# 9.6.2. Inverting MT Data¶

Here, we invert synthetic impedance tensor data using E3DMT versions 1 and 2. When inverting impedances, the E3DMT codes have a tendency to place conductive artifacts proximal to the receivers. To overcome this obstacle, we demonstrate a basic approach for limiting artifacts through the use of interface weights.

Important

The MT data are in GIF format; see GIF data conventions page. This means the locations are Easting-Northing-Elevation. The data convention however is defined X = Northing, Y = Easting and Z = Down. Failure to recognize this will cause incorrect interpretation of the data. To get addition background information on the coordinate systems used by the E3DMT codes, see the theory sections of the version 1 and version 2 manuals.

## 9.6.2.1. Setup for the Exercise¶

**If you have completed the tutorial** “Importing, Interpreting and Preparing NSEM Data”:

Open your pre-existing GIFtools project

Set the working directory (if you would like to change it)

**If you have NOT completed the previous tutorial and would like to start here, complete the following steps:**

Open GIFtools

Import the observed data in E3DMT version 1 format. The data file is

MTdata_v1.obsand is found in theassetsfolder (Impedance tensor data in V/A)Load OcTree mesh. Found in the folder

assets/octree_model_mt.Load active cells model. Found in the folder

assets/octree_model_mt.Load true model. Found in the folder

assets/octree_model_mt.

**Pro tip:** To avoid confusion between location and data coordinate systems, use the set data headers tool to define location columns as *Easting, Northing* and *Elevation*.

Important

Data were generated using E3DMT version 2 and a block model approximating TKC. To keep things simple, a constant topography of 400 m. Uncertainties of 0.0025 \(\pm\) 5% were added to all impedance tensor measurements.

## 9.6.2.2. Reducing Artifacts through Interface Weighting¶

When inverting MT data, the E3DMT codes have a tendency to place conductive structures near receiver locations due to the sensitivity of the data to those cells. Here, we generate interface weights to counteract this problem. By forcing lateral smoothness within the top few layers of cells, we can limit the artifacts and force the inversion to place conductive structures at the appropriate depths.

Use edit options and set the following parameters:

set the OcTree mesh

set as

log modelset topography as the active cells model

set 3 layers of surface weights with values 10, 5, and 2.5 in decreasing order

Face value = 0.01

Face tolerance = 0.01

## 9.6.2.3. E3DMT Version 1¶

Let us now invert the impedance tensor data using E3DMT version 1.

Use edit options to set the inversion parameters

- Basic Tab:

Select the impedance data

Set mesh

Set topography to active cells model

No background susceptibility

1D conductivity of 0.001 S/m (which we inferred from apparent resistivity maps)

Use

Iterativesolver unless you have sufficient RAM to useDirectsolver.

- Model Options Tab:

Set

Beta cooling scheduleto ‘custom by clicking button’. Usebeta max = 1,beta min = 1e-8andreduction factor = 0.25Set

Chi Factor= 0.8 since E3DMT version 1 uses a non-standard measure of data misfit; see manual <https://e3dmt.readthedocs.io/en/e3dmt/content/theory.html#data-misfit>

alpha S= 1e-10,alpha E= 1,alpha N= 1 andalpha Z= 2.56 (to recover smoothest model and balance gradient terms based on cell dimensions)Use the weights object to add additional weights

Set the

active cells topoas the active model cellsSet initial model as 0.001 S/m

Set reference model as 0.001 S/m

Set role in model objective function to

SMOOTH_MOD_DIFClick

Apply and write files

The results of the inversion are shown below. We are able to recover conductive pipe and the moderately conductive overburden. The conductivty of the overburden is also recovered (\(\sigma \approx 0.005\) S/m). We are also able to recover the approximate depth to the pipe (~100 m) and the pipe’s general shape.

The inversion reaches target misfit after 3 iterations. The model norm is discontinuous because the current model is set to be the reference model for the next beta value. The recovered model is able to reproduce the observed anomaly quite well. And misfit map indicated we are fitting each component of the data evenly.

## 9.6.2.4. E3DMT Version 2¶

Let us now invert the impedance tensor data using E3DMT version 2. Unlike version 1, version 2 requires that user define the receiver which measure the fields.

Click the impedance data object and set receivers from locations. Use the following values:

Easting width = 2 m

Northing width = 2 m

Vertical width = 2 m

Dipole length = 10 m

Use edit options to set the inversion parameters

- Basic Tab:

Select the impedance data

Set mesh

Set topography to active cells model

No background susceptibility

1D conductivity of 0.001 S/m (which we inferred from apparent resistivity maps)

Use

Iterativesolver unless you have sufficient RAM to useDirectsolver.

- Model Options Tab:

Set

Beta cooling scheduleto ‘custom by clicking button’. Usebeta max = 1,beta min = 1e-8andreduction factor = 0.25Set

Chi Factor= 1

alpha S= 1e-10,alpha E= 1,alpha N= 1 andalpha Z= 2.56 (to recover smoothest model and balance gradient terms based on cell dimensions)Use the weights object to add additional weights

Set the

active cells topoas the active model cellsSet initial model as 0.001 S/m

Set reference model as 0.001 S/m

Set role in model objective function to

SMOOTH_MOD_DIFClick

Apply and write files

The results of the inversion are shown below. We are able to recover conductive pipe and the moderately conductive overburden. The conductivty of the overburden is also recovered (\(\sigma \approx 0.005\) S/m). We are also able to recover the approximate depth to the pipe (~100 m) and the pipe’s general shape.

The inversion reaches target misfit after 3 iterations. The model norm is discontinuous because the current model is set to be the reference model for the next beta value. The recovered model is able to reproduce the observed anomaly quite well. And misfit map indicated we are fitting each component of the data evenly.