# Define survey
survey = dc.survey.Survey_ky(source_list)
# Define the a data object. Uncertainties are added later
dc_data = data.Data(survey, dobs=dobs)
# Plot apparent conductivity using pseudo-section
mpl.rcParams.update({"font.size": 12})
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.05, 0.05, 0.8, 0.9])
plot_pseudosection(
dc_data,
ax=ax1,
survey_type="dipole-dipole",
data_type="appConductivity",
space_type="half-space",
scale="log",
y_values="pseudo-depth",
pcolor_opts={"cmap": "viridis"},
)
ax1.set_title("Apparent Conductivity [S/m]")
plt.show()
#############################################
# Assign Uncertainties
# --------------------
#
# Inversion with SimPEG requires that we define standard deviation on our data.
# This represents our estimate of the noise in our data. For DC data, a relative
# error is applied to each datum.
#
# Here, we demonstrate how to plot 2D data in pseudo-section.
# First, we plot the voltages in pseudo-section as a scatter plot. This
# allows us to visualize the pseudo-sensitivity locations for our survey.
# Next, we plot the apparent conductivities in pseudo-section as a filled
# contour plot.
#
# Plot voltages pseudo-section
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])
plot_pseudosection(
survey,
dobs=np.abs(dpred),
plot_type="scatter",
ax=ax1,
scale="log",
cbar_label="V/A",
scatter_opts={"cmap": mpl.cm.viridis},
)
ax1.set_title("Normalized Voltages")
plt.show()
# Get apparent conductivities from volts and survey geometry
apparent_conductivities = 1 / apparent_resistivity_from_voltage(survey, dpred)
# Plot apparent conductivity pseudo-section
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])
plot_pseudosection(
survey,
# Plot 2D Pseudosections
# ----------------------
#
title_str = [
"East-West Line at Northing = 0 m",
"North-South Line at Easting = -350 m",
"North-South Line at Easting = -350 m",
]
# Plot apparent conductivity pseudo-section
for ii in range(len(survey_2d_list)):
vlim = [apparent_conductivity_3d.min(), apparent_conductivity_3d.max()]
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])
plot_pseudosection(
survey_2d_list[ii],
dobs=apparent_conductivities_2d[ii],
plot_type="contourf",
ax=ax1,
vlim=vlim,
scale="log",
cbar_label="Apparent Conducitivty [S/m]",
mask_topography=True,
contourf_opts={"levels": 30, "cmap": mpl.cm.viridis},
)
ax1.set_title(title_str[ii])
plt.show()
#
# Here, we demonstrate how to plot 2D DC data in pseudo-section.
# First, we plot the voltages in pseudo-section as a scatter plot. This
# allows us to visualize the pseudo-sensitivity locations for our survey.
# Next, we plot the apparent conductivities in pseudo-section as a filled
# contour plot.
#
# Plot voltages pseudo-section
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])
plot_pseudosection(
dc_survey,
dpred_dc,
"scatter",
ax=ax1,
scale="log",
cbar_label="V/A",
scatter_opts={"cmap": mpl.cm.viridis},
)
ax1.set_title("Normalized Voltages")
plt.show()
# Get apparent conductivities from volts and survey geometry
apparent_conductivities = 1 / apparent_resistivity_from_voltage(
dc_survey, dpred_dc)
# Plot apparent conductivity pseudo-section
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])
plot_pseudosection(
ip_survey = ip.survey.Survey(source_list)
# Define the a data object. Uncertainties are added later
dc_data = data.Data(dc_survey, dobs=dobs_dc)
ip_data = data.Data(ip_survey, dobs=dobs_ip)
# Plot apparent conductivity using pseudo-section
mpl.rcParams.update({"font.size": 12})
fig = plt.figure(figsize=(11, 9))
ax1 = fig.add_axes([0.05, 0.55, 0.8, 0.45])
plot_pseudosection(
dc_data,
ax=ax1,
survey_type="dipole-dipole",
data_type="appConductivity",
space_type="half-space",
scale="log",
y_values="pseudo-depth",
)
ax1.set_title("Apparent Conductivity [S/m]")
# Plot apparent chargeability in pseudo-section. Since data are secondary
# potentials, we must normalize by the DC voltage first.
apparent_chargeability = ip_data.dobs / dc_data.dobs
ax2 = fig.add_axes([0.05, 0.05, 0.8, 0.45])
plot_pseudosection(
ip_data,
dobs=apparent_chargeability,
ax=ax2,
# Plot apparent conductivity using pseudo-section
mpl.rcParams.update({"font.size": 12})
apparent_conductivities = 1 / apparent_resistivity_from_voltage(
dc_data.survey, dc_data.dobs
)
# Plot apparent conductivity pseudo-section
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])
plot_pseudosection(
dc_data.survey,
apparent_conductivities,
"contourf",
ax=ax1,
scale="log",
cbar_label="S/m",
mask_topography=True,
contourf_opts={"levels": 20, "cmap": mpl.cm.viridis},
)
ax1.set_title("Apparent Conductivity")
plt.show()
# Plot apparent chargeability in pseudo-section
apparent_chargeability = ip_data.dobs
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])
plot_pseudosection(
ip_data.survey,
apparent_chargeability,
# ------------------------------------
#
# Here, we demonstrate how to plot 2D data in pseudo-section.
# First, we plot the actual data (voltages) in pseudo-section as a scatter plot.
# This allows us to visualize the pseudo-sensitivity locations for our survey.
# Next, we plot the data as apparent conductivities in pseudo-section with a filled
# contour plot.
#
# Plot voltages pseudo-section
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])
plot_pseudosection(
dc_data,
plot_type="scatter",
ax=ax1,
scale="log",
cbar_label="V/A",
scatter_opts={"cmap": mpl.cm.viridis},
)
ax1.set_title("Normalized Voltages")
plt.show()
# Plot apparent conductivity pseudo-section
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.1, 0.15, 0.75, 0.78])
plot_pseudosection(
dc_data,
plot_type="contourf",
ax=ax1,
scale="log",
data_type="apparent conductivity",
# Predict the data by running the simulation. The data are the raw voltage in
# units of volts.
dpred = simulation.dpred(conductivity_model)
# Define a data object (required for pseudo-section plot)
data_obj = data.Data(survey, dobs=dpred)
# Plot apparent conductivity pseudo-section
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_axes([0.05, 0.05, 0.8, 0.9])
plot_pseudosection(
data_obj,
ax=ax1,
survey_type="dipole-dipole",
data_type="appConductivity",
space_type="half-space",
scale="log",
y_values='pseudo-depth',
pcolor_opts={"cmap": "viridis"},
)
ax1.set_title("Apparent Conductivity [S/m]")
plt.show()
#######################################################################
# Optional: Write out dpred
# -------------------------
#
# Write DC resistivity data, topography and true model
#