.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/tutorials/07-fdem/plot_fwd_1_em1dfm_dispersive.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_content_tutorials_07-fdem_plot_fwd_1_em1dfm_dispersive.py: 1D Forward Simulation for a Susceptible and Chargeable Earth ============================================================ Here we use the module *simpeg.electromangetics.frequency_domain_1d* to compare predicted frequency domain data for a single sounding when the Earth is purely conductive, conductive and magnetically susceptible, and when it is chargeable. In this tutorial, we focus on: - Defining receivers, sources and the survey - Defining physical properties when the Earth is chargeable and/or magnetically susceptibility - Setting physical property values as constant in the simulation Our survey geometry consists of a vertical magnetic dipole source located 30 m above the Earth's surface. The receiver is offset 10 m horizontally from the source. .. GENERATED FROM PYTHON SOURCE LINES 22-25 Import Modules -------------- .. GENERATED FROM PYTHON SOURCE LINES 25-37 .. code-block:: Python import numpy as np from matplotlib import pyplot as plt from simpeg import maps import simpeg.electromagnetics.frequency_domain as fdem from simpeg.electromagnetics.utils.em1d_utils import ColeCole plt.rcParams.update({"font.size": 16}) # sphinx_gallery_thumbnail_number = 2 .. GENERATED FROM PYTHON SOURCE LINES 38-46 Create Survey ------------- Here we demonstrate a general way to define the receivers, sources and survey. For this tutorial, the source is a vertical magnetic dipole that will be used to simulate data at a number of frequencies. The receivers measure real and imaginary ppm data. .. GENERATED FROM PYTHON SOURCE LINES 46-87 .. code-block:: Python # Frequencies being observed in Hz frequencies = np.logspace(0, 8, 41) # Define a list of receivers. The real and imaginary components are defined # as separate receivers. receiver_location = np.array([10.0, 0.0, 10.0]) receiver_orientation = "z" # "x", "y" or "z" data_type = "ppm" # "secondary", "total" or "ppm" receiver_list = [ fdem.receivers.PointMagneticFieldSecondary( receiver_location, orientation=receiver_orientation, data_type=data_type, component="both", ) ] # Define a source list. A source must defined for each frequency. source_location = np.array([0.0, 0.0, 10.0]) source_orientation = "z" # "x", "y" or "z" moment = 1.0 # dipole moment source_list = [] for freq in frequencies: source_list.append( fdem.sources.MagDipole( receiver_list=receiver_list, frequency=freq, location=source_location, orientation=source_orientation, moment=moment, ) ) # Define a 1D FDEM survey survey = fdem.survey.Survey(source_list) .. GENERATED FROM PYTHON SOURCE LINES 88-101 Defining a Layered Earth Model ------------------------------ Here, we define the layer thicknesses and physical properties for our 1D simulation. If we have N layers, parameters for the physical properties must be defined for each layer and we must provide N-1 layer thicknesses. The lowest layer is assumed to extend to infinity. For this tutorial, we predict the response for a halfspace model, however the script has been generalized to work for an arbitrary number of layers. If the Earth is a halfspace, the thicknesses could instead be defined by an empty array, and each physical property value by an array of length 1. .. GENERATED FROM PYTHON SOURCE LINES 101-150 .. code-block:: Python # Layer thicknesses thicknesses = np.array([20, 40]) n_layer = len(thicknesses) + 1 # In SimPEG, the Cole-Cole model is used to define a frequency-dependent # electrical conductivity when the Earth is chargeable. sigma = 1e-2 # infinite conductivity in S/m eta = 0.8 # intrinsice chargeability [0, 1] tau = 0.0001 # central time-relaxation constant in seconds c = 0.8 # phase constant [0, 1] # Magnetic susceptibility in SI chi = 0.2 # For each physical property, the parameters must be defined for each layer. # In this case, we must define all parameters for the Cole-Cole conductivity # as well as the magnetic susceptibility. sigma_model = sigma * np.ones(n_layer) eta_model = eta * np.ones(n_layer) tau_model = tau * np.ones(n_layer) c_model = c * np.ones(n_layer) mu0 = 4 * np.pi * 1e-7 mu_model = mu0 * (1 + chi) * np.ones(n_layer) # Here, we let the infinite conductivity be the model. As a result, we only # need to define the mapping for this parameter. All other parameters used # to define physical properties will be fixed when creating the simulation. model_mapping = maps.IdentityMap(nP=n_layer) # Plot complex conductivity at all frequencies sigma_complex = ColeCole(frequencies, sigma, eta, tau, c) fig = plt.figure(figsize=(6, 5)) ax = fig.add_axes([0.15, 0.15, 0.8, 0.75]) ax.semilogx(frequencies, sigma * np.ones(len(frequencies)), "b", lw=3) ax.semilogx(frequencies, np.real(sigma_complex), "r", lw=3) ax.semilogx(frequencies, np.imag(sigma_complex), "r--", lw=3) ax.grid() ax.set_xlim(np.min(frequencies), np.max(frequencies)) ax.set_ylim(0.0, 1.1 * sigma) ax.set_xlabel("Frequency (Hz)") ax.set_ylabel("Conductivity") ax.legend( [r"$\sigma_{\infty}$", r"$Re[\sigma (\omega)]$", r"$Im[\sigma (\omega)]$"], loc="center right", ) plt.show() .. image-sg:: /content/tutorials/07-fdem/images/sphx_glr_plot_fwd_1_em1dfm_dispersive_001.png :alt: plot fwd 1 em1dfm dispersive :srcset: /content/tutorials/07-fdem/images/sphx_glr_plot_fwd_1_em1dfm_dispersive_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 151-168 Define the Forward Simulation and Predict Data ----------------------------------------------- Here we predict the FDEM sounding for several halfspace models (conductive, susceptible, chargeable). Since the physical properties defining the Earth are different, it requires a separate simulation object be created for each case. Each simulation requires the user define the survey, the layer thicknesses and a mapping. A universal mapping was created by letting sigma be the model. All other parameters used to define the physical properties are permanently set when defining the simulation. When using the *simpeg.electromagnetics.frequency_domain_1d* module, note that predicted data are organized by source, then by receiver, then by frequency. .. GENERATED FROM PYTHON SOURCE LINES 168-196 .. code-block:: Python # Response for conductive Earth simulation = fdem.Simulation1DLayered( survey=survey, thicknesses=thicknesses, sigmaMap=model_mapping ) dpred = simulation.dpred(sigma_model) # Simulate response for a conductive and susceptible Earth simulation_susceptible = fdem.Simulation1DLayered( survey=survey, thicknesses=thicknesses, sigmaMap=model_mapping, mu=mu_model ) dpred_susceptible = simulation_susceptible.dpred(sigma_model) # Simulate response for a chargeable Earth simulation_chargeable = fdem.Simulation1DLayered( survey=survey, thicknesses=thicknesses, sigmaMap=model_mapping, eta=eta, tau=tau, c=c, ) dpred_chargeable = simulation_chargeable.dpred(sigma_model) .. GENERATED FROM PYTHON SOURCE LINES 197-200 Plotting Results ------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 200-224 .. code-block:: Python fig = plt.figure(figsize=(7, 7)) ax = fig.add_axes([0.15, 0.1, 0.8, 0.8]) ax.semilogx(frequencies, dpred[0::2], "b-", lw=3) ax.semilogx(frequencies, dpred[1::2], "b--", lw=3) ax.semilogx(frequencies, dpred_susceptible[0::2], "r-", lw=3) ax.semilogx(frequencies, dpred_susceptible[1::2], "r--", lw=3) ax.semilogx(frequencies, dpred_chargeable[0::2], "g-", lw=3) ax.semilogx(frequencies, dpred_chargeable[1::2], "g--", lw=3) ax.set_xlim([frequencies.min(), frequencies.max()]) ax.grid() ax.set_xlabel("Frequency (Hz)") ax.set_ylabel("|Hs| (A/m)") ax.set_title("Secondary Magnetic Field") ax.legend( ( "Real (conductive)", "Imaginary (conductive)", "Real (susceptible)", "Imaginary (susceptible)", "Real (chargeable)", "Imaginary (chargeable)", ) ) .. image-sg:: /content/tutorials/07-fdem/images/sphx_glr_plot_fwd_1_em1dfm_dispersive_002.png :alt: Secondary Magnetic Field :srcset: /content/tutorials/07-fdem/images/sphx_glr_plot_fwd_1_em1dfm_dispersive_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.701 seconds) **Estimated memory usage:** 9 MB .. _sphx_glr_download_content_tutorials_07-fdem_plot_fwd_1_em1dfm_dispersive.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_fwd_1_em1dfm_dispersive.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_fwd_1_em1dfm_dispersive.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_