Пример #1
0
# 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.
Пример #2
0
#
# 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,
Пример #3
0
# 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()
Пример #4
0
#
# 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(
Пример #5
0
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,
Пример #6
0
# 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,
Пример #7
0
# ------------------------------------
#
# 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",
Пример #8
0
# 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
#