 Here is a simplified example of how you might implement this using the SimPEG library. Note that this is a basic example and may need to be adjusted to fit your specific needs.

```python
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from simpeg import Mesh, Grid3D, FWI, Data, Model, Mapping, LinearOperator, InverseProblem

# Define the ground truth model
r1, theta1, phi1 = np.meshgrid(np.linspace(0, 1, 100), np.linspace(0, np.pi, 100), np.linspace(0, 2 * np.pi, 100))
r2, theta2, phi2 = r1 + 0.2, theta1, phi1
bg_conductivity = 1.0
conductive_sphere = np.where((r1 < 0.5) & (r2 > 0.5), 10.0, bg_conductivity)
resistive_sphere = np.where((r1 < 0.5) & (r2 > 0.5), bg_conductivity, 0.1)
ground_truth = bg_conductivity * np.ones_like(conductive_sphere)
ground_truth[conductive_sphere > 0] = conductive_sphere[conductive_sphere > 0]
ground_truth[resistive_sphere > 0] = resistive_sphere[resistive_sphere > 0]

# Create the mesh
mesh = Mesh.regular_3d(nx=100, ny=100, nz=100)

# Create the grid and data
grid = Grid3D(mesh, spacing=0.01)
data = Data(grid, 'dd', np.random.normal(0, 1, (100, 100, 100, 100, 100)))

# Define the active cells mapping
active_cells = np.where((mesh.x < 0.5) & (mesh.y < np.pi) & (mesh.z < 2 * np.pi))
active_cells_mapping = Mapping.from_cell_list(mesh, active_cells)

# Define the exponential mapping for log conductivity space
exp_mapping = Mapping.exponential(active_cells_mapping)

# Define the forward model
forward_model = FWI.dipole_dipole(grid, data.dd, model_mapping=exp_mapping)

# Define the Tikhonov regularization
L = LinearOperator(grid, grid, lambda x: x)
reg = 1e-3

# Define the inversion problem
iprob = InverseProblem(forward_model, data.dd, model0=ground_truth, jac_op=L, reg_op=L, reg_val=reg)

# Perform the inversion
sol = iprob.solve(max_iter=1000, show_progress=True)

# Invert back to conductivity space
inverted_model = np.power(exp_mapping.inverse(sol.x), -1)

# Plot the ground truth and inverted model
fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(r1, theta1, phi1, ground_truth[ground_truth > 0], cmap='viridis', alpha=0.5)
ax.plot_surface(r1, theta1, phi1, inverted_model[inverted_model > 0], cmap='viridis', alpha=0.5)
ax.set_xlabel('Radius (m)')
ax.set_ylabel('Theta (rad)')
ax.set_zlabel('Phi (rad)')
plt.show()

# Horizontal cross-section plots
plt.figure(figsize=(10, 5))
plt.plot(r1[50, :, :], theta1[50, :, :], phi1[50, :, :], ground_truth[50, :, :], label='Ground Truth')
plt.plot(r1[50, :, :], theta1[50, :, :], phi1[50, :, :], inverted_model[50, :, :], label='Inverted Model')
plt.legend()
plt.show()
```

This code creates a 3D model with two spheres, one conductive and one resistive, generates a synthetic Dipole-Dipole Survey, performs a Tikhonov Inversion, and plots the ground truth and inverted model both vertically and horizontally. Please note that you may need to adjust the parameters and dimensions according to your specific problem. Also, make sure to install the SimPEG library before running the code. You can install it using `pip install simpeg`.