Stratigraphic#

Construct layers model from given layers properties such as density, porosity permeability, transmissivity, resistivity , patches and so on … Build Statigraphic model from Inversion models blocks. This should be used to predict the log under each station as well as the thicknesses from the collected true boreholes and well resistivity data in the survey area.

class watex.geology.stratigraphic.GeoStrataModel(area=None, beta=5, n_epochs=100, ptol=0.1, eta=0.0001, kind='linear', degree=1, z0=0.0, max_depth=700.0, step=50.0, doi='1km', tolog10=False, verbose=False, **kwargs)[source]#

Bases: GeoBase

Create a stratigraphic model from 2D inversion models blocks.

The Inversion model block is two dimensional array of shape (n_vertical_nodes, n_horizontal_nodes ). Can use external packages to

build blocks and provide the 2Dblock into crm parameter.

The challenge of this class is firstly to delineate with much accuracy the existing layer boundary (top and bottom) and secondly,to predict the stratigraphy log before the drilling operations at each station. Moreover, it’s a better way to select the right drilling locationand also to estimate the thickness of existing layer such as water table layer as well as to figure out the water reservoir rock in the case of groundwater exploration [1].

Note that if the model blocks is build from external softwares. User can provided model usefull details into the keyword arguments of the fit methods. For instance the model_station_locations, model_depth, model_x_nodes etc. See more details in fit method docstring.

Parameters

area (str, optional) – The name of the area where the survey is done.
beta (int, default=5) – Value to divide into the CRM blocks to improve the computation times.
n_epochs (int, default=100) – Number of iterations for new resistivity model construction (NM).
ptol (float, default=.1) – the error value to tolerate during NM construction between the tres values given and the calculated resistivity in crm. Higher is the error, less is the accuracy.
eta (float, default=1e4) – It is the learning rate for gradient descent computing to reach its convergence. If kind is set to polynomial the default value should be set`1e-8`.
kind (str , default='linear') – Kind of model function to compute the best fit model to replace the value in crm . Can be ‘linear’ or ‘polynomial’. if polynomial is set, specify the degree.
degree (int, default=1) – Polynomial function degree to implement gradient descent algorithm. If kind is set to Polynomial the default degree is 3.
z0 (float, default=0.) – The elevation at the first stations. Note here, we consider that no topography is included and the default is the level of the sea. If the model_depth`if provided in the `fit_params, no need to specify.
max_depth (float, default=700) – The maximum depth for building the block. by default, the unit is in meters. If model_depth is given, no need to provide.
step (float, default=50) – The step between stations. Here the unit is in meters. If the model_station_locations if given through the fit_params, no need to specified.
doi (default='1km') – the depth of investigation of the skin-depth. By default without any unit suffix, it should be in meters.
tolog10 (bool, default=False) – Convert the true values of layer resistivities to log10.
verbose (bool, default=False) – Output messages and warn users.

nm_#

The New resistivity model (NM) matrix with the same dimension with crm model blocks.

Type: Ndarray of ( n_depth , n_stations )

nmSites_#

The recomputed station locations of the new resistivity model (NM)

Type: ArrayLike

crmSites_#

The recomputed stations locations of the model-calculated resistivity (CRM)

Type: ArrayLike,

Examples

>>> import numpy as np
>>> from watex.geology.stratigraphic import GeoStrataModel
>>>
>>> # (1):  Using the CRM without any inversion specified files.
>>>
>>> # test while files
>>> np.random.seed (42)
>>> # generate a model calculated resistivity, layers and true
>>> # resistivities values
>>> crm = np.abs( np.random.randn (215 , 70 ) *1000 )
>>> tres = np.linspace (crm.min() +1  , crm.max() +1 , 12 ) #
>>> layers = ['granites', 'gneiss', 'sedim.']
>>> gs= GeoStrataModel( to_log10 =True )
>>> gs.fit(crm, tres = tres, layers =layers).buildNM(display_infos =True )
>>> print( gs.nm_.shape)
build-NM: 18B [00:01, 12.60B/s]
----------------------------------------------------------------------
                      layers [auto=automatic](13)
----------------------------------------------------------------------
   1. hard rock (auto)                2. clay (auto)
   3. conglomerate (auto)             4. sedimentary rock (auto)
   5. gravel (auto)                   6. igneous rock (auto)
   7. fresh water (auto)              8.Sedim.
   9.Gneiss                          10. *struture not found (auto)
  11. saprolite (auto)               12. duricrust (auto)
  13.Granites
----------------------------------------------------------------------
(215, 70)
>>> gs.strataModel () # plot strata model, by default kind ='NM'
>>> gs.plotStrata ('s7')  # plot strata log at station S7
Out[7]: watex.geology.stratigraphic.PseudoStratigraphic
>>>
>>> # (2): Works with occam2d inversion files if 'pycsamt' or 'mtpy'
>>> # is installed. It will call the module Geodrill from pycsamt to
>>> make occam2d 2D resistivity block for our demo.
>>> # It presumes pycsamt is installed.
>>>
>>> from pycsamt.geodrill.geocore import Geodrill
>>> path=r'data/inversfiles/inver_res/K4' # path to inversion files
>>> inversion_files = {'model_fn':'Occam2DModel',
...                   'mesh_fn': 'Occam2DMesh',
...                    "iter_fn":'ITER27.iter',
...                   'data_fn':'OccamDataFile.dat'
...                    }
>>> tres =[10, 66, 70, 180, 1000, 2000, 3000, 7000, 15000 ]
>>> layers =['river water', 'fracture zone', 'granite']
>>> inversion_files = {key:os.path.join(path, vv) for key,
                vv in inversion_files.items()}
>>> gdrill= Geodrill (**inversion_files,
                     input_resistivities=input_resistivity_values
                     )
>>> # we can collect the 'model_res' and occam2d inversion usefull
>>> # 'attributes' from  `gdrill object` and passed to the
>>> # 'GeoStrataModel' then fit_params keyword arguments method as
>>> geosObj = GeoStrataModel(ptol =0.1).fit(
                     crm = gdrill.model_res ,
                     tres=gdrill.input_resistivities,
                     layers=gdrill.input_layer_names,
                     model_x_nodes=gdrill.model_x_nodes,
                     model_stations= gdrill.station_names,
                     model_depth= gdrill.geo_depth,
                     model_station_locations= gdrill.station_locations,
                     data_fn = gdrill.data_fn ,
                     mesh_fn=gdrill.mesh_fn,
                     iter_fn= gdrill.iter_fn
                     model_fn= gdrill.model_fn)
>>> geosObj.buildNM ()
>>> zmodel = geosObj._zmodel
>>> geosobj.nm_ # New constructed resistivity 2D model block

Notes

Modules work properly with occam2d inversion files if ‘pycsamt’ or ‘mtpy’ is installed and inherits the GeoBase class which works with geological structures and properties. Furhermore, Occam2d inversion files are also acceptables for building model blocks. However the MODEM resistivity files development is still ongoing.

References

1: Kouadio, L. K., Liu, R., Malory, A. O., Liu, W., Liu, C., A novel approach for water reservoir mapping using controlled source audio - frequency magnetotelluric in Xingning area , Hunan Province, China. Geophys. Prospect., https://doi.org/10.1111/1365-2478.1338

buildNM(return_NM=Ellipsis, display_infos=Ellipsis)[source]#: Build New discrete resistivity from the model-calculated resistivity CRM Trigger the NM build and return the NM building option

fit(crm, tres=None, layers=None, **fit_params)[source]#

Check, populate attributes and rebuild the model-calculated resistivity (CRM).

Parameters

crm (ndarray of shape(n_vertical_nodes, n_horizontal_nodes ),) – Array-like of inversion two dimensional model blocks. Note that the n_vertical_nodes is the node from the surface to the depth while n_horizontal_nodes must include the station location (sites/stations)
tres (List, Arraylike of float,) – Layer true values of resistivity collected in the survey area. refer to [1] for more details. Resistivity is preferable to be distinct.
layers (list or ArrayLike of str) – The name of layers collected in the survey area. Their corresponding resistivity values are the argument of tres. Mostly, it refers to the geological informations of collected in the area.
fit_params (dict,) –
If a keyword argument refering to inversion model details, it should be prefixed by the keyword model_ such as the the following attributes:
- model_depth: Arraylike shape (n_depth, ) of the model. It is the depth the surface to the bottom of each layer that composed the pseudo-boreholes. It refer to the n_vertical nodes.
- model_resistivities: ArrayLike of shape ( y_nodes , x_nodes ). It is array of model block composed of resistivity values of vertical nodes and horizontal nodes. It is the model-calculated resistivity. No need if the value is passed to crm parameter.
- model_x_nodes: ArrayLike of shape (x_nodes, ) is the horizontal nodes of the model block.
- model_station_locations: Arraylike of offset of valid stations locations. It might truly refers to the number of investigated stations.
- model_stations: List or Arrylike of the stations names of the investigated area. It might be consistent with the model_station_locations.
- model_rms: Root-Mean-Squared values of the models after inversion. By default , the target is set to 1.0.
- model_roughness: Roughness of the models. This is an optional parameters and using when OCCAM2D software is used to invert the data.
Model files can also be includes. In that case, the attributes must suffixes with _fn. Some useful model files can be model, mesh data and iter. For instance to set the data file to a keyword arguments it sould be data_fn=xxxx whether the new data attribute should be retrived as data_fn. Note that:
- model_fn is the model files after inversions
- data_fn is the data files before inversions
- iter_fn is the iteration results files if applicable with
  the kind of inversion software used
- mesh_fn is the mesh files construct for forward modeling.

Returns

``self`` – return self for methods chaining.

Return type

watex.geology.stratigraphic.GeoStrataModel.

References

1: Kouadio, L. K., Liu, R., Malory, A. O., Liu, W., Liu, C., A novel approach for water reservoir mapping using controlled source audio - frequency magnetotelluric in Xingning area , Hunan Province, China. Geophys. Prospect., https://doi.org/10.1111/1365-2478.1338

classmethod geoArgumentsParser(config_file=None)[source]#

Read and parse the GeoStrataModel arguments files from the config [JSON|YAML] file. :param config_file: configuration file. Can be [JSON|YAML]

Example

>>> GeoStrataModel.geoArgumentsParser(
    'data/saveJSON/cj.data.json')
>>> GeoStrataModel.geoArgumentsParser(
    'data/saveYAML/cy.data.yml')

property inspect#: Inspect object whether is fitted or not

static plotStrata(station, zoom=None, annotate_kws=None, **kws)[source]#

Build the Stratalog. :param station: station to visualize the plot. :param zoom: float represented as visualization ratio

ex: 0.25 –> 25% view from top =0.to 0.25* investigation depth or a list composed of list [top, bottom].

Example

>>> input_resistivity_values =[10, 66,  700, 1000, 1500,  2000,
                        3000, 7000, 15000 ]
>>> input_layer_names =['river water', 'fracture zone', 'granite']
# Run it to create your model block alfter that you can only use
#  `plotStrata` only
# >>> obj= quick_read_geomodel(lns = input_layer_names,
#                             tres = input_resistivity_values)
>>> plotStrata(station ='S00')

set_inversion_model_attr(**model_params)[source]#

Set inversion model parameters as attributes. Note all related to the raw inversion model strats with attribte model_xxx ` while all related to files must must suffixed with `_fn. For instances the folling data such [‘model’, ‘iter’, ‘data’ , ‘mesh’] can be added as keywords arguments where _fn is suffixed to each names.

Parameters

model_parameters (dict) –

Keyword arguments from inversion model parameters. The useful attributes passed to accurathe model creation are:

model_depth: Arraylike shape (n_depth, ) of the model, mostly refer to the vertical nodes.
model_resistivities: ArrayLike of shape ( y_nodes , x_nodes ). It is array of model block composed of resistivity values of vertical nodels and horizontal nodes. It is the model-calculated resistivity. It is usefull to pass the same argument as crm parameters.
model_x_nodes: ArrayLike of shape (x_nodes, ) is the horizontal nodes of the model block.
model_station_locations: Arraylike of offset of valid stations locations. It might truly refers to the number of investigated stations.
model_stations: List or Arraylike of the stations names of the investigated area. It might be consistent with the model_station_locations.
model_rms: Root-Mean-Squared values of the models after inversion. By default , the target is set to 1.0.
model_roughness: Roughness of the models. This is an optional parameters and using when OCCAM2D software is used to invert the data.

Model files can also be includes. In that case, the attributes must suffixes with _fn. Some useful model files can be model, mesh data and iter. For instance to set the data file to a keyword arguments it sould be data_fn=xxxx whether the new data attribute should be retrived as data_fn. Note that:

model_fn is the model files after inversions
data_fn is the data files before inversions
iter_fn is the iteration results files if applicable with the kind of inversion software used
mesh_fn is the mesh files construct for forward modeling.

strataModel(kind='nm', **kwargs)[source]#

Visualize the strataModel after nm creating using decorator from :class:’~.geoplot2d’.

Parameters

kind – can be : - nm mean new model plots after inputs the tres - crm means calculated resistivity from occam model blocks default is nm.
plot_misft – Set to True if you want to visualise the error between the nm and crm.
scale – Can be m or km for plot scale
in_percent` – Set to True to see your plot map scaled in %.

Example

>>> from watex.geology.stratigraphic import GeostrataModel
>>> geosObj = GeostrataModel().fit(**inversion_files,
                      input_resistivities=input_resistivity_values,
                      layers=input_layer_names)
>>> geosObj.strataModel(kind='nm', misfit_G =False)

property subblocks#: Model subblocks divised by beta

property tres#: Input true resistivity