Magnetometer Datas inversion. Here we try to get a 3D recovered magnetic susceptibility model with invertion total magnetic intensity (TMI).
We use theses SimPeg examples:
-Sparse Norm Inversion for Total Magnetic Intensity Data on a Tensor Mesh -Slicer demo
The pictures are from Emigma 7.8 for the same datas but with theses (17.55.11.jpeg, 17.55.12.jpeg)
Here we invert total magnetic intensity (TMI) data to recover a magnetic susceptibility model. We formulate the inverse problem as an iteratively re-weighted least-squares (IRLS) optimization problem. For this tutorial, we focus on the following:
- Defining the survey from xyz formatted data
- Generating a mesh based on survey geometry
- Including surface topography
- Defining the inverse problem (data misfit, regularization, optimization)
- Specifying directives for the inversion
- Setting sparse and blocky norms
- Plotting the recovered model and data misfit
Although we consider TMI data in this tutorial, the same approach can be used to invert other types of geophysical data.
The example demonstrates the plot_3d_slicer
- contributed by
@prisae <https://github.com/prisae>_
Using the inversion result from the example notebook
plot_laguna_del_maule_inversion.ipynb <http://docs.simpeg.xyz/content/examples/20-published/plot_laguna_del_maule_inversion.html>_
In the notebook, you have to use :code:%matplotlib notebook.