5.2.6. DC Inversion

Here, we use dcsensitivity.exe and sens2weights.exe to compute sensitivity weights for the DC inversion. Then we use the code dcoctree_inv.exe to recover a conductivity model. Because this is a simple example with no noise, we assigned uncertainties of 1e-8 V + 1% to all DC data. In practice, data are noisy and choosing appropriate uncertainties is very important for successful inversion.

Note

Depending on the application, sensitivity and/or interface weighting may or may not improve the final model. Here we apply the weights to promote familiarity with this code. For this example, the data are very-well constrained by the data and significant weighting may not even be necessary.

5.2.6.1. Sensitivity Weights

Here, the code dcsensitivity.exe and the input file dcsens.inp (see format ) are used to approximate the sensitivities. Then the code sens2weights.exe and the input file sens2weights.inp create a sensitivity weights file. This counteracts the inversion’s natural tendancy to incorrectly place anomalous structures near the electrodes. Files relevant to this part of the example are in the sub-folder dc_sensitivities . Before running this example, you may want to do the following:

To compute the sensitivities, the following input file was used:

../../_images/dcsens_input.png

To generate the sensitivity weights file, the following input file was used:

../../_images/dcweights_input.png

The final sensitivity weights for the DC inversion is shown below.

../../_images/dc_sens_weights.png

5.2.6.2. DC Inversion

Here we use the code dcoctree_inv.exe to recover a conductivity model. Before running the example, you may want to:

Files relevant to this part of the example are in the sub-folder dc_inv. To invert the synthetic data, the input file below (dc_inv.inp) was used. For formatting, see format :

../../_images/dcinv_input.png

The true model (left) and the final recovered model (right) are shown below.

../../_images/dc_inv.png