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E4D User Guide

E4D User Guide

### Electrical Methods Inverse Modeling

Inversion modeling is by far the most computationally demanding in terms of both cpu cycles and memory requirements. To accommodate these requirements, the inversion algorithm is implemented in parallel; forward simulations, Jacobian matrix construction, and Jacobian matrix storage are evenly divided among the set of slave processors.

The information provided by an ER survey is typically insufficient to uniquely determine the subsurface bulk conductivity distribution at the scale of the computational mesh. In order to produce a reasonable representation of the true subsurface conductivity, the inverse solution must be constrained by a priori information in addition to the information provided by the data. The inverse modeling input provided by the data is given implicitly in the survey file. The a priori solution constraints are provided in the inverse options file.

**Workflow for Inverse Modeling**

**Step 1: Mesh Selection**

- If mesh exists, go to step 2
- If mesh does not exist, create mesh using
**E4D Mesh Generation**

**Step 2: Inverse Modeling Input**

- starting conductivity model
- specify homogeneous distribution (eg. 0.01 or 'average') in
**<e4d.inp> file** create*OR***conductivity file**

- specify homogeneous distribution (eg. 0.01 or 'average') in
**survey file****output options file****inversion options file**- reference model
**conductivity file**

**Step 3: E4D Execution**

- Create
**<e4d.inp> file** **Run E4D**

**Output Files**

**conductivity file**named <sigma.#> where # is the iteration number**<run_time.txt> file****simulated data file****<e4d.log>**