1D Forward Simulation for a Single Sounding#

Here we use the module SimPEG.electromangetics.frequency_domain_1d to predict frequency domain data for a single sounding over a 1D layered Earth. In this tutorial, we focus on the following:

  • Defining receivers, sources and the survey

  • How to predict total field, secondary field or ppm data

  • The units of the model and resulting data

  • Defining and running the 1D simulation for a single sounding

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.

Import Modules#

import numpy as np
import os
from matplotlib import pyplot as plt
from discretize import TensorMesh

from SimPEG import maps
from SimPEG.electromagnetics import frequency_domain as fdem
from SimPEG.utils import plot_1d_layer_model

plt.rcParams.update({"font.size": 16})
write_output = False

# sphinx_gallery_thumbnail_number = 2

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.

# Frequencies being observed in Hz
frequencies = np.array([382, 1822, 7970, 35920, 130100], dtype=float)

# Define a list of receivers. The real and imaginary components are defined
# as separate receivers.
receiver_location = np.array([10.0, 0.0, 30.0])
receiver_orientation = "z"  # "x", "y" or "z"
data_type = "ppm"  # "secondary", "total" or "ppm"

receiver_list = []

# Define the source list. A source must be defined for each frequency.
source_location = np.array([0.0, 0.0, 30.0])
source_orientation = "z"  # "x", "y" or "z"
moment = 1.0  # dipole moment

source_list = []
for freq in frequencies:

# Define a 1D FDEM survey
survey = fdem.survey.Survey(source_list)

Defining a 1D Layered Earth Model#

Here, we define the layer thicknesses and electrical conductivities for our 1D simulation. If we have N layers, we define N electrical conductivity values and N-1 layer thicknesses. The lowest layer is assumed to extend to infinity. If the Earth is a halfspace, the thicknesses can be defined by an empty array, and the physical property values by an array of length 1.

In this case, we have a more conductive layer within a background halfspace. This can be defined as a 3 layered Earth model.

# Physical properties
background_conductivity = 1e-1
layer_conductivity = 1e0

# Layer thicknesses
thicknesses = np.array([20.0, 40.0])
n_layer = len(thicknesses) + 1

# physical property model (conductivity model)
model = background_conductivity * np.ones(n_layer)
model[1] = layer_conductivity

# Define a mapping from model parameters to conductivities
model_mapping = maps.IdentityMap(nP=n_layer)

# Plot conductivity model
thicknesses_for_plotting = np.r_[thicknesses, 40.0]
mesh_for_plotting = TensorMesh([thicknesses_for_plotting])

fig = plt.figure(figsize=(6, 5))
ax = fig.add_axes([0.15, 0.15, 0.8, 0.75])
plot_1d_layer_model(thicknesses_for_plotting, model, ax=ax, show_layers=False)
plot fwd 1 em1dfm

Define the Forward Simulation, Predict Data and Plot#

Here we define the simulation and predict the 1D FDEM sounding data. The simulation requires the user define the survey, the layer thicknesses and a mapping from the model to the conductivities of the layers.

When using the SimPEG.electromagnetics.frequency_domain_1d module, predicted data are organized by source, then by receiver, then by frequency.

# Define the simulation
simulation = fdem.Simulation1DLayered(

# Predict sounding data
dpred = simulation.dpred(model)

# Plot sounding data
fig = plt.figure(figsize=(6, 6))
ax = fig.add_axes([0.15, 0.15, 0.8, 0.75])
ax.semilogx(frequencies, np.abs(dpred[0::2]), "k-o", lw=3, ms=10)
ax.semilogx(frequencies, np.abs(dpred[1::2]), "k:o", lw=3, ms=10)
ax.set_xlabel("Frequency (Hz)")
ax.set_ylabel("|Hs/Hp| (ppm)")
ax.set_title("Secondary Magnetic Field as ppm")
ax.legend(["Real", "Imaginary"])
Secondary Magnetic Field as ppm
<matplotlib.legend.Legend object at 0x7f36b5ef9d30>

Optional: Export Data#

Write the predicted data. Note that noise has been added.

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 = 0.05 * np.abs(dpred) * np.random.randn(len(dpred))
    dpred += noise

    fname = dir_path + "em1dfm_data.txt"
        np.c_[frequencies, dpred[0 : len(frequencies)], dpred[len(frequencies) :]],
        header="FREQUENCY HZ_REAL HZ_IMAG",

Total running time of the script: (0 minutes 2.913 seconds)

Estimated memory usage: 8 MB

Gallery generated by Sphinx-Gallery