Gippsland Basin Uncertainty Study
=================================

.. code:: python

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.. code:: python

    %matplotlib inline

.. code:: python

    #import the ususal libraries + the pynoddy UncertaintyAnalysis class

    import sys, os, pynoddy
    # from pynoddy.experiment.UncertaintyAnalysis import UncertaintyAnalysis

    # adjust some settings for matplotlib
    from matplotlib import rcParams
    # print rcParams
    rcParams['font.size'] = 15

    # determine path of repository to set paths corretly below
    repo_path = os.path.realpath('../..')
    import pynoddy.history
    import pynoddy.experiment.uncertainty_analysis
    rcParams.update({'font.size': 20})

The Gippsland Basin Model
-------------------------

In this example we will apply the UncertaintyAnalysis class we have been
playing with in the previous example to a 'realistic' (though highly
simplified) geological model of the Gippsland Basin, a petroleum field
south of Victoria, Australia. The model has been included as part of the
PyNoddy directory, and can be found at
``pynoddy/examples/GBasin_Ve1_V4.his``

.. code:: python

    reload(pynoddy.history)
    reload(pynoddy.output)
    reload(pynoddy.experiment.uncertainty_analysis)
    reload(pynoddy)

    # the model itself is now part of the repository, in the examples directory:
    history_file = os.path.join(repo_path, "examples/GBasin_Ve1_V4.his")

While we could hard-code parameter variations here, it is much easier to
store our statistical information in a csv file, so we load that
instead. This file accompanies the ``GBasin_Ve1_V4`` model in the
pynoddy directory.

.. code:: python

    params = os.path.join(repo_path,"examples/gipps_params.csv")

Generate randomised model realisations
--------------------------------------

Now we have all the information required to perform a Monte-Carlo based
uncertainty analysis. In this example we will generate 100 model
realisations and use them to estimate the information entropy of each
voxel in the model, and hence visualise uncertainty. It is worth noting
that in reality we would need to produce several thousand model
realisations in order to adequately sample the model space, however for
convinience we only generate a small number of models here.

.. code:: python

    # %%timeit   # Uncomment to test execution time
    ua = pynoddy.experiment.uncertainty_analysis.UncertaintyAnalysis(history_file, params)
    ua.estimate_uncertainty(100,verbose=False)

A few utility functions for visualising uncertainty have been included
in the UncertaintyAnalysis class, and can be used to gain an
understanding of the most uncertain parts of the Gippsland Basin. The
probabability voxets for each lithology can also be accessed using
``ua.p_block[lithology_id]``, and the information entropy voxset
accessed using ``ua.e_block``.

Note that the Gippsland Basin model has been computed with a vertical
exaggeration of 3, in order to highlight vertical structure.

.. code:: python

    ua.plot_section(direction='x',data=ua.block)
    ua.plot_entropy(direction='x')

.. figure:: 7-Gippsland-Basin-Uncertainty_files/7-Gippsland-Basin-Uncertainty_11_0.png
   :alt: png

   png

.. figure:: 7-Gippsland-Basin-Uncertainty_files/7-Gippsland-Basin-Uncertainty_11_1.png
   :alt: png

   png

It is immediately apparent (and not particularly surprising) that
uncertainty in the Gippsland Basin model is concentrated around the thin
(but economically interesting) formations comprising the La Trobe and
Strzelecki Groups. The faults in the model also contribute to this
uncertainty, though not by a huge amount.

Exporting results to VTK for visualisation
------------------------------------------

It is also possible (and useful!) to export the uncertainty information
to .vtk format for 3D analysis in software such as ParaView. This can be
done as follows:

.. code:: python

    ua.extent_x = 29000
    ua.extent_y = 21600
    ua.extent_z = 4500

    output_path = os.path.join(repo_path,"sandbox/GBasin_Uncertainty")
    ua.export_to_vtk(vtk_filename=output_path,data=ua.e_block)

The resulting vtr file can (in the sandbox directory) can now be loaded
and properly analysed in a 3D visualisation package such as ParaView.

.. figure:: 15-Gippsland-Basin-Uncertainty_files/3D-render.png
   :alt: 3-D visualisation of cell information entropy

   3-D visualisation of cell information entropy

.. code:: python