# 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 #