3 Inversions DC


In this notebook, we use a synthetic example to explore aspects of inversion including:

  • assigning uncertainties to the the data
  • adjusting the regularization parameters on the smallness and smoothness terms
  • adjusting the value of the trade-off parameter β\beta

Step 0: Imports and load survey info

These steps are the same as in the previous notebook.

# core python 
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm, SymLogNorm, Normalize
import matplotlib.gridspec as gridspec
import ipywidgets

# tools in the simPEG Ecosystem 
import discretize  # for creating computational meshes

# linear solvers
    from pymatsolver import Pardiso as Solver  # this is a fast linear solver 
except ImportError:
    from SimPEG import SolverLU as Solver  # this will be slower

# SimPEG inversion machinery
from SimPEG import (
    Data, maps,
    data_misfit, regularization, optimization, inverse_problem, 
    inversion, directives

# DC resistivity and IP modules
from SimPEG.electromagnetics import resistivity as dc
# set the font size in the plots
from matplotlib import rcParams
rcParams["font.size"] = 14
    import warnings

Step 1: Load DC Survey

This is the same as in the previous notebook, but included so we can re-visit any of the steps as necessary.

line = "46800E"

dc_data_file = f"./century/{line}/{line[:-1]}POT.OBS"
def read_dcip_data(filename, verbose=True):
    Read in a .OBS file from the Century data set into a python dictionary. 
    The format is the old UBC-GIF DCIP format.
    filename : str
        Path to the file to be parsed
    verbose: bool
        Print some things? 
        A dictionary with the locations of
        - a_locations: the positive source electrode locations (numpy array) 
        - b_locations: the negative source electrode locations (numpy array) 
        - m_locations: the receiver locations (list of numpy arrays)
        - n_locations: the receiver locations (list of numpy arrays)
        - n_locations: the receiver locations (list of numpy arrays)
        - observed_data: observed data (list of numpy arrays)
        - standard_deviations: assigned standard deviations (list of numpy arrays)
        - n_sources: number of sources (int)
    # read in the text file as a numpy array of strings (each row is an entry)
    contents = np.genfromtxt(filename, delimiter=' \n', dtype=np.str)
    # the second line has the number of sources, current, and data type (voltages if 1)
    n_sources = int(contents[1].split()[0])
    if verbose is True: 
        print(f"number of sources: {n_sources}")
    # initialize storage for the electrode locations and data
    a_locations = np.zeros(n_sources)
    b_locations = np.zeros(n_sources)
    m_locations = []
    n_locations = []
    observed_data = []
    standard_deviations = []
    # index to track where we have read in content 
    content_index = 1 
    # loop over sources 
    for i in range(n_sources):
        # start by reading in the source info 
        content_index = content_index + 1  # read the next line
        a_location, b_location, nrx = contents[content_index].split()  # this is a string

        # convert the strings to a float for locations and an int for the number of receivers
        a_locations[i] = float(a_location)
        b_locations[i] = float(b_location)
        nrx = int(nrx)

        if verbose is True: 
            print(f"Source {i}: A-loc: {a_location}, B-loc: {b_location}, N receivers: {nrx}")

        # initialize space for receiver locations, observed data associated with this source
        m_locations_i, n_locations_i = np.zeros(nrx), np.zeros(nrx)
        observed_data_i, standard_deviations_i = np.zeros(nrx), np.zeros(nrx)

        # read in the receiver info 
        for j in range(nrx):
            content_index = content_index + 1  # read the next line
            m_location, n_location, datum, std = contents[content_index].split()

            # convert the locations and data to floats, and store them
            m_locations_i[j] = float(m_location)
            n_locations_i[j] = float(n_location)
            observed_data_i[j] = float(datum)
            standard_deviations_i[j] = float(std)

        # append the receiver info to the lists
    return {
        "a_locations": a_locations,
        "b_locations": b_locations, 
        "m_locations": m_locations,
        "n_locations": n_locations,
        "observed_data": observed_data, 
        "standard_deviations": standard_deviations,
        "n_sources": n_sources, 
dc_data_dict = read_dcip_data(dc_data_file, verbose=False)
# initialize an empty list for each 
source_list = []

# center the survey and work in local coordinates
x_local = 0.5*(np.min(dc_data_dict["a_locations"]) + np.max(np.hstack(dc_data_dict["n_locations"])))

for i in range(dc_data_dict["n_sources"]):
    # receiver electrode locations in 2D 
    m_locs = np.vstack([
        dc_data_dict["m_locations"][i] - x_local, 
    n_locs = np.vstack([
        dc_data_dict["n_locations"][i] - x_local,
    # construct the receiver object 
    receivers = dc.receivers.Dipole(locations_m=m_locs, locations_n=n_locs, storeProjections=False)
    # construct the source 
    source = dc.sources.Dipole(
        location_a=np.r_[dc_data_dict["a_locations"][i] - x_local, 0.],
        location_b=np.r_[dc_data_dict["b_locations"][i] - x_local, 0.],
    # append the new source to the source list
survey = dc.Survey(source_list=source_list)

Step 2: Build a Mesh

Similar to the previous notebook, we use a simple function to design our mesh.

def build_mesh(
    A function for designing a Tensor Mesh based on DC survey parameters
    survey: dc.Survey 
        A DC (or IP) survey object
    n_cells_per_spacing_[x, z]: int
        Number of [x, z]-cells per the minimum electrode spacing
    n_core_extra_[x, z]: int
        Number of extra cells with the same size as the core domain beyond the survey extent
    core_domain_z_ratio: float
        Factor that multiplies the maximum AB, MN separation to define the core mesh extent
    padding_factor: float
        Factor by which we expand the mesh cells in the padding region
    n_pad_[x, z]: int
        Number of padding cells in the x, z directions
    min_electrode_spacing = np.min(np.abs(survey.locations_a[:, 0] - survey.locations_b[:, 0]))

    dx = min_electrode_spacing / n_cells_per_spacing_x
    dz = min_electrode_spacing / n_cells_per_spacing_z
    # define the x core domain
    core_domain_x = np.r_[
        survey.electrode_locations[:, 0].min(),
        survey.electrode_locations[:, 0].max()
    # find the y core domain
    # find the maximum spacing between source, receiver midpoints
    mid_ab = (survey.locations_a + survey.locations_b)/2
    mid_mn = (survey.locations_m + survey.locations_n)/2
    separation_ab_mn = np.abs(mid_ab - mid_mn)
    max_separation = separation_ab_mn.max()
    core_domain_z = np.r_[-core_domain_z_ratio * max_separation, 0.]
    # add extra cells beyond the core domain
    n_core_x = np.ceil(np.diff(core_domain_x)/dx) + n_core_extra_x*2  # on each side
    n_core_z = np.ceil(np.diff(core_domain_z)/dz) + n_core_extra_z  # just below
    # define the tensors in each dimension
    hx = [(dx, n_pad_x, -padding_factor), (dx, n_core_x), (dx, n_pad_x, padding_factor)]
    hz = [(dz, n_pad_z, -padding_factor), (dz, n_core_z)]

    mesh = discretize.TensorMesh([hx, hz], x0="CN")

    return mesh, core_domain_x, core_domain_z
mesh, core_domain_x, core_domain_z = build_mesh(survey)
<AxesSubplot:xlabel='x1', ylabel='x2'>
<Figure size 432x288 with 1 Axes>

Step 3: Design our True Model

Here, we will focus in on a synthetic example so we know the true solution. Again, we will use a model containing 2 blocks in a halfspace.

3.1 Define the model geometry and physical properties

Here, we define a model with one conductive block and one resistive block in a halfspace.

# define the resistivities
rho_background = 100
rho_resistive_block = 1000
rho_conductive_block = 10

# define the geometry of each block
xlim_resistive_block = np.r_[-500, -250]
zlim_resistive_block = np.r_[-150, -75]

xlim_conductive_block = np.r_[250, 500]
zlim_conductive_block = np.r_[-150, -75]
rho = rho_background * np.ones(mesh.nC)

# resistive block
inds_resistive_block = (
    (mesh.gridCC[:, 0] >= xlim_resistive_block.min()) & (mesh.gridCC[:, 0] <= xlim_resistive_block.max()) &
    (mesh.gridCC[:, 1] >= zlim_resistive_block.min()) & (mesh.gridCC[:, 1] <= zlim_resistive_block.max())

rho[inds_resistive_block] = rho_resistive_block

# conductive block
inds_conductive_block = (
    (mesh.gridCC[:, 0] >= xlim_conductive_block.min()) & (mesh.gridCC[:, 0] <= xlim_conductive_block.max()) &
    (mesh.gridCC[:, 1] >= zlim_conductive_block.min()) & (mesh.gridCC[:, 1] <= zlim_conductive_block.max())

rho[inds_conductive_block] = rho_conductive_block
fig, ax = plt.subplots(1, 1, figsize=(12, 4))
out = mesh.plotImage(np.log10(rho), ax=ax, pcolorOpts={"cmap":"Spectral"})
plt.colorbar(out[0], ax=ax, label="log$_{10} \\rho$", orientation="horizontal", fraction=0.05, pad=0.25)
ax.set_ylim(core_domain_z + np.r_[-100, 0])
<Figure size 864x288 with 2 Axes>

3.2 Define the true model

Since we will be working with log-resistivities in the inversion, our true model is defined as the log of the resistivity.

model_true = np.log(rho)

Step 4: Set up and run a forward simulation

These will be the "observed data" in the inversion. We add noise to the result (a default of 5%). If you would like to adjust the noise level, you can change the relative_error, you can pass add_noise to the make_synthetic_data function.

As in the first notebook, we will invert for log-resistivity, so we use an ExpMap in the forward simulation. Again, we use the storeJ option to store the

mapping = maps.ExpMap(mesh)

# Generate 2.5D DC problem
simulation_dc = dc.Simulation2DNodal(
    mesh, rhoMap=mapping, solver=Solver, survey=survey, storeJ=True

# compute our "observed data"
synthetic_data = simulation_dc.make_synthetic_data(model_true, add_noise=True)
CPU times: user 1.01 s, sys: 77.2 ms, total: 1.09 s
Wall time: 373 ms
# plot pseudosections 
fig, ax = plt.subplots(1, 1, figsize=(12, 4))
# plot a psuedosection of the data
    synthetic_data, data_type="apparent resistivity", 
    plot_type="contourf", data_location=True, ax=ax, 
ax.set_aspect(1.5)  # some vertical exxageration
ax.set_xlabel("Northing (m)")

<Figure size 864x288 with 2 Axes>

Recall: inversion as optimization

We forumlate the inverse problem as an optimization problem consisting of a data misfit and a regularization

minmΦ(m)=Φd(m)+βΦm(m)s.t. ΦdΦdmiLmmiU\min_{\mathbf{m}} \Phi(\mathbf{m}) = \Phi_d(\mathbf{m}) + \beta\Phi_m(\mathbf{m}) \\ s.t. ~ \Phi_d \leq \Phi_d^* \quad \mathbf{m}_i^{\rm L} \leq \mathbf{m}_ \leq \mathbf{m}_i^{\rm U}


  • m\mathbf{m} is our inversion model - a vector containing the set of parameters that we invert for

  • Φd\Phi_d is the data misfit

    Φd(m)=12Wd(F(m)dobs)2 \Phi_d(\mathbf{m}) = \frac{1}{2}\|\mathbf{W_d} (\mathcal{F}(\mathbf{m}) - \mathbf{d}^{\text{obs}})\|^2

  • Φm\Phi_m is the regularization

    Φm(m)=12(αsWs(mmref)2+αxWx(m)2+αzWz(m)2) \Phi_m(\mathbf{m}) = \frac{1}{2}\big(\alpha_s\|\mathbf{W_s} (\mathbf{m} - \mathbf{m}_{\text{ref}})\|^2 + \alpha_x\|\mathbf{W_x} (\mathbf{m})\|^2 + \alpha_z\|\mathbf{W_z} (\mathbf{m})\|^2 \big)

  • β\beta is a trade-off parameter that weights the relative importance of the data misfit and regularization terms

  • Φd\Phi_d^* is our target misfit, which is typically set to N/2N/2 where NN is the number of data (Parker, 1994) (or also see Oldenburg & Li (2005))

Step 5: assign standard deviations to the data

The standard deviations are an estimate of the level of noise on your data. These are used to construct the weights in the Wd\mathbf{W_d} matrix of the data misfit.

It is common to define the standard deviation in terms of a relative_error and a noise_floor.

KaTeX parse error: Expected 'EOF', got '_' at position 15: \text{standard_̲deviation} =  \…

\text{standard_deviation} =  \text{relative_error}\times|d^{obs}| + \text{noise_floor}

For DC resistivity, it is common to choose a relative_error between 0.02-0.1 (2% - 10% error). The noise_floor value defines threshold for data below which we consider those values to be close to zero. It is important to set a non-zero noise_floor when we can have zero-crossings in our data (e.g. both positive and negative values - which we do in DC!). The noise_floor ensures that we don't try to fit near-zero values to very high accuracy.

fig, ax = plt.subplots(1, 1)
ax.hist(np.log10(np.abs(synthetic_data.dobs)), 30)
Text(0.5, 0, '$log_{10}(|d^{obs}|)$')
<Figure size 432x288 with 1 Axes>
relative_error = 0.05  # 5% error
noise_floor = 1e-4
fig, ax = plt.subplots(1, 1)
ax.hist(np.log10(np.abs(synthetic_data.dobs)), 30)
ax.axvline(np.log10(noise_floor), linestyle="dashed", color="C1")
<matplotlib.lines.Line2D at 0x12bbe1750>
<Figure size 432x288 with 1 Axes>

5.1 Assign uncertainties to our data object

In SimPEG, the data object is responsible for keeping track of the survey geometry, observed data and uncertainty values. The standard_deviation property is what is used to construct the Wd\mathbf{W_d} matrix in the data misfit

W_d = diag(1 / data.standard_deviation) 
synthetic_data.relative_error = relative_error
synthetic_data.noise_floor = noise_floor
    relative_error * np.abs(synthetic_data.dobs) + noise_floor, 

Plot the data sorted by amplitude and associated uncertainties.

inds_sort = np.argsort(np.abs(synthetic_data.dobs))
sorted_data = np.abs(synthetic_data.dobs[inds_sort])
sorted_std = synthetic_data.standard_deviation[inds_sort]

fig, ax = plt.subplots(2, 1, figsize=(12, 8))

x = np.arange(0, len(sorted_data))

ax[0].plot(x, sorted_data, '.k')
ax[1].semilogy(x, sorted_data, '.k')

ax[0].set_title("sorted data and associated uncertainties")

for a in ax:
    a.fill_between(x, sorted_data - sorted_std, sorted_data + sorted_std, alpha=0.25)
    a.set_ylabel("observed data (V)")

<Figure size 864x576 with 2 Axes>

Step 6: Assembling the inversion

Here, we are going to set up functions for constructing and running the inversion so that we can easily adjust the parameters used in the inversion.

We start be defining a function that will construct our inverse_problem object which requires the data_misfit, regularization, and optimization be constructed.

def create_inverse_problem(
    relative_error=relative_error, noise_floor=noise_floor,
    alpha_s=1e-3,  alpha_x=1, alpha_z=1, mref=np.log(rho_background), 
    maxIter=20, maxIterCG=20,
    A function that contructs a data misfit, regularization, and optimization and 
    assembles them into an inverse_problem object. 
    relative_error: float
        relative error we assign to the data (used to construct the standard_deviation)
    noise_floor: float
        noise floor that we use to construct the standard deviations of the data
    alpha_s: float
        weight for the smallness component of the regularization
    alpha_[x, z]: float
        weight for the [x, z]-smoothness term in the regularization
    mref: float
        reference model value used in the smallness term of the regularization
    maxIter: int
        maximum number of iterations in the inversion
    maxIterCG: int
        maximum number of Conjugate Gradient iterations used to compute a step 
        in the Inexact Gauss Newton optimization
    # set the uncertainties and define the data misfit
    synthetic_data.relative_error = relative_error
    synthetic_data.noise_floor = noise_floor
    dmisfit = data_misfit.L2DataMisfit(data=synthetic_data, simulation=simulation_dc)
    # regularization
    reg = regularization.Tikhonov(
        mesh, alpha_s=alpha_s, alpha_x=alpha_x, alpha_y=alpha_z, mref=mref*np.ones(mesh.nC)
    # optimization 
    opt = optimization.InexactGaussNewton(maxIter=maxIter, maxIterCG=maxIterCG)
    # return the inverse problem 
    return inverse_problem.BaseInvProblem(dmisfit, reg, opt)

Next, we define a simple function for assembling the inversion. There are very simiar functions in SimPEG already that write these outputs to a file (see for example directives.SaveOutputEveryIteration). In part, I show this here to give you a sense of how directives work in the inversion so if you are interested in writing your own, then you can use this as a template.

The initialize method is called at the very beginning of the inversion. In the BetaEstimate_ByEig, which we used in notebook 1, this is where we initialze an estimate of beta.

The endIter method is called at the end of every iteration. For example in the BetaSchedule directive, this is where we update the value of beta.

In this directive, we will create a simple dictionary that saves some outputs of interest during the inversion.

class SaveInversionProgress(directives.InversionDirective):
    A custom directive to save items of interest during the course of an inversion
    def initialize(self):
        This is called when we first start running an inversion
        # initialize an empty dictionary for storing results 
        self.inversion_results = {

    def endIter(self):
        This is run at the end of every iteration. So here, we just append 
        the new values to our dictionary
        # Save the data
        # grab the components of the regularization and evaluate them here
        # the regularization has a list of objective functions  
        # objfcts = [smallness, smoothness_x, smoothness_z]
        # and the multipliers contain the alpha values
        # multipliers = [alpha_s, alpha_x, alpha_z]
        reg = self.reg.objfcts[0] 
        phi_s = reg.objfcts[0](self.invProb.model) * reg.multipliers[0]
        phi_x = reg.objfcts[1](self.invProb.model) * reg.multipliers[1]
        phi_z = reg.objfcts[2](self.invProb.model) * reg.multipliers[2]

There are three directives that we use in this inversion

  • BetaEstimate_ByEig: sets an initial value for β\beta by estimating the largest eigenvalue of Φd\Phi_d and of Φm\Phi_m and then taking their ratio. The value is then scaled by the beta0_ratio value, so for example if you wanted the regularization to be ~10 times more important than the data misfit, then we would set beta0_ratio=10

  • BetaSchedule: this reduces the value of beta during the course of the inversion. Particularly for non-linear problems, it can be advantageous to start off by seeking a smooth model which fits the regularization and gradually decreasing the infulence of the regularization to increase the influence of the data.

  • TargetMisfit: this directive checks at the end of each iteration if we have reached the target misfit. If we have, the inversion terminates, it not, it continues. Optionally, a chifact can be set to stop the inversion when phi_d <= chifact * phi_d_star. By default (chifact=1)

def create_inversion(
    inv_prob, beta0_ratio=1e2, cool_beta=True,
    beta_cooling_factor=2, beta_cooling_rate=1,
    use_target=True, chi_factor=1, 
    A method for creating a SimPEG inversion with directives for estimating and 
    updating beta as well as choosing a stopping criteria
    inv_prob: inverse_problem
        An inverse problem object that describes the data misfit, regularization and optimization
    beta0_ratio: float
        a scalar that multiplies ratio of the estimated largest eigenvalue of phi_d and phi_m
    cool_beta: bool
        True: use a beta cooling schedule. False: use a fixed beta
    beta_cooling_factor: float
        reduce beta by a factor of 1/beta_cooling factor 
    beta_cooling_rate: int
        number of iterations we keep beta fixed for 
        (we cool beta by the cooling factor every `beta_cooling_rate` iterations) 
    chi_factor: float
        Stop the inversion when phi_d <= chifact * phi_d_star. By default (`chifact=1`)
    # set up our directives
    beta_est = directives.BetaEstimate_ByEig(beta0_ratio=beta0_ratio)
    target = directives.TargetMisfit(chifact=chi_factor)
    save = SaveInversionProgress()
    directives_list = [beta_est, save]
    if use_target is True:
    if cool_beta is True:
        beta_schedule = directives.BetaSchedule(coolingFactor=beta_cooling_factor, coolingRate=beta_cooling_rate)

    return inversion.BaseInversion(inv_prob, directiveList=directives_list), target, save

6.1 Create and run the inversion

rho0 = rho_background
inv_prob = create_inverse_problem(
    relative_error=0.05, noise_floor=1e-4,
    alpha_s=1e-3,  alpha_x=1, alpha_z=1, mref=np.log(rho0), 
    maxIter=20, maxIterCG=20,

inv, target_misfit, inversion_log = create_inversion(
    inv_prob, beta0_ratio=1e2, cool_beta=True,
    beta_cooling_factor=2, beta_cooling_rate=1,
    use_target=False, chi_factor=1 

phi_d_star = survey.nD / 2
target = target_misfit.chifact * phi_d_star
m0 = np.log(rho0) * np.ones(mesh.nC)
model_recovered = inv.run(m0)
inversion_results = inversion_log.inversion_results

        SimPEG.InvProblem is setting bfgsH0 to the inverse of the eval2Deriv.
        ***Done using same Solver and solverOpts as the problem***
model has any nan: 0
============================ Inexact Gauss Newton ============================
  #     beta     phi_d     phi_m       f      |proj(x-g)-x|  LS    Comment   
x0 has any nan: 0
   0  1.30e+02  1.31e+03  0.00e+00  1.31e+03    4.13e+02      0              
   1  6.48e+01  6.85e+02  1.79e+00  8.01e+02    1.06e+02      0              
   2  3.24e+01  5.05e+02  3.81e+00  6.29e+02    7.83e+01      0   Skip BFGS  
   3  1.62e+01  3.48e+02  7.32e+00  4.66e+02    5.55e+01      0   Skip BFGS  
   4  8.10e+00  2.22e+02  1.28e+01  3.26e+02    3.78e+01      0   Skip BFGS  
   5  4.05e+00  1.29e+02  2.09e+01  2.14e+02    2.50e+01      0   Skip BFGS  
   6  2.03e+00  6.82e+01  3.11e+01  1.31e+02    1.58e+01      0   Skip BFGS  
   7  1.01e+00  3.60e+01  4.19e+01  7.84e+01    9.30e+00      0   Skip BFGS  
   8  5.07e-01  2.18e+01  5.14e+01  4.79e+01    5.14e+00      0   Skip BFGS  
   9  2.53e-01  1.52e+01  6.05e+01  3.06e+01    3.01e+00      0   Skip BFGS  
  10  1.27e-01  1.19e+01  7.00e+01  2.07e+01    1.84e+00      0   Skip BFGS  
  11  6.33e-02  9.87e+00  8.12e+01  1.50e+01    1.42e+00      0   Skip BFGS  
  12  3.17e-02  8.63e+00  9.49e+01  1.16e+01    1.49e+00      0   Skip BFGS  
  13  1.58e-02  7.85e+00  1.12e+02  9.62e+00    2.11e+00      0   Skip BFGS  
  14  7.91e-03  7.39e+00  1.35e+02  8.46e+00    5.76e+00      0   Skip BFGS  
  15  3.96e-03  7.21e+00  1.35e+02  7.74e+00    5.27e-01      0              
  16  1.98e-03  6.96e+00  1.63e+02  7.28e+00    4.59e+00      0              
  17  9.89e-04  6.75e+00  1.66e+02  6.91e+00    7.08e-01      0              
  18  4.95e-04  6.55e+00  1.91e+02  6.64e+00    2.42e+00      2   Skip BFGS  
  19  2.47e-04  6.43e+00  2.34e+02  6.49e+00    5.26e+00      1   Skip BFGS  
  20  1.24e-04  6.18e+00  2.61e+02  6.21e+00    2.77e+00      0              
------------------------- STOP! -------------------------
1 : |fc-fOld| = 2.8185e-01 <= tolF*(1+|f0|) = 1.3140e+02
1 : |xc-x_last| = 1.8910e+00 <= tolX*(1+|x0|) = 2.8859e+01
0 : |proj(x-g)-x|    = 2.7739e+00 <= tolG          = 1.0000e-01
0 : |proj(x-g)-x|    = 2.7739e+00 <= 1e3*eps       = 1.0000e-02
1 : maxIter   =      20    <= iter          =     20
------------------------- DONE! -------------------------
# inversion_results_app = ipywidgets.interact(
#     plot_results_at_iteration,
#     iteration = ipywidgets.IntSlider(min=0, max=inversion_results["iteration"][-1]-1, value=0)
# )
# inversion_results_app

6.2 analyze the results - data misfit

def plot_normalized_misfit(iteration=None, observed_data=synthetic_data, ax=None):
    Plot the normalized data misfit
    if ax is None:
        fig, ax = plt.subplots(1, 1)
    if iteration is None:
        dpred = inversion_results["dpred"][-1]
        dpred = inversion_results["dpred"][iteration]
    normalized_misfit = (dpred - observed_data.dobs)/observed_data.standard_deviation
    out = dc.utils.plot_pseudosection(
        observed_data, dobs=normalized_misfit, data_type="misfit",
        plot_type="contourf", data_location=True, ax=ax, 

    ax.set_title("normalized misfit")
    ax.set_aspect(1.5)  # some vertical exxageration
    ax.set_xlabel("Northing (m)")

    cb_axes = plt.gcf().get_axes()[-1]
    cb_axes.set_xlabel('normalized misfit')
    return ax
# plot misfit
fig, ax = plt.subplots(1, 1, figsize=(12, 4))
<AxesSubplot:title={'center':'normalized misfit'}, xlabel='Northing (m)', ylabel='n-spacing'>
<Figure size 864x288 with 2 Axes>

6.3 analyze the results - how did we get here?

def plot_beta(inversion_results, x="iteration", ax=None):
    Plot the beta-value as a function of iteration 
    if ax is None:
        fig, ax = plt.subplots(1, 1)
    if x not in ["iteration", "beta"]:
        raise Exception("beta should be plotted as a function of iteration or beta")
    ax.plot(inversion_results[x], inversion_results["beta"], "-oC0", label="$\\beta$")
    return ax

def plot_misfit_and_regularization(inversion_results, x="iteration", ax=None):
    Plot the data misfit and regularization as a function of iteration
    if ax is None:
        fig, ax = plt.subplots(1, 1)
    if x not in ["iteration", "beta"]:
        raise Exception("misfit and regularization should be plotted as a function of iteration or beta")
    ax.plot(inversion_results[x], inversion_results["phi_d"], "-oC1", label="$\Phi_d$")
    ax.axhline(phi_d_star, linestyle="--", color="k", label="$\Phi_d^*$")
    ax.axhline(phi_d_star, linestyle="-.", color="k", label="$\chi\Phi_d^*$")

    ax_phim = ax.twinx()
    ax_phim.plot(inversion_results[x], inversion_results["phi_m"], "-oC2", label="$\Phi_m$")
    return ax

def plot_regularization_components(inversion_results, x="iteration", ax=None):
    Plot the smallness and smoothness terms in the regularization as a function of iteration
    if ax is None:
        fig, ax = plt.subplots(1, 1)
    if x not in ["iteration", "beta"]:
        raise Exception("regularization components should be plotted as a function of iteration or beta")
    ax.plot(inversion_results[x], inversion_results["phi_m_small"], "-oC3", label="$\\alpha_s\Phi_s$")
    ax.plot(inversion_results[x], inversion_results["phi_m_smooth_x"], "-oC4", label="$\\alpha_x\Phi_x$")
    ax.plot(inversion_results[x], inversion_results["phi_m_smooth_z"], "-oC5", label="$\\alpha_z\Phi_z$")
    return ax

def plot_tikhonov_curve(inversion_results, ax=None):
    Plot the Tikhonov curve: Phi_d as a function of Phi_m
    if ax is None:
        fig, ax = plt.subplots(1, 1)
    ax.plot(inversion_results["phi_m"], inversion_results["phi_d"], "-oC6", label="$\Phi_d$")
    ax.axhline(phi_d_star, linestyle="--", color="k", label="$\Phi_d^*$")
    ax.axhline(phi_d_star, linestyle="-.", color="k", label="$\chi\Phi_d^*$")
    return ax
fig, ax = plt.subplots(3, 1, figsize=(10, 8), sharex=True)

plot_beta(inversion_results, ax=ax[0])
plot_misfit_and_regularization(inversion_results, ax=ax[1])
plot_regularization_components(inversion_results, ax=ax[2])
Text(0.5, 0, 'iteration')
<Figure size 720x576 with 4 Axes>
<AxesSubplot:xlabel='$\\Phi_m$', ylabel='$\\Phi_d$'>
<Figure size 432x288 with 1 Axes>

6.3 recovered model

def plot_model(
    model=model_recovered, ax=None, clim=np.exp(np.r_[np.min(model_true), np.max(model_true)]), 
    colorbar=True, zlim=np.r_[-400, 0]
    A plotting function for our recovered model 
    if ax is None:
        fig, ax = plt.subplots(1, 1)
    out = mesh.plotImage(
        mapping * model, pcolorOpts={'norm':LogNorm(), 'cmap':'Spectral'}, ax=ax,

    ax.set_ylabel('Elevation (m)')
    ax.set_xlabel('Easting (m)')
    ax.set_aspect(1.5)  # some vertical exxageration
    if colorbar is True:
        cb = plt.colorbar(out[0], fraction=0.05, orientation='horizontal', ax=ax, pad=0.25)
        cb.set_label("Resistivity ($\Omega$m)")
    return ax

6.4 Explore the results as a function of iteration

def plot_results_at_iteration(iteration=0):
    A function to plot inversion results as a function of iteration 
    fig = plt.figure(figsize=(12, 8))
    spec = gridspec.GridSpec(ncols=6, nrows=2, figure=fig)

    ax_tikhonov = fig.add_subplot(spec[:, :2])
    ax_misfit = fig.add_subplot(spec[0, 2:])
    ax_model = fig.add_subplot(spec[1, 2:])

    plot_tikhonov_curve(inversion_results, ax=ax_tikhonov)
        inversion_results["phi_m"][iteration], inversion_results["phi_d"][iteration], 
        'ks', ms=10
    ax_tikhonov.set_title(f"iteration {iteration}")
    plot_normalized_misfit(iteration=iteration, ax=ax_misfit)

    plot_model(inversion_results["model"][iteration], ax=ax_model, )
    # make the tikhonov plot square

inversion_results_app = ipywidgets.interact(
    iteration = ipywidgets.IntSlider(min=0, max=inversion_results["iteration"][-1]-1, value=0)

6.5 Compare with the True model

fig, ax = plt.subplots(2, 1, figsize=(10, 8))
clim = np.exp(np.r_[model_true.min(), model_true.max()])
plot_model(model_recovered, clim=clim,  ax=ax[0])
plot_model(model_true, clim=clim,  ax=ax[1])
<AxesSubplot:xlabel='Easting (m)', ylabel='Elevation (m)'>
<Figure size 720x576 with 4 Axes>

Homework ✏️

What happens if you introduce a layer above the blocks?

  • Define a layer from z=-50 to z=-100.
  • Start with a resistive layer.
    • How does this change our ability to resolve the blocks?
  • Now try with a conductive layer.
  • Get creative! Build a model that interests you.

Send us your images and discussion on slack!