Simulate a 1D Sounding over a Layered Earth#

Here we use the module SimPEG.electromangetics.static.resistivity to predict sounding data over a 1D layered Earth. In this tutorial, we focus on the following:

  • General definition of sources and receivers

  • How to define the survey

  • How to predict voltage or apparent resistivity data

  • The units of the model and resulting data

For this tutorial, we will simulate sounding data over a layered Earth using a Wenner array. The end product is a sounding curve which tells us how the electrical resistivity changes with depth.

Import modules#

import os
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

from SimPEG import maps
from SimPEG.electromagnetics.static import resistivity as dc
from SimPEG.utils import plot_1d_layer_model

mpl.rcParams.update({"font.size": 16})

write_output = False

# sphinx_gallery_thumbnail_number = 2

Create Survey#

Here we demonstrate a general way to define sources and receivers. For pole and dipole sources, we must define the A or AB electrode locations, respectively. For the pole and dipole receivers, we must define the M or MN electrode locations, respectively.

a_min = 20.0
a_max = 500.0
n_stations = 25

# Define the 'a' spacing for Wenner array measurements for each reading
electrode_separations = np.linspace(a_min, a_max, n_stations)

source_list = []  # create empty array for sources to live

for ii in range(0, len(electrode_separations)):
    # Extract separation parameter for sources and receivers
    a = electrode_separations[ii]

    # AB electrode locations for source. Each is a (1, 3) numpy array
    A_location = np.r_[-1.5 * a, 0.0, 0.0]
    B_location = np.r_[1.5 * a, 0.0, 0.0]

    # MN electrode locations for receivers. Each is an (N, 3) numpy array
    M_location = np.r_[-0.5 * a, 0.0, 0.0]
    N_location = np.r_[0.5 * a, 0.0, 0.0]

    # Create receivers list. Define as pole or dipole.
    receiver_list = dc.receivers.Dipole(
        M_location, N_location, data_type="apparent_resistivity"
    receiver_list = [receiver_list]

    # Define the source properties and associated receivers
    source_list.append(dc.sources.Dipole(receiver_list, A_location, B_location))

# Define survey
survey = dc.Survey(source_list)

Defining a 1D Layered Earth Model#

Here, we define the layer thicknesses and electrical resistivities for our 1D simulation. If we have N layers, we define N electrical resistivity values and N-1 layer thicknesses. The lowest layer is assumed to extend to infinity. In the case of a halfspace, the layer thicknesses would be an empty array.

# Define layer thicknesses.
layer_thicknesses = np.r_[100.0, 100.0]

# Define layer resistivities.
model = np.r_[1e3, 4e3, 2e2]

# Define mapping from model to 1D layers.
model_map = maps.IdentityMap(nP=len(model))

Plot Resistivity Model#

Here we plot the 1D resistivity model.

# Plot the 1D model
ax = plot_1d_layer_model(layer_thicknesses, model_map * model)
ax.set_xlabel(r"Resistivity ($\Omega m$)")
plot fwd 1 dcr sounding
Text(0.5, 49.52222222222221, 'Resistivity ($\\Omega m$)')

Define the Forward Simulation and Predict DC Resistivity Data#

Here we predict DC resistivity data. If the keyword argument rhoMap is defined, the simulation will expect a resistivity model. If the keyword argument sigmaMap is defined, the simulation will expect a conductivity model.

simulation = dc.simulation_1d.Simulation1DLayers(

# Predict data for a given model
dpred = simulation.dpred(model)

# Plot apparent resistivities on sounding curve
fig = plt.figure(figsize=(11, 5))
ax1 = fig.add_axes([0.1, 0.1, 0.75, 0.85])
ax1.semilogy(1.5 * electrode_separations, dpred, "b")
ax1.set_xlabel("AB/2 (m)")
ax1.set_ylabel(r"Apparent Resistivity ($\Omega m$)")
plot fwd 1 dcr sounding

Optional: Export Data#

Export data and true model

if write_output:
    dir_path = os.path.dirname(__file__).split(os.path.sep)
    dir_path = os.path.sep.join(dir_path) + os.path.sep

    if not os.path.exists(dir_path):

    noise = 0.025 * dpred * np.random.randn(len(dpred))

    data_array = np.c_[
        dpred + noise,

    fname = dir_path + "app_res_1d_data.dobs"
    np.savetxt(fname, data_array, fmt="%.4e")

Total running time of the script: (0 minutes 3.618 seconds)

Estimated memory usage: 8 MB

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