.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "advanced_examples/rock_physics_screening_PDF.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_advanced_examples_rock_physics_screening_PDF.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_advanced_examples_rock_physics_screening_PDF.py:


Rock physics data screening: 2D probability density function (PDF)
==================================================================

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.. code-block:: Python



    import numpy as np
    import matplotlib.pyplot as plt
    import pandas as pd
    plt.rcParams['font.size']=14
    plt.rcParams['font.family']='arial'
    plt.rcParams['axes.labelpad'] = 10.0









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.. code-block:: Python



    # import the module 
    from rockphypy import QI











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When data points cluster or overlap, it can be challenging to visualize their distribution. To address this issue, we can estimate the probability density function (PDF) of the data using kernel density estimation, which can enhance the visual clarity of the data. In "QI", the built-in method for performing KDE is KDE diffusion.


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.. code-block:: Python



    # parameters 
    Dqz, Kqz, Gqz = 2.65, 36.6, 45 ## grain density, bulk and shear modulus 
    Dsh, Ksh, Gsh = 2.7, 21, 7 # shale/clay density, bulk and shear modulus
    Dc,Kc, Gc =2.65, 36.6, 45 # cement density, bulk and shear modulus
    Db, Kb = 1, 2.2 # brine density, bulk modulus
    Do, Ko = 0.8, 1.5 # oil density, bulk modulus
    Dg, Kg = 0.2, 0.06 # gas density, bulk modulus
    phi_c=0.4 # critical porosity
    sigma=20 # effective pressure 
    scheme=2
    Cn=8.6
    # could be array
    vsh=0 # shale volume
    #phib_p=[0.3,0.36,0.38,0.39] # define cement porosity for Vp
    phib_p=0.3
    f= 0.5 # slip factor 










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Applied to field data 
^^^^^^^^^^^^^^^^^^^^^
Let's import the same synthetic well log data and apply the rock physics screening to the well log data 


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.. code-block:: Python


    # read data: fork the repo to get the dataset first and run this example. 
    data = pd.read_csv('../../data/well/sandstone.csv',index_col=0)

    # compute the elastic bounds
    phi,vp1,vp2,vp3,vs1,vs2,vs3 = QI.screening(Dqz,Kqz,Gqz,Dsh,Ksh,Gsh,Dc,Kc,Gc,Db,Kb,phib_p,phi_c,sigma,vsh,scheme,f, Cn)

    # create an object with data 
    qi= QI(data.VP,phi=data.PHIT_ND,Vsh= data.VSH_GR)

    # call the screening plot method 
    fig= qi.kde_plot(phi,vp1,vp2,vp3)

    plt.ylim([1900,6100])
    plt.ylabel('Vp (Km/s)')
    plt.yticks(np.arange(2000,6200, 1000),[2,3,4,5,6])




.. image-sg:: /advanced_examples/images/sphx_glr_rock_physics_screening_PDF_001.png
   :alt: rock physics screening PDF
   :srcset: /advanced_examples/images/sphx_glr_rock_physics_screening_PDF_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    ([<matplotlib.axis.YTick object at 0x7578c0921a90>, <matplotlib.axis.YTick object at 0x7578c0921eb0>, <matplotlib.axis.YTick object at 0x7578cf0bb340>, <matplotlib.axis.YTick object at 0x7578c2de0b50>, <matplotlib.axis.YTick object at 0x7578c2de0370>], [Text(0, 2000, '2'), Text(0, 3000, '3'), Text(0, 4000, '4'), Text(0, 5000, '5'), Text(0, 6000, '6')])



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As shown by the figure, using a 2D PDF can provide a clearer visualization of the data distribution compared to a normal scatter plot.

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**Reference** 
- Avseth, P., Lehocki, I., Kjøsnes, Ø., & Sandstad, O. (2021). Data‐driven rock physics analysis of North Sea tertiary reservoir sands. Geophysical Prospecting, 69(3), 608-621.
- Kernel density estimation via diffusion in 1d and 2d: https://kde-diffusion.readthedocs.io/en/stable/


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.091 seconds)


.. _sphx_glr_download_advanced_examples_rock_physics_screening_PDF.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: rock_physics_screening_PDF.ipynb <rock_physics_screening_PDF.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: rock_physics_screening_PDF.py <rock_physics_screening_PDF.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: rock_physics_screening_PDF.zip <rock_physics_screening_PDF.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_