# 2. Model property mapping¶

Physical rock properties (and their units) can be a tricky thing. And emg3d is no difference in this respect. It was first developed for oil and gas, having resistive bodies in mind. You will therefore find that the documentation often talks about resistivity (Ohm.m). However, internally the computation is carried out in conductivities (S/m), so resistivities are converted into conductivities internally. For the simple forward model this is not a big issue, as the output is simply the electromagnetic field. However, moving over to optimization makes things more complicated, as the gradient of the misfit function, for instance, depends on the parametrization.

Since emg3d v0.12.0 it is therefore possible to define a emg3d.models.Model in different ways, thanks to different maps, defined with the parameter mapping. Currently implemented are six different maps:

• 'Resistivity': $$\rho$$ (Ohm.m), the default;

• 'LgResistivity': $$\log_{10}(\rho)$$;

• 'LnResistivity': $$\log_e(\rho)$$;

• 'Conductivity': $$\sigma$$ (S/m);

• 'LgConductivity': $$\log_{10}(\sigma)$$;

• 'LnConductivity': $$\log_e(\sigma)$$.

We take here the model from 5. Total vs primary/secondary field and map it once as 'LgResistivity' and once as 'LgConductivity', and verify that the resulting electric field is the same.

import emg3d
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('bmh')


## Survey¶

src = [0, 0, -1950, 0, 0]   # x-dir. source at the origin, 50 m above seafloor
off = np.arange(5, 81)*100  # Offsets
rec = [off, off*0, -2000]   # In-line receivers on the seafloor
res = [0.3, 1]              # 1D resistivities (Ohm.m): [water, background]
freq = 1.0                  # Frequency (Hz)


## Mesh¶

We create quite a coarse grid (100 m minimum cell width), to have reasonable fast computation times.

grid = emg3d.construct_mesh(
frequency=freq,
min_width_limits=100.0,
properties=[res, res, res, res],
center=(src, src, -2000),
domain=([-100, 8100], [-500, 500], [-3500, -1500]),
verb=0,
)
grid

 MESH EXTENT CELL WIDTH FACTOR dir nC TensorMesh 73,728 cells x 96 -3,303.39 11,303.39 100.00 996.95 1.39 y 24 -3,703.39 3,703.39 100.00 996.95 1.39 z 32 -7,512.73 240.89 100.00 1,344.36 1.45

## Define resistivities¶

# Layered_background
res_x = np.ones(grid.n_cells)*res            # Water resistivity
res_x[grid.cell_centers[:, 2] < -2000] = res  # Background resistivity

# Include the target
xx = (grid.cell_centers[:, 0] >= 0) & (grid.cell_centers[:, 0] <= 6000)
yy = abs(grid.cell_centers[:, 1]) <= 500
zz = (grid.cell_centers[:, 2] > -3500)*(grid.cell_centers[:, 2] < -3000)

res_x[xx*yy*zz] = 100.  # Target resistivity


## Create LgResistivity and LgConductivity models¶

# Create log10-res model
model_lg_res = emg3d.Model(
grid, property_x=np.log10(res_x), mapping='LgResistivity')

# Create log10-con model
model_lg_con = emg3d.Model(
grid, property_x=np.log10(1/res_x), mapping='LgConductivity')

# Plot the models
fig, axs = plt.subplots(figsize=(9, 6), nrows=1, ncols=2)

# log10-res
f0 = grid.plot_slice(model_lg_res.property_x, v_type='CC',
normal='Y', ax=axs, clim=[-2, 2])
axs.set_title(r'Resistivity (Ohm.m); $\log_{10}$-scale')
axs.set_xlim([-1000, 8000])
axs.set_ylim([-4000, -1500])

# log10-con
f1 = grid.plot_slice(model_lg_con.property_x, v_type='CC',
normal='Y', ax=axs, clim=[-2, 2])
axs.set_title(r'Conductivity (S/m); $\log_{10}$-scale')
axs.set_xlim([-1000, 8000])
axs.set_ylim([-4000, -1500])

plt.tight_layout()
fig.colorbar(f0, ax=axs, orientation='horizontal', fraction=0.05)
plt.show() ## Compute electric fields¶

source = emg3d.TxElectricDipole(coordinates=src)
solver_opts = {
'source': source,
'frequency': freq,
'verb': 1,
}

efield_lg_res = emg3d.solve_source(model_lg_res, **solver_opts)
efield_lg_con = emg3d.solve_source(model_lg_con, **solver_opts)

# Extract responses at receiver locations.


Out:

:: emg3d :: 8.9e-07; 1(4); 0:00:04; CONVERGED
:: emg3d :: 8.9e-07; 1(4); 0:00:04; CONVERGED


## Compare the two results¶

plt.figure()
plt.title('Comparison')

# Log_10(resistivity)-model.
plt.plot(off/1e3, rec_lg_res.real, 'C0',
label=r'$\Re[\log_{10}(\rho)]$-model')
plt.plot(off/1e3, rec_lg_res.imag, 'C1-',
label=r'$\Im[\log_{10}(\rho)]$-model')

# Log_10(conductivity)-model.
plt.plot(off/1e3, rec_lg_con.real, 'C2-.',
label=r'$\Re[\log_{10}(\sigma)]$-model')
plt.plot(off/1e3, rec_lg_con.imag, 'C3-.',
label=r'$\Im[\log_{10}(\sigma)]$-model')

plt.xlabel('Offset (km)')
plt.ylabel('$E_x$ (V/m)')
plt.yscale('symlog', linthresh=1e-17)
plt.legend()

plt.tight_layout()
plt.show() emg3d.Report()

 Sat Jul 17 17:50:25 2021 CEST OS Linux CPU(s) 4 Machine x86_64 Architecture 64bit RAM 15.5 GiB Environment Python Python 3.9.4 | packaged by conda-forge | (default, May 10 2021, 22:13:33) [GCC 9.3.0] numpy 1.21.0 scipy 1.7.0 numba 0.53.1 emg3d 1.1.0 empymod 2.1.2 xarray 0.18.2 discretize 0.7.0 h5py 3.3.0 matplotlib 3.4.2 tqdm 4.61.2 Intel(R) oneAPI Math Kernel Library Version 2021.2-Product Build 20210312 for Intel(R) 64 architecture applications

Total running time of the script: ( 0 minutes 9.940 seconds)

Estimated memory usage: 41 MB

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