def setUp(self):
aSpacing = 2.5
nElecs = 5
surveySize = nElecs * aSpacing - aSpacing
cs = surveySize / nElecs / 4
self.mesh = discretize.TensorMesh(
[
[(cs, 10, -1.3), (cs, surveySize / cs), (cs, 10, 1.3)],
[(cs, 3, -1.3), (cs, 3, 1.3)],
# [(cs, 5, -1.3), (cs, 10)]
],
"CN",
)
survey_end_points = np.array([-surveySize / 2, surveySize / 2, 0, 0])
source_list = generate_dcip_sources_line("dipole-dipole", "volt", "2D",
survey_end_points, 0., 5, 2.5)
self.d_d_survey = dc.survey.Survey(source_list)
source_list = generate_dcip_sources_line("dipole-pole", "volt", "2D",
survey_end_points, 0., 5, 2.5)
self.d_p_survey = dc.survey.Survey(source_list)
source_list = generate_dcip_sources_line("pole-dipole", "volt", "2D",
survey_end_points, 0., 5, 2.5)
self.p_d_survey = dc.survey.Survey(source_list)
source_list = generate_dcip_sources_line("pole-pole", "volt", "2D",
survey_end_points, 0., 5, 2.5)
self.p_p_survey = dc.survey.Survey(source_list)
#
# Define survey line parameters
survey_type = "dipole-dipole"
dimension_type = "2D"
data_type = "volt"
end_locations = np.r_[-400.0, 400.0]
station_separation = 40.0
num_rx_per_src = 10
# Generate source list for DC survey line
source_list = generate_dcip_sources_line(
survey_type,
data_type,
dimension_type,
end_locations,
topo_2d,
num_rx_per_src,
station_separation,
)
# Define survey
survey = dc.survey.Survey(source_list, survey_type=survey_type)
###############################################################
# Create Tree Mesh
# ------------------
#
# Here, we create the Tree mesh that will be used to predict DC data.
#
data_type = "volt"
dimension_type = "3D"
end_locations_list = [
np.r_[-1000.0, 1000.0, 0., 0.], np.r_[-300., -300., -1000.0, 1000.0],
np.r_[300., 300., -1000.0, 1000.0]
]
station_separation = 100.0
num_rx_per_src = 10
# The source lists for each line can be appended to create the source
# list for the whole survey.
source_list = []
for ii in range(0, len(end_locations_list)):
source_list += generate_dcip_sources_line(survey_type, data_type,
dimension_type,
end_locations_list[ii], xyz_topo,
num_rx_per_src,
station_separation)
# Define the survey
survey = dc.survey.Survey(source_list)
###############################################################
# Create OcTree Mesh
# ------------------
#
# Here, we create the OcTree mesh that will be used to predict DC data.
#
dh = 40.0 # base cell width
dom_width_x = 10000.0 # domain width x
def setUp(self):
aSpacing = 2.5
nElecs = 5
surveySize = nElecs * aSpacing - aSpacing
cs = surveySize / nElecs / 4
mesh = discretize.TensorMesh(
[
[(cs, 10, -1.3), (cs, surveySize / cs), (cs, 10, 1.3)],
[(cs, 3, -1.3), (cs, 3, 1.3)],
[(cs, 5, -1.3), (cs, 10)],
],
"CNN",
)
survey_end_points = np.array([-surveySize / 2, surveySize / 2, 0, 0])
source_list = generate_dcip_sources_line(
"dipole-dipole", "volt", "3D", survey_end_points, 0.0, 5, 2.5
)
survey = dc.survey.Survey(source_list)
A = survey.locations_a
B = survey.locations_b
M = survey.locations_m
N = survey.locations_n
# add some other receivers and sources to the mix
# electrode_locations = np.unique(np.r_[A, B, M, N], axis=0)
electrode_locations = survey.electrode_locations
rx_p = dc.receivers.Pole(electrode_locations[[2]])
rx_d = dc.receivers.Dipole(electrode_locations[[2]], electrode_locations[[3]])
tx_pd = dc.sources.Pole([rx_d], electrode_locations[0])
tx_pp = dc.sources.Pole([rx_p], electrode_locations[0])
tx_dp = dc.sources.Dipole(
[rx_p], electrode_locations[0], electrode_locations[1]
)
source_list = survey.source_list
source_list.append(tx_pd)
source_list.append(tx_pp)
source_list.append(tx_dp)
survey = dc.Survey(source_list)
self.survey = survey
# This survey is a mix of d-d, d-p, p-d, and p-p txs and rxs.
self.sim1 = dc.Simulation3DNodal(
survey=survey,
mesh=mesh,
solver=Pardiso,
storeJ=False,
sigmaMap=maps.IdentityMap(mesh),
miniaturize=False,
)
self.sim2 = dc.Simulation3DNodal(
survey=survey,
mesh=mesh,
solver=Pardiso,
storeJ=False,
sigmaMap=maps.IdentityMap(mesh),
miniaturize=True,
)
self.model = np.ones(mesh.nC)
self.f1 = self.sim1.fields(self.model)
self.f2 = self.sim2.fields(self.model)
np.r_[-1000.0, 1000.0, 0.0, 0.0],
np.r_[-300.0, -300.0, -1000.0, 1000.0],
np.r_[300.0, 300.0, -1000.0, 1000.0],
]
station_separation = 100.0
num_rx_per_src = 10
# The source lists for each line can be appended to create the source
# list for the whole survey.
source_list = []
for ii in range(0, len(end_locations_list)):
source_list += generate_dcip_sources_line(
survey_type,
data_type,
dimension_type,
end_locations_list[ii],
xyz_topo,
num_rx_per_src,
station_separation,
)
# Define the survey
dc_survey = dc.survey.Survey(source_list)
###############################################################
# Create OcTree Mesh
# ------------------
#
# Here, we create the OcTree mesh that will be used to predict DC data.
#
