Forward Simulation of Total Magnetic Intensity Data

Here we use the module simpeg.potential_fields.magnetics to predict magnetic data for a magnetic susceptibility model. We simulate the data on a tensor mesh. For this tutorial, we focus on the following:

• How to define the survey

• How to predict magnetic data for a susceptibility model

• How to include surface topography

• The units of the physical property model and resulting data

Import Modules

import numpy as np
from scipy.interpolate import LinearNDInterpolator
import matplotlib as mpl
import matplotlib.pyplot as plt
import os

from discretize import TensorMesh
from discretize.utils import mkvc, active_from_xyz
from simpeg.utils import plot2Ddata, model_builder
from simpeg import maps
from simpeg.potential_fields import magnetics

write_output = False

# sphinx_gallery_thumbnail_number = 2

Topography

Surface topography is defined as an (N, 3) numpy array. We create it here but topography could also be loaded from a file.

[x_topo, y_topo] = np.meshgrid(np.linspace(-200, 200, 41), np.linspace(-200, 200, 41))
z_topo = -15 * np.exp(-(x_topo**2 + y_topo**2) / 80**2)
x_topo, y_topo, z_topo = mkvc(x_topo), mkvc(y_topo), mkvc(z_topo)
xyz_topo = np.c_[x_topo, y_topo, z_topo]

Defining the Survey

Here, we define survey that will be used for the simulation. Magnetic surveys are simple to create. The user only needs an (N, 3) array to define the xyz locations of the observation locations, the list of field components which are to be modeled and the properties of the Earth’s field.

# Define the observation locations as an (N, 3) numpy array or load them.
x = np.linspace(-80.0, 80.0, 17)
y = np.linspace(-80.0, 80.0, 17)
x, y = np.meshgrid(x, y)
x, y = mkvc(x.T), mkvc(y.T)
fun_interp = LinearNDInterpolator(np.c_[x_topo, y_topo], z_topo)
z = fun_interp(np.c_[x, y]) + 10  # Flight height 10 m above surface.
receiver_locations = np.c_[x, y, z]

# Define the component(s) of the field we want to simulate as a list of strings.
# Here we simulation total magnetic intensity data.
components = ["tmi"]

# Use the observation locations and components to define the receivers. To
# simulate data, the receivers must be defined as a list.
receiver_list = magnetics.receivers.Point(receiver_locations, components=components)

receiver_list = [receiver_list]

# Define the inducing field H0 = (intensity [nT], inclination [deg], declination [deg])
inclination = 90
declination = 0
strength = 50000

source_field = magnetics.sources.UniformBackgroundField(
    receiver_list=receiver_list,
    amplitude=strength,
    inclination=inclination,
    declination=declination,
)

# Define the survey
survey = magnetics.survey.Survey(source_field)

Defining a Tensor Mesh

Here, we create the tensor mesh that will be used for the forward simulation.

dh = 5.0
hx = [(dh, 5, -1.3), (dh, 40), (dh, 5, 1.3)]
hy = [(dh, 5, -1.3), (dh, 40), (dh, 5, 1.3)]
hz = [(dh, 5, -1.3), (dh, 15)]
mesh = TensorMesh([hx, hy, hz], "CCN")

Defining a Susceptibility Model

Here, we create the model that will be used to predict magnetic data and the mapping from the model to the mesh. The model consists of a susceptible sphere in a less susceptible host.

# Define susceptibility values for each unit in SI
background_susceptibility = 0.0001
sphere_susceptibility = 0.01

# Find cells that are active in the forward modeling (cells below surface)
ind_active = active_from_xyz(mesh, xyz_topo)

# Define mapping from model to active cells
nC = int(ind_active.sum())
model_map = maps.IdentityMap(nP=nC)  # model is a vlue for each active cell

# Define model. Models in SimPEG are vector arrays
model = background_susceptibility * np.ones(ind_active.sum())
ind_sphere = model_builder.get_indices_sphere(
    np.r_[0.0, 0.0, -45.0], 15.0, mesh.cell_centers
)
ind_sphere = ind_sphere[ind_active]
model[ind_sphere] = sphere_susceptibility

# Plot Model
fig = plt.figure(figsize=(9, 4))

plotting_map = maps.InjectActiveCells(mesh, ind_active, np.nan)
ax1 = fig.add_axes([0.1, 0.12, 0.73, 0.78])
mesh.plot_slice(
    plotting_map * model,
    normal="Y",
    ax=ax1,
    ind=int(mesh.shape_cells[1] / 2),
    grid=True,
    clim=(np.min(model), np.max(model)),
)
ax1.set_title("Model slice at y = 0 m")
ax1.set_xlabel("x (m)")
ax1.set_ylabel("z (m)")

ax2 = fig.add_axes([0.85, 0.12, 0.05, 0.78])
norm = mpl.colors.Normalize(vmin=np.min(model), vmax=np.max(model))
cbar = mpl.colorbar.ColorbarBase(ax2, norm=norm, orientation="vertical")
cbar.set_label("Magnetic Susceptibility (SI)", rotation=270, labelpad=15, size=12)

plt.show()

Simulation: TMI Data for a Susceptibility Model

Here we demonstrate how to predict magnetic data for a magnetic susceptibility model using the integral formulation.

Define the forward simulation. By setting the ‘store_sensitivities’ keyword argument to “forward_only”, we simulate the data without storing the sensitivities

simulation = magnetics.simulation.Simulation3DIntegral(
    survey=survey,
    mesh=mesh,
    model_type="scalar",
    chiMap=model_map,
    active_cells=ind_active,
    store_sensitivities="forward_only",
    engine="choclo",
)

Tip

Since SimPEG v0.22.0 we can use Choclo as the engine for running the magnetic simulations, which results in faster and more memory efficient runs. Just pass engine="choclo" when constructing the simulation.

Compute predicted data for a susceptibility model

dpred = simulation.dpred(model)

# Plot
fig = plt.figure(figsize=(6, 5))
v_max = np.max(np.abs(dpred))

ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.85])
plot2Ddata(
    receiver_list[0].locations,
    dpred,
    ax=ax1,
    ncontour=30,
    clim=(-v_max, v_max),
    contourOpts={"cmap": "bwr"},
)
ax1.set_title("TMI Anomaly")
ax1.set_xlabel("x (m)")
ax1.set_ylabel("y (m)")

ax2 = fig.add_axes([0.87, 0.1, 0.03, 0.85])
norm = mpl.colors.Normalize(vmin=-np.max(np.abs(dpred)), vmax=np.max(np.abs(dpred)))
cbar = mpl.colorbar.ColorbarBase(
    ax2, norm=norm, orientation="vertical", cmap=mpl.cm.bwr
)
cbar.set_label("$nT$", rotation=270, labelpad=15, size=12)

plt.show()

Optional: Export Data

Write the data and topography

if write_output:
    dir_path = os.path.dirname(__file__).split(os.path.sep)
    dir_path.extend(["outputs"])
    dir_path = os.path.sep.join(dir_path) + os.path.sep

    if not os.path.exists(dir_path):
        os.mkdir(dir_path)

    fname = dir_path + "magnetics_topo.txt"
    np.savetxt(fname, np.c_[xyz_topo], fmt="%.4e")

    np.random.seed(211)
    maximum_anomaly = np.max(np.abs(dpred))
    noise = 0.02 * maximum_anomaly * np.random.randn(len(dpred))
    fname = dir_path + "magnetics_data.obs"
    np.savetxt(fname, np.c_[receiver_locations, dpred + noise], fmt="%.4e")

Estimated memory usage: 295 MB