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watex.utils.sfi#

watex.utils.sfi(cz, p=None, s=None, dipolelength=None, view=False, raw=False, return_components=False, **plotkws)[source]#

Compute the pseudo-fracturing index known as sfi.

The sfi parameter does not indicate the rock fracturing degree in the underground but it is used to speculate about the apparent resistivity dispersion ratio around the cumulated sum of the resistivity values of the selected anomaly. It uses a similar approach of IF parameter proposed by `Dieng et al`_ (2004). Furthermore, its threshold is set to \(sqrt{2}\) for symmetrical anomaly characterized by a perfect distribution of resistivity in a homogenous medium. The formula is given by:

\[sfi=\sqrt{(P_a^{*}/P_a )^2+(M_a^{*}/M_a )^2}\]

where \(P_a\) and \(M_a\) are the anomaly power and the magnitude respectively. \(P_a^{*}\) is and \(M_a^{*}\) are the projected power and magnitude of the lower point of the selected anomaly.

Parameters:

  • cz (array-like,) – Selected conductive zone

  • p (array-like,) – Station positions of the conductive zone.

  • dipolelength (float. If p is not given, it will be set) – automatically using the default value to match the cz size. The default value is 10..

  • view (bool, default=False,) – Visualize the fitting curve. Default is False.

  • raw (bool,default=False,) – Overlaining the fitting curve with the raw curve from cz.

  • return_components (bool, default=False,) – If True, it returns the different components used for compute sfi especially for external visualization.

  • plotkws (dict) – `Matplotlib plot`_ keyword arguments.

Returns:

sfi – value computed for pseudo-fracturing index

Return type:

float

Examples

>>> import numpy as np
>>> from watex.property import P
>>> from watex.utils.exmath import sfi
>>> rang = np.random.RandomState (42)
>>> condzone = np.abs(rang.randn (7))
>>> # no visualization and default value `s` with global minimal rho
>>> pfi = sfi (condzone)
... 3.35110143
>>> # visualize fitting curve
>>> plotkws  = dict (rlabel = 'Conductive zone (cz)',
                     label = 'fitting model',
                     color=f'{P().frcolortags.get("fr3")}',
                     )
>>> sfi (condzone, view= True , s= 5, figsize =(7, 7),
          **plotkws )
Out[598]: (array([ 0., 10., 20., 30.]), 1)

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