps to map from model to mesh
sigma_map = active_sigma_map * wire_map.sigma
mu_map = active_mu_map * wire_map.mu
'''Simulation'''
simulation = fdem.simulation.Simulation3DMagneticFluxDensity(
mesh, survey=survey, sigmaMap=sigma_map, muMap=mu_map, Solver=Solver)
'''Predict'''
# Compute predicted data for a your model.
dpred = simulation.dpred(model)
dpred = dpred * 1e9
# Data are organized by frequency, transmitter location, then by receiver.
# We had nFreq transmitters and each transmitter had 2 receivers (real and
# imaginary component). So first we will pick out the real and imaginary
# data
bx_real = dpred[0:len(dpred):6]
bx_imag = dpred[1:len(dpred):6]
bx_total = np.sqrt(np.square(bx_real) + np.square(bx_imag))
by_real = dpred[2:len(dpred):6]
by_imag = dpred[3:len(dpred):6]
by_total = np.sqrt(np.square(by_real) + np.square(by_imag))
bz_real = dpred[4:len(dpred):6]
bz_imag = dpred[5:len(dpred):6]
bz_total = np.sqrt(np.square(bz_real) + np.square(bz_imag))
mag_data = np.c_[mkvc(bx_total), mkvc(by_total), mkvc(bz_total)]
return receiver_locations, mag_data, mesh, sigma_map * model
