class watex.geology.drilling.Borehole(lat=None, lon=None, area=None, status=None, depth=None, base_depth=None, geol=None, staticlevel=None, airlift=None, id=None, qmax=None, **kwds)[source]#

Bases: Geology

Focused on Wells and Borehole offered to the population. To use the data for prediction purpose, each Borehole provided must be referenced on coordinates values or provided the same as the one used on ves or erp file.

fit(data)[source]#

Fit Borehole data and populate the corrsponding attributes

class watex.geology.drilling.DSBorehole(hole=None, dname=None, projection='ll', utm_zone=None, datum='WGS84', epsg=None, reference_ellipsoid=23, encoding='utf-8', lonlat=None, verbose=0)[source]#

Bases: object

Class delas with Borehole datasets.

watex.geology.drilling.DSBorehole works with data collected in a single borehole. For instance, it could follow the arrangement of h502 data in watex.datasets.load_hlogs()

Parameters:

  • hole (str) – Name or ID of the borehole.

  • dname (str, optional) – Depth column name. If depth is specify an attribute depth_ should be created. Depth specification is usefull for log plotting of machine training.

  • utm_zone (Optional, string) – zone number and ‘S’ or ‘N’ e.g. ‘55S’. Default to the centre point of coordinates points in the survey area. It should be a string (##N or ##S) in the form of number and North or South hemisphere, 10S or 03N

  • projection (str, ['utm'|'dms'|'ll']) – The coordinate system in which the data points for the profile is collected. If not given, the auto-detection will be triggered and find the suitable coordinate system. However, it is recommended to provide it for consistency. Note that if x and y are composed of value less than 180 degrees for longitude and 90 degrees for latitude, it should be considered as longitude-latitude (ll) coordinates system. If x and y are degree-minutes-second (dms or dd:mm:ss) data, they must be specify as coordinate system in order to accept the non-numerical data before transforming to ll. If data is passed to the fit() method and dms is not specify, x and y values should be discarded.

  • datum (string, default = 'WGS84') – well known datum ex. WGS84, NAD27, NAD83, etc.

  • epsg (Optional, int) –

    epsg number defining projection (

    see http://spatialreference.org/ref/ for moreinfo)

    Overrides utm_zone if both are provided.

  • reference_ellipsoid (int, default=23) – reference ellipsoids is derived from Peter H. Dana’s website- http://www.utexas.edu/depts/grg/gcraft/notes/datum/elist.html Department of Geography, University of Texas at Austin Internet: pdana@mail.utexas.edu . Default is 23 constrained to WGS84.

  • encoding (str, default ='utf8') – Default encoding for parsing data. Can also be [‘utf-16-be’] for reading bytes characters.

  • lonlat (Tuple, Optional) – longitude/latitude for borehole coordinates. The location where the borehole is performed.

  • verbose (int, default=0) – Output messages.

depth_#

Depth array if dname is specified.

Type:

Series

data_#

Sanitized dataframe.

Type:

Pandas DataFrame

Note

Each columns of the dataframe is an attribute. Note that all the non- alphabetic letters is removed and replace by ‘_’.

Examples

>>> import watex as wx
>>> from watex.geology import DSBorehole
>>> hdata= wx.fetch_data ('hlogs',samples = 12 ).frame
>>> b = DSBorehole (hole='H502').fit(hdata)
>>> b.feature_names_in_
Out[77]:
['depth_top',
 'depth_bottom',
 'layer_thickness',
 'resistivity',
 'gamma_gamma',
 'natural_gamma',
 'sp',
 'short_distance_gamma',
 'well_diameter',
 'aquifer_thickness',
 'hole_depth_before_pumping',
 'hole_depth_after_pumping',
 'hole_depth_loss',
 'depth_starting_pumping',
 'pumping_depth_at_the_end',
 'pumping_depth',
 'section_aperture',
 'k',
 'kp',
 'r',
 'rp',
 'hole_id',
 'strata_name',
 'rock_name',
 'aquifer_group',
 'pumping_level']
>>> b.strata_name
Out[78]:
0                       topsoil
1                        gravel
2                      mudstone
3                     siltstone
4                      mudstone

176 coal 177 siltstone 178 coarse-grained sandstone 179 fine-grained sandstone 180 coarse-grained sandstone Name: strata_name, Length: 181, dtype: object

fit(data, **fit_params)[source]#

Fit Borehole data and populate attribute data.

By default if the projection is given as latitude/longitude xlon, ylat are longitude and latitude respectively.

Parameters:

  • data (pd.DataFrame or Path-like object.) – Data containing xlon and y values as series. Then if xlon and y are given as string argument, their names must be included in the data columns. Otherwise an error will raise.

  • fit_params (dict,) – Keyword arguments passed to watex.utils.to_numeric_dtypes() for data management.

Returns:

self – Instanced object for chaining method.

Return type:

Instanced object

property inspect#

Inspect object whether is fitted or not

set_depth(z0=0.0, max_depth=None, reset_depth=Ellipsis)[source]#

Set the a random depth if depth is not supplied in the Borehole data

To fetch the depth, use attribute depth_. Note that if the depth exists, calling set_depth will erase the former depth value. Use in cautioness.

Parameters:

  • z0 (float, default=0.) – The surface reference. Preferably, it is set to null.

  • max_depth (float, default=700.) – The maximum depth. Depth size must fit the length of the data in meters. Default depth is fixed to 700 meters.

  • reset_depth (bool, default =False,) – An alternative way to controle whether to keep the previous and new computed depth in the borehole data. The parameter erases the previous depth if exists the borehole data. If True a new depth is set in replacement to the previous depth and both are kept in the data otherwise.

Returns:

self – Instanced object for chaining method.

Return type:

Instanced object

Examples

>>> import watex as wx
>>> from watex.geology import DSBorehole
>>> hdata= wx.fetch_data ('hlogs').frame
>>> b = DSBorehole (hole='H502').fit(hdata)
>>> b.set_depth ()
>>> b.depth_
Out[82]:
0        0.000000
1        3.888889
2        7.777778
3       11.666667
4       15.555556

176 684.444444 177 688.333333 178 692.222222 179 696.111111 180 700.000000 Name: depth, Length: 181, dtype: float64 >>> b.set_depth (max_depth = 900, reset_depth= True ) >>> b.depth_ Out[85]: 0 0.0 1 5.0 2 10.0 3 15.0 4 20.0

176 880.0 177 885.0 178 890.0 179 895.0 180 900.0 Name: depth, Length: 181, dtype: float64

set_strata(add_electrical_properties=False, random_state=None, shuffle=True, reset_strata=Ellipsis, reset_electrical_properties=Ellipsis)[source]#

Generate a pseudo strata associated to each depth in the borehole data.

Parameters:

  • add_electrical_properties (bool, default=False,) – Add electrical resistivty values associated to each generated stratum

  • random_state (int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional) – If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given.

  • shuffle (bool, default=True) – Shuffle the random generated thicknesses.

  • reset_strata (bool, default=False,) – generate new strata at each depth. If True and the name strata is valid in the borehole data colum name. Layer names that composes each stratum should be erased.

  • reset_electrical_properties (bool, default=False) – Erase the former electrical values and replace by new names that fit each strata.

Returns:

self – Instanced object for chaining method.

Return type:

Instanced object

Examples

>>> import watex as wx
>>> from watex.geology import DSBorehole
>>> hdata= wx.fetch_data ('hlogs', key='h803').frame
>>> b = DSBorehole (hole='H803').fit(hdata)
>>> b.set_strata ()
>>> b.strata_
Out[122]:
0                tourmalinite
1                        silt
2                         mud
3         volcaniclastic rock
4                ore minerals

129 sulphide-rich material 130 argillite 131 graphite 132 high-Mg basalt 133 shale Name: strata, Length: 134, dtype: object >>> b.set_strata (add_electrical_properties= True, reset_strata= True) >>> b.strata_ Out[123]: 0 phyllite 1 syenite 2 laterite 3 saprolite 4 psammopelite

129 chert 130 granulite 131 pyroclastic rock 132 lamprophyre 133 ignimbrite Name: strata, Length: 134, dtype: object

b.strata_electrical_properties_ Out[124]: 0 0.0 1 0.0 2 0.0 3 330.6 4 0.0

129 0.0 130 0.0 131 0.0 132 0.0 133 0.0 Name: strata_electrical_properties, Length: 134, dtype: float64 >>>

set_thickness(h0=1, shuffle=True, dirichlet_dist=Ellipsis, reset_layer_thickness=Ellipsis, reset_depth=Ellipsis, **kws)[source]#

Generate a random layer thickness from borehole refering to the depth.

To fetch the thickness, use attribute layer_thickness_. Use reset_layer_thickness to set new strata thicknesses.

Parameters:

  • h0 (int, default='1m') – Thickness of the first layer.

  • shuffle (bool, default=True) – Shuffle the random generated thicknesses.

  • dirichlet_dis (bool, default=False) – Draw samples from the Dirichlet distribution. A Dirichlet-distributed random variable can be seen as a multivariate generalization of a Beta distribution. The Dirichlet distribution is a conjugate prior of a multinomial distribution in Bayesian inference.

  • reset_layer_thickness (bool, default=False,) – Set new layer thicknesses to the existing stratum. If True and the data included layer thicknesses, the latter should be dropped in replacement to the new ones. However, if False, no action is performed and both are kept in the data.

  • reset_depth (bool, default=False) – Note that thickness generating works with the depth. So, if the reset_depth is set to True, a new depth is computed and drop the former ones. From this new depth, the thickness generating is creating.

  • random_state (int, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional) – If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given.

  • unit (str, default='m') – The reference unit for generated layer thicknesses. Default is meters

Returns:

self – Instanced object for chaining method.

Return type:

Instanced object

Examples

>>> import watex as wx
>>> from watex.geology import DSBorehole
>>> hdata= wx.fetch_data ('hlogs').frame
>>> b = DSBorehole (hole='H502').fit(hdata)
>>> b.set_thickness ()
>>> b.layer_thickness_
0      5.410380
1      2.068812
2      0.398028
3      6.352873
4      6.395714

176 3.396871 177 0.012463 178 7.124004 179 7.038323 180 3.439711 Name: layer_thickness, Length: 181, dtype: float64 >>> b.set_thickness (dirichlet_dist=True, reset_layer_thickness=True

).layer_thickness_

Out[89]: 0 0.681640 1 1.986043 2 6.413090 3 5.305284 4 0.000144

176 4.119242 177 12.161252 178 1.809102 179 0.408810 180 4.281848 Name: layer_thickness, Length: 181, dtype: float64

class watex.geology.drilling.DSBoreholes(area=None, holeid=None, lat=None, lon=None, projection='ll', utm_zone=None, datum='WGS84', epsg=None, encoding='utf-8', interp_coords=False, reference_ellipsoid=23, verbose=False)[source]#

Bases: object

Class deals with many boreholes dataset.

DSBoreholes works with the data set composed of multiple borehole data. The data columns are the all attributes of the object and any non-alphateic character is by _. For instance, a column name layer thickness should have an attribute named layer_thickness. Each borehole (row) data become its own object which encompasses all columns as attributes. To have full control of how data must be retrieved, holeid parameter must be set. For instance, to retrieve the borehole with ID equals to bx02, after fitting the class with appropriate parameters, attibute hole depth ( if exist in the data) can be retrieved as self.hole.bx02.hole_depth.

By default if the projection is given as latitude/longitude

Parameters:

  • area (str) – Name of area where the data collection is made.

  • holeid (str, optional) –

    The name of column of the boreholes collections ID. Note that if

    given, it must exist in the borehole datasets. Note that if hole ID is not specified, each borehole can be fetched from a attribute hole count from 0 to n_samples. For instance, the borehole number 12 can be collected using:

    >>> b = DSBoreholes ().fit(<borehole_data>)
>>> b.hole.hole11

    where 12 is the 12em position as Python index starts with 0. However when holeid is specified, the hole attribute is replaced by each value of the hole_id column as:

    >>> borehole_data['hole_id'][:3]
0    B0092
1    B0093
2    B0094
Name: hole_id, dtype: object
>>> b.hole.B0092
>>> b.hole.B0094

    where B0092 or B0094 are the borehole in the columns hole_id. Note that hole_id can be any other names at least it is explicitly specified as a argument of the ``holeid` parameter.

  • lon (ArrayLike 1d /str , optional) – One dimensional arrays. xlon can be consider as the abscissa of the landmark and ylat as ordinates array. If xlon or ylat is passed as string argument, data must be passed as fit_params keyword arguments and the name of xlon and y must be a column name of the data. By default xlon and ylat are considered as longitude and latitude when dms or ll coordinate system is passed.

  • lat (ArrayLike 1d /str , optional) – One dimensional arrays. xlon can be consider as the abscissa of the landmark and ylat as ordinates array. If xlon or ylat is passed as string argument, data must be passed as fit_params keyword arguments and the name of xlon and y must be a column name of the data. By default xlon and ylat are considered as longitude and latitude when dms or ll coordinate system is passed.

  • utm_zone (Optional, string) – zone number and ‘S’ or ‘N’ e.g. ‘55S’. Default to the centre point of coordinates points in the survey area. It should be a string (##N or ##S) in the form of number and North or South hemisphere, 10S or 03N

  • projection (str, ['utm'|'dms'|'ll']) – The coordinate system in which the data points for the profile is collected. If not given, the auto-detection will be triggered and find the suitable coordinate system. However, it is recommended to provide it for consistency. Note that if x and y are composed of value less than 180 degrees for longitude and 90 degrees for latitude, it should be considered as longitude-latitude (ll) coordinates system. If x and y are degree-minutes-second (dms or dd:mm:ss) data, they must be specify as coordinate system in order to accept the non-numerical data before transforming to ll. If data is passed to the fit() method and dms is not specify, x and y values should be discarded.

  • datum (string, default = 'WGS84') – well known datum ex. WGS84, NAD27, NAD83, etc.

  • epsg (Optional, int) –

    epsg number defining projection (

    see http://spatialreference.org/ref/ for moreinfo)

    Overrides utm_zone if both are provided.

  • encoding (str, default ='utf8') – Default encoding for parsing data. Can also be [‘utf-16-be’] for reading bytes characters.

  • interp_coords (bool, default=False) – Interpolate position coordinates.

  • reference_ellipsoid (int, default=23) – reference ellipsoids is derived from Peter H. Dana’s website- http://www.utexas.edu/depts/grg/gcraft/notes/datum/elist.html Department of Geography, University of Texas at Austin Internet: pdana@mail.utexas.edu . Default is 23 constrained to WGS84.

  • verbose (int, default=0) – Output messages.

lon_, lat_

longitude/latitude of coordinates arrays.

Type:

Arraylike,

`hole.<holeid>.<data_column>`

Each borehole, commonly which ID correspond to each row. Each row can be fetched as ‘holeID’. If holeid is nt specified, the string literal hole+index of data composed the borehole object.

Type:

Boxspace

Notes

When data is supplied and lon and lat are given by their names existing in the dataframe columns, by default, the non-numerical data are removed. However, if y and x are given in DD:MM:SS in the dataframe, the coordinate system must explicitly set to ``dms` to keep the non-numerical values in the data.

Examples

>>> import watex as wx
>>> from watex.geology import DSBoreholes
>>> bs_data = wx.fetch_data ('nlogs', key='hydro', samples=12 ,
                             as_frame=True )
>>> bs=DSBoreholes ().fit(bs_data)
>>> bs.holeid
Out[61]: 'hole'
>>> # when the default object hole is set as:
>>> bs.hole # outputs a Boxspace object each borehole can be retrieved
>>> # as hole object count from 0. to number or rows -1. Here is an
>>> example of fetching the hole 11.
>>> bs.hole.hole10
Out[62]:
{'hole_id': 'B0103',
 'uniform_number': 1.1343e+16,
 'original_number': 'Guangzhou multi-element urban geological survey drilling 19ZXXSW11',
 'lon': '113:43:00.99',
 'lat': '23:16:17.23',
 'longitude': 113.71694166666668,
 'latitude': 23.271452777777775,
 'east': 2577207.0,
 'north': 19778060.0,
 'easting': 2577207.276,
 'northing': 19778177.29,
 'coordinate_system': 'Xian 90',
 'elevation': 22.0,
 'final_hole_depth': 60.1,
 'quaternary_thickness': 45.8,
 'aquifer_thickness': 18.1,
 'top_section_depth': 42.0,
 'bottom_section_depth': 60.1,
 'groundwater_type': 'igneous rock fissure water',
 'static_water_level': 2.36,
 'drawdown': 28.84,
 'water_inflow': 0.08,
 'unit_water_inflow': 0.003,
 'filter_pipe_diameter': 0.16,
 'water_inflow_in_m3_d': 2.94}
>>> # when we specified the hole ID to the column that compose the ID like:
>>> bs=DSBoreholes (holeid ='hole_id').fit(bs_data)
>>> bs.hole.B0103
Out[63]:
{'hole_id': 'B0103',
 'uniform_number': 1.1343e+16,
 'original_number': 'Guangzhou multi-element urban geological survey drilling 19ZXXSW11',
 'lon': '113:43:00.99',
 'lat': '23:16:17.23',
 'longitude': 113.71694166666668,
 'latitude': 23.271452777777775,
 'east': 2577207.0,
 'north': 19778060.0,
 'easting': 2577207.276,
 'northing': 19778177.29,
 'coordinate_system': 'Xian 90',
 'elevation': 22.0,
 'final_hole_depth': 60.1,
 'quaternary_thickness': 45.8,
 'aquifer_thickness': 18.1,
 'top_section_depth': 42.0,
 'bottom_section_depth': 60.1,
 'groundwater_type': 'igneous rock fissure water',
 'static_water_level': 2.36,
 'drawdown': 28.84,
 'water_inflow': 0.08,
 'unit_water_inflow': 0.003,
 'filter_pipe_diameter': 0.16,
 'water_inflow_in_m3_d': 2.94}
>>> # each columns can be fetched as
>>> bs.quaternary_thickness
Out[64]:
0     40.5
1     12.3
2     25.5
3     40.0
4     35.0
5     47.0
6     34.0
7     40.4
8     15.1
9     17.2
10    45.8
11    47.0
Name: quaternary_thickness, dtype: float64
fit(data, **fit_params)[source]#

Fit Hole data set and populate attributes.

Parameters:

  • data (Path-like Object or DataFrame) – Hole data.

  • fit_params (dict,) – Keyword arguments passed to watex.to_numeric_dtypes() to sanitize the data.

Returns:

self – Instanced object for chaining methods.

Return type:

DSBoreholes

property inspect#

Inspect object whether is fitted or not

set_coordinates(reflong, reflat, step='5m', todms=False, r=45, **kws)[source]#

Generate longitude and latitude coordinates for boreholes.

It assumes boreholes are aligned along the same axis.

Parameters:

  • reflong (float or string or list of [start, stop]) – Reference longitude in degree decimal or in DD:MM:SS for the first site considered as the origin of the landmark.

  • reflat (float or string or list of [start, stop]) – Reference latitude in degree decimal or in DD:MM:SS for the reference site considered as the landmark origin. If value is given in a list, it can containt the start point and the stop point.

  • step (float or str) – Offset or the distance of seperation between different sites in meters. If the value is given as string type, except the km, it should be considered as a m value. Only meters and kilometers are accepables.

  • r (float or int) – The rotate angle in degrees. Rotate the angle features toward the direction of the projection profile. Default value use the bearing() value in degrees.

  • todms (bool, Default=False) – Reconvert the longitude/latitude degree decimal values into the DD:MM:SS.

  • kws (dict,) – Additional keywords of makeCoords().

Returns:

self – Instanced object for method chaining.

Return type:

Instanced object

Examples

>>> bs_data = wx.fetch_data ('nlogs', key='ns', samples=7,
                             as_frame=True )
>>> bs=DSBoreholes ().fit(bs_data)
>>> bs.set_coordinates(reflong= 113.4, reflat=22.56, step ='10m')
>>> bs.set_coordinates(reflong= 113.4, reflat=22.56, step ='10m')
>>> bs.lat_
Out[71]:
array([22.56      , 22.56009391, 22.56018782, 22.56028174, 22.56037565,
       22.56046956, 22.56056347])
>>> bs.lon_
Out[72]:
array([113.4       , 113.40007436, 113.40014871, 113.40022307,
       113.40029742, 113.40037178, 113.40044614])
class watex.geology.drilling.DSDrill(area=None, holeid=None, dname=None, projection='ll', utm_zone=None, datum='WGS84', encoding='utf-8', epsg=None, reference_ellipsoid=23, property_names=None, verbose=0)[source]#

Bases: object

Drill data set class.

DSDrill reads, constructs the well/hole (drillhole:DH), geology and geochemistry samples into a data set for transforming geophysics, geology, GIS, and geochemistry data collecting in a survey area into a three dimensional representation with `Oasis montaj`_. Deal with drillhole menu of Oasis Montaj software. Build data and contruct three dimensional data with `Oasis montaj`_ in Seequent.

Parameters:
area: str

Area where the drilling operation is performed.

holeid: str,

the column name in the data where the well/hole ID is stored.

dname: str, optional

Depth column name. If depth is specify an attribute depth_ should be created. Depth specification is usefull for log plotting of machine training.

utm_zone: Optional, string

zone number and ‘S’ or ‘N’ e.g. ‘55S’. Default to the centre point of coordinates points in the survey area. It should be a string (##N or ##S) in the form of number and North or South hemisphere, 10S or 03N

projection: str, [‘utm’|’dms’|’ll’]

The coordinate system in which the data points for the profile is collected. If not given, the auto-detection will be triggered and find the suitable coordinate system. However, it is recommended to provide it for consistency. Note that if x and y are composed of value less than 180 degrees for longitude and 90 degrees for latitude, it should be considered as longitude-latitude (ll) coordinates system. If x and y are degree-minutes-second (dms or dd:mm:ss) data, they must be specify as coordinate system in order to accept the non-numerical data before transforming to ll. If data is passed to the fit() method and dms is not specify, x and y values should be discarded.

datum: string, default = ‘WGS84’

well known datum ex. WGS84, NAD27, NAD83, etc.

encoding: str, default =’utf8’

Default encoding for parsing data. Can also be [‘utf-16-be’] for reading bytes characters.

epsg: Optional, int
epsg number defining projection (

see http://spatialreference.org/ref/ for moreinfo)

Overrides utm_zone if both are provided.

reference_ellipsoid: int, default=23

reference ellipsoids is derived from Peter H. Dana’s website- http://www.utexas.edu/depts/grg/gcraft/notes/datum/elist.html Department of Geography, University of Texas at Austin Internet: pdana@mail.utexas.edu . Default is 23 constrained to WGS84.

propety_names: dict,

Data column can not fit the Drilling property columns. In that case. Mapping the property names is usefull to specify the columns in the original data that fits the DSDrill property codes. For instance:

   property_names ={"hole name": 'DH_Hole',
                    "easting": 'DH_East',
                    "northing": 'DH_Northing'
                    }

where "hole name", "easting" and "northing" are the column names in
the former data set. These names should be replaced by the "DH_Hole",
"DH_East" and "DH_North" respectively. The mapping can be used  to
specify any other property names. Note that the valuable properties
are:

- "DH_Hole": Well/hole ID
- "DH_East": Easting coordinates of the well/hole
- "DH_North":  Northing coordinates of the well/hole.
- "Mask": Any comment about the well/hole ID
- "DH_RH": Radius value of the well/hole ID
- 'DH_From': Top(roof) of any stratum/sample or rock in the well/hole.
- "DH_To" : Wall( bottom) of any stratum/sample or rock in the well/hole.
- "Rock" : Rock or strata/layer in the well/hole.
- "DH_Azimuth": Azimuth value in the well/hole.
- 'DH_Top': Surface-level of the well/hole compared to the level of sea.
- 'DH_Bottom': Maximum depth of the well/hole.
- 'DH_PlanDepth': Any section performed in the well/hole can be inputed.
- 'DH_Decr': Description of the well/hole.
- 'Sample': Sample collected in the well/hole at different depth.
- 'DH_Dip': Dip of the well/hole.
- 'Elevation': Elevation of the well/hole.
- 'DH_RL': Level of piezometric value if exists in the well/hole.
verbose: int, default=0

Output messages.

Attributes:
depth_: Series

Depth array if dname is specified.

data_: Pandas DataFrame

Sanitized dataframe.

collar_:Pandas DataFrame

Collar data composed of each borehole description.

geology_: Pandas DataFrame

Geology data that compose each geological rocks

samples_: Pandas DataFrame

Each Geochemistry samples data compose each sample collected in the survey area.

build_collar(*, return_collar=False, compute_azimuth=Ellipsis, utm_zone=Ellipsis)[source]#

Build manually the collar data of collected drilling area.

Collar data is composed of well/hole description and usefull informations.

build_geology() collect the rocks or strata names collected during the drilling operations or well contructions. The collection of the information will build a geology dataset which can be used to Oasis Montaj software modeling. Below is an example of geology data set construction. This is some explanation of the prompt:

  • well/hole ID: Is the name or ID of the rocks or strata collected in the area

  • coordinates (xlon, xlat): is the geographical coordinates where the drilling operation is performed. Expect projection is ll. To enter the UTM coordinates, set the projection in the building object to utm like:

    >>> DSDrill (projection='utm').build_geology (return_geology =True )

  • radius (m): Is the radius of the hole/well

  • surface-level: The level of the well/hole compared to the level of the sea. By default it is set to null as the level of the sea.

  • depth: depth of the well/hole in meters.

  • dip: dip in degree of the well/hole.

  • elevation: elevation value of the well/hole in meters. Default is no topography i.e. equal null.

  • azimuth: azimuth value in degrees. If not given explicitely, it can be calculated using the utm coordinates provided that the parameter compute_azimuth is set to True and utm_zone is also provided. In this case, we assume that all well/hole collected belongs to the same area. Furthermore, azimuth calculation will cancel if one of the above condition is not met and if only a single borehole is supplied.

  • plan-depth: Is a litteral string to give at which stage a section in a borehole is performed. Note that this is optional parameter and can be skipped. It has no more influence about the collar construction.

  • description: Give a short description of well/hole mostly about the drilling settlment.

  • mask/comments: Is any comments aboud the well/hole.

Parameters:

  • return_collar (bool, default=False,) – Return the collar dataset rather than object (False) after entering the appropriate argumments. Note that even True, the collar data set can be retrieved via the attribute collar_.

  • compute_azimuth (bool, default=False) – Recompute the azimuth using the UTM coordinates. Note that projection need to be turned in ‘utm’ as well as the ‘utm_zone’ needs also to be supplied.

  • utm_zone (str,) – zone number and ‘S’ or ‘N’ e.g. ‘55S’. Default to the centre point of coordinates points in the survey area. It should be a string (##N or ##S)in the form of number and North or South hemisphere, 10S or 03N. if utm_zone is already set, it is not need to reset again. Resetting new utm_zone will erase the value of the former attribute. However for azimuth calculation, utm zone cannot be None otherwise the process is aborted.

Returns:

self, geology dataset – Return object when return_geology=False and DataFrame otherwise.

Return type:

DSDrill or pd.DataFrame.

Examples

>>> col_data=DSDrill (projection ='utm').build_collar (return_collar =True )
Enter the well/hole ID:DXT03
Enter DXT03 coordinates (xlon, ylat):297856 352145
Enter DXT03 radius in meters [Optional]:.25
Enter DXT03 surface-level <top> in meters[0.]:
Enter DXT03 depth <bottom> in meters[700.]:120
Enter DXT03 dip in degrees [-90]:
Enter DXT03 elevation in meters [0.]:
Enter DXT03 azimuth [Optional]:
Enter DXT03 plan-depth [Optional]:wandx01
Enter DXT03 description [Optional]:hole-test
Enter valuable comments about DXT03 [Optional]:RAS
Tap "exit|0" to terminate or "Enter" to continue:
Enter the well/hole ID:toxg
Enter TOXG coordinates (xlon, ylat):125869 235645
Enter TOXG radius in meters [Optional]:2.3
Enter TOXG surface-level <top> in meters[0.]:-10
Enter TOXG depth <bottom> in meters[700.]:135
Enter TOXG dip in degrees [-90]:-70
Enter TOXG elevation in meters [0.]:3
Enter TOXG azimuth [Optional]:2.3
Enter TOXG plan-depth [Optional]:wxzu
Enter TOXG description [Optional]:None
Enter valuable comments about TOXG [Optional]:RAS
Tap "exit|0" to terminate or "Enter" to continue:0
>>> col_data
Out[3]:
  DH_Hole   DH_East  DH_North  DH_RH  ...  DH_Azimuth  DH_PlanDepth    DH_Decr  Mask
0   DXT03  297856.0  352145.0   0.25  ...         NaN       wandx01  hole-test   RAS
1    TOXG  125869.0  235645.0   2.30  ...         2.3          wxzu       None   RAS
build_geology(*, return_geology=False, hole_elevation=0.0)[source]#

Build manually the geology data of collected drilling area.

build_geology() collect the rocks or strata names collected during the drilling operations or well contructions. The collection of the information will build a geology dataset which can be used to Oasis Montaj software modeling. Below is an example of geology data set construction. This is some explanation of the prompt:

  • well/hole ID: Is the name or ID of the rocks or strata collected in the area

  • coordinates (xlon, xlat): is the geographical coordinates where the drilling operation is performed. Expect projection is ll. To enter the UTM coordinates, set the projection in the building object to utm like:

    >>> DSDrill (projection='utm').build_geology (return_geology =True )

  • radius (m): Is the radius of the hole/well

  • depth: depth of the well/hole in meters.

  • layer thickness: The thickness of each strata in the whole. Note that when many thickness are supplied, it may correspond to each layer i.e. the number of thicknesses must equal to the number of layers. If not the case, NA should be used to indicate the missing layer/rock name in the geology dataset.

  • mask/comments: Is any comments aboud the well/hole.

Parameters:

  • return_geology (bool, default=False,) – Return the geology dataset rather than object (False) after entering the appropriate argumments. Note that even True, the geology data set can be retrieved via the attribute geology_.

  • hole_elevation (float, default=0,) – The elevation or the level of surface of the well/hole compared to the level of sea. Note that elevation must be negative value on the top of the air for layer/strat calculation.

Returns:

self, geology dataset – Return object when return_geology=False and DataFrame otherwise.

Return type:

DSDrill or pd.DataFrame.

Examples

>>> DSDrill ().build_geology (return_geology =True )
Enter the well/hole ID:GEOK4
Enter GEOK4 coordinates (xlon, ylat):10 15
Enter GEOK4 radius in meters [Optional]:12.2
Enter the GEOK4 depth in meters: 75
Enter each stratum thickness of GEOK4 [top-->bottom] in meters:10 20 11
Enter the layer/rock names of GEOK4 [top-->bottom]:granite gneiss
Enter valuable comments about GEOK4 [Optional]:building test
Tap exit|0 to terminate or enter to continue:0
Out[17]:
  DH_Hole       DH_East  DH_North  ...  DH_To     Rock           Mask
0   GEOK4  1.105412e+06  500000.0  ...   10.0  granite  building test
1   GEOK4  1.105412e+06  500000.0  ...   30.0   gneiss  building test
2   GEOK4  1.105412e+06  500000.0  ...   41.0       NA  building test
3   GEOK4  1.105412e+06  500000.0  ...   75.0       NA  building test
build_geosamples(*, return_samples=False)[source]#

Build manually the geochemistry samples of area.

build_geosamples() collect the geochemistry samples in the survey area with their geographical coordinates locations. The name as well as the sample thickness and the radium of the holes can be from the prompt. The collection of the information will build a geosamples dataset which can be used to Oasis Montaj software modeling. Below is an examples of Outputted. This is some indication of the prompts:

  • sample ID: Is the name or ID of the samples collected in the area

  • coordinates (xlon, xlat): is the geographical coordinates where the sampling is performed.

  • The radius (m): Is the radius of the hole did for collecting the sample.

  • samples thickness: Ask the thickness of the samples in the whole. Note that when many thickness are supplied, it means the same sample is collected at different depth. There are two kind of data to supply:

    • t-value: Compose only with the layer thickness values. For instance t= "10 20 7 58" indicates four samples with thicknesses equals to 10, 20, 7 and 58 ( meters) respectively.

    • tb-range: compose only with thickness range at each depth. For instance t= "0-10 10-30 40-47 101-159". Note the character used to separate thickness range is '-'. Here, the top(roof) and bottom(wall) of the sample are 0 (top) and 10 (bottom), 10 and 30, 40 and 47 , and 101 and 159 for the same sample.

    • Note that any mixed types is not acceptable and willraises error. To verify whether the expected samples values is acceptable or not, use the following watex.utils.geotools.get_thick_from_range() or watex.utils.geotools.get_thick_from_values() functions.

  • sample name: Is the name of samples collected refereing to the different depth. In principle, the number of samples thickness must equals to the number of samples. If not the case, NA should be used to indicate the missing samples in the geosamples dataset.

  • mask/comments: Is any comments aboud the sample

Parameters:

return_samples (bool, default=False,) – Return the samples dataset rather than object (False) after prompted the appropriate argumments. Note that even True, the geosample data set can be retrieved via the attribute sample_.

Returns:

self, geosample dataset – Return object when return_samples=False and DataFrame otherwise.

Return type:

DSDrill or pd.DataFrame.

Examples

>>> dr = DSDrill ().build_geosamples (return_samples =True )
Enter the sample ID:sx02
Enter SX02 coordinates (xlat, ylon):12 15
Enter SX02 radius in meters [Optional]:2.5
Enter the sample thickness of SX02 in meters:15 15 48 23
Enter the sample names of SX02:pup sup op
Enter valuable comments about SX02 [Optional]:t-value
Tap "exit|0" to terminate or "Enter" to continue:
Enter the sample ID:sx05
Enter SX05 coordinates (xlat, ylon):15 56
Enter SX05 radius in meters [Optional]:2.3
Enter the sample thickness of SX05 in meters:12-17 26-36 40-57
Enter the sample names of SX05:benz op
Enter valuable comments about SX05 [Optional]:t-range1
Tap "exit|0" to terminate or "Enter" to continue:
Enter the sample ID:sx07
Enter SX07 coordinates (xlat, ylon):10 15
Enter SX07 radius in meters [Optional]:1.25
Enter the sample thickness of SX07 in meters:56-76
Enter the sample names of SX07:gru
Enter valuable comments about SX07 [Optional]:t-range2
Tap "exit|0" to terminate or "Enter" to continue:0
>>> dr
Out[8]:
  DH_Hole       DH_East       DH_North  DH_RH  DH_From  DH_To Sample      Mask
0    SX02  1.326554e+06  500000.000000   2.50      0.0   15.0    pup   t-value
1    SX02  1.326554e+06  500000.000000   2.50     15.0   30.0    sup   t-value
2    SX02  1.326554e+06  500000.000000   2.50     30.0   78.0     op   t-value
3    SX02  1.326554e+06  500000.000000   2.50     78.0  101.0     NA   t-value
4    SX05  1.658569e+06  392487.772324   2.30     12.0   17.0   benz  t-range1
5    SX05  1.658569e+06  392487.772324   2.30     26.0   36.0     op  t-range1
6    SX05  1.658569e+06  392487.772324   2.30     40.0   57.0     NA  t-range1
7    SX07  1.105412e+06  500000.000000   1.25     56.0   76.0    gru  t-range2
code = ('DH_Hole', 'DH_East', 'DH_North', 'Mask', 'DH_RH', 'DH_From', 'DH_To', 'Rock', 'DH_Azimuth', 'DH_Top', 'DH_Bottom', 'DH_PlanDepth', 'DH_Decr', 'Sample', 'DH_Dip', 'Elevation', 'DH_RL')#
fit(data=None, **fit_params)[source]#

Fit drill data to build

Parameters:

  • data (pd.DataFrame or Path-like object.) – Data containing the drilling informations.

  • fit_params (dict,) – Keyword arguments passed to watex.utils.to_numeric_dtypes() for data management.

Returns:

self – Instanced object for chaining method.

Return type:

Instanced object

Examples

>>> from watex.geology.drilling import Drill
>>> csv_data ='data/drill/nbleDH.csv'
>>> xlsx_data= 'data/drill/nbleDH.xlsx'
>>> dr0 = Drill().fit(csv_data)
>>> dr0.data_.head(2)
  DH_Hole__ID_      DH_East     DH_North  ... DH_Top    DH_Bottom DH_PlanDepth
0          S01  477205.6935  2830978.218  ...      0       968.83          NaN
1          S02  477261.7258  2830944.879  ...      0  974.8945704          NaN
>>> dr1 = Drill().fit(xlsx_data)
>>> dr1.data_.head(2)
    DH_Hole (ID)      DH_East  ...  sample03     sample04
  0          S01  477205.6935  ...       pup  Boudin Axis
  1          S02  477261.7258  ...       pup          pzs
get_collar(data=None, reset_collar=False, **kws)[source]#

Get or set the collar data.

Parameters:

  • data (str, pd.Dataframe) – Path-like object of dataframe containing the collar data

  • reset_collar (bool, defult=False,) – If collar is provided, resetting the collar data will replace the previous collar data in the original data.

  • kws (dict,) – Keyword arguments passed to watex.utils.coreutils._is_readable()

Returns:

self – Object instanced for method chaining

Return type:

Instanced object

Examples

>>> from watex.geology.drilling import DSDrill
>>> xlsx_data= 'data/drill/nbleDH.xlsx'
>>> dr = DSDrill().fit(xlsx_data)
>>> dr2 = dr.get_collar (dr.collar_, reset_collar= True )
>>> dr2.collar_
  DH_Hole (ID)      DH_East     DH_North  ...  DH_PlanDepth  DH_Decr  Mask
0          S01  477205.6935  2830978.218  ...           NaN      NaN    NaN
1          S02  477261.7258  2830944.879  ...           NaN      NaN    NaN

>>> dr.holeid # id hole is autodetected if not given
'DH_Hole (ID)'
>>> # retreive the holeID S01
>>> dr.collar.S01
{'DH_Hole (ID)': 'S01',
 'DH_East': 477205.6935,
 'DH_North': 2830978.218,
 'DH_Dip': -90.0,
 'Elevation ': 0.0,
 'DH_Azimuth': 0.0,
 'DH_Top': 0.0,
 'DH_Bottom': 968.83,
 'DH_PlanDepth': nan,
 'DH_Decr': nan,
 'Mask ': nan}
get_geology(data=None, reset_geology=False, **kws)[source]#

Get the geological informations that composed each drilling.

Parameters:

  • data (str, dataframe) – Path like object composed of layer thickness and geology

  • reset_geology (bool, default=False) – If True it assumes that the data

  • kws (dict,) – Keyword arguments passed to watex.utils.coreutils._is_readable()

Returns:

self – Object instanced for method chaining

Return type:

Instanced object

Examples

>>> from watex.geology.drilling import DSDrill
>>> xlsx_data= 'data/drill/nbleDH.xlsx'
>>> dr = DSDrill().fit(xlsx_data)
>>> dr.get_geology (dr.geology_, reset_geology=True ).geology_
  DH_Hole     Thick01  ...                    Rock03  Rock04
0     S01    0.200000  ...  carbonate iron formation    ROCK
1     S02  174.429396  ...                       GRT    ROCK
>>> dr.holeid # id hole is autodetected if not given
Out[62]: 'DH_Hole'
>>> # retreive the hole ID of S01  drilling.
>>> dr.geology.S01
{'DH_Hole': 'S01',
 'Thick01': 0.2,
 'Thick02': 98.62776918,
 'Thick03': 204.7500461,
 'Thick04': 420.0266651,
 'Rock01': 'clast supported breccia',
 'Rock02': 'sulphide-rich material',
 'Rock03': 'carbonate iron formation',
 'Rock04': 'ROCK'}
get_geosamples(data=None, reset_samples=False, **kws)[source]#

Get or set the geochemistry samples data.

Parameters:

  • data (str, pd.Dataframe) – Path-like object of dataframe containing the geochemistry sample data

  • reset_geosamples (bool, default=False,) – If geochemistry samples is provided, resetting the samples data will replace the previous samples data in the original data.

  • kws (dict,) – Keyword arguments passed to watex.utils.coreutils._is_readable()

Returns:

self – Object instanced for method chaining

Return type:

Instanced object

Examples

>>> from watex.geology.drilling import DSDrill
>>> xlsx_data= 'data/drill/nbleDH.xlsx'
>>> dr = DSDrill().fit(xlsx_data)
>>> dr.get_geosamples (dr.samples_, reset_samples= True ).samples_
  DH_Hole  Thick01     Thick02  ...             sample02  sample03     sample04
0     S01     10.0   98.627769  ...                  prt       pup  Boudin Axis
1     S02     17.4  313.904388  ...  Banding/gneissosity       pup          pzs
>>> dr.holeid # id hole is autodetected if not given
'DH_Hole'
>>> # retreive the holeID geosamples S02
>>> dr.samples.S02
{'DH_Hole': 'S02',
 'Thick01': 17.4,
 'Thick02': 313.9043882,
 'Thick03': 400.12,
 'Thick04': 515.3,
 'sample01': 'pup',
 'sample02': 'Banding/gneissosity',
 'sample03': 'pup',
 'sample04': 'pzs'}
property inspect#

Inspect object whether is fitted or not

class watex.geology.drilling.Drill(well_filename=None, auto=True, **kwargs)[source]#

Bases: Geology

This class is focus on well logs . How to generate well Log for Oasis:

Parameters:

  • **well_filename** (string ,) – The well filename. 02 options is set : 1rst option is to build well data manually and the program will generate a report. 2nd option is to send to the program a typical file type to be parsed . the programm parses only the typical well datafile. If None , the program will redirect to build mannually option .

  • **auto** (bool) –

    option to automatically well data . set to True

    if you want to build manually a well data .

    default is False

  • ========================================= (==================== ==========) –

  • Description (Key Words/Attributes Type) –

  • ========================================= –

  • S. (utm_zone str utm WGS84 zone. should be N or) – default is 49N .

  • provided. (compute_azimuth bool if no azimuth is) – set to True to letprogram to compute azimuth .*Default* is False.

  • 90 (Drill_dip float The dip of drill hole.*default* is) –

  • drill (Drill_buttom float The average bottom of) – can be filled during the well buiding . default is None

:paramcan be filled during the well

buiding . default is None

Parameters:

  • data. (mask int the mask of DrillHole(DH)) – *Default * is 1.

  • ========================================= (==================== ==========) –

  • ======================================================= (==================) –

  • Description (Methods) –

  • ======================================================= –

  • dataframe (_collar build _collar data return collar log) – format dhGeology build DH log geology return geology log dataframe.

  • sample (dhSample build DH Geochemistry-Strutural) – log dataframe

  • Sample (*return*) – log dataframe

  • Elevation (dhSurveyElevAz build DH Elevation & Azimuth logs.*return *) – & Azimuth dataframes

  • log (writeDHDATA output) – Oasis Montaj

  • ======================================================= –

:param : :type : Example: :param >>> from watex.geoloy.drilling import Drill: :param >>> parser_file =’nbleDH.csv’: :param >>> drill_obj=Drill(well_filename=’data/drill/drill_example_files’): :param >>> scollar=drill._collar(DH_Top=None): :param >>> sgeo=drill.dhGeology(): :param >>> ssam=drill.dhSample(): :param >>> selevaz=drill.dhSurveyElevAz( add_elevation=None: :param : :param … add_azimuth=None): :param >>> swrite=drill.writeDHData(data2write =”*”: :param savepath =None):

dhGeology(dh_geomask=None)[source]#

Method to build geology drillhole log. The name of input rock must feell exaction accordinag to a convention AGSO file . If not sure for the name of rock and Description and label. You may consult the geocode folder before building the well_filename. If the entirely rock name is given , program will search on the AGSO file the corresponding Label and code . If the rock name is founc then it will take its CODE else it will generate exception.

Parameters:

dh_geomask (*) – geology mask. send mask value can take exactly the np.ndarray(num_of_geology set ,). The better way to set geology maskis to fill on the wellfilename. if not , programm will take the general mask value. The default is None.

Returns:

geology drillhole log.

Return type:

pd.DataFrame

dhSample(path_to_agso_codefile=None, dh_sampmask=None)[source]#

Method to build Sample log. This method focuses on the sample obtained during the DH trip.it may georeferenced as the well_filename needed. A main thing is to set the AGSO_STCODES file. AGSO_STCODES is the conventional code of structurals sample. If you have an own AGSO_STCODES , you may provide the path * kwargs=path_to_ags_codefile * . the program will read and generate logs according to the DESCRIPTION and STCODES figured. if None, the program will take it STCODES and set the samplelogs. When you set the Sample code aor sample name , make sur that the name match the same name on STCODES. If not , program will raises an error.

Parameters:

  • path_to_agso_codefile (*) – path to conventional AGSO_STRUCTURAL CODES. The default is None.

  • dh_sampmask (*) – Structural mask. The default is None.

Returns:

Sample DH log.

Return type:

pd.DataFrame

dhSurveyElevAz(add_elevation=None, add_azimuth=None, **kwargs)[source]#

Method to build Elevation & Azimuth DH logs. if add_elevation and . add_azimuth are set . The programm will ignore the computated azimuth, and it will replace to the new azimuth provided . all elevation will be ignore and set by the new elevation . *kwargs arguments {add_elevation , add-azimuth } must match the same size like the number of Drillholes . Each one must be on ndarray(num_of_holes, 1).

Parameters:

  • add_elevation (*) – elevation data (num_of_holes, 1) The default is None.

  • add_azimuth (*) – azimuth data (num_of_holes,1). The default is None.

  • DH_RL (*) – if not provided , it’s set to 0. means No topography is added’.

Returns:

  • pd.Dataframe – Elevation DH log .

  • pd.DataFrame – Azimuth DH log.

writeDHData(data2write=None, **kwargs)[source]#

Method to write allDH logs. It depends to the users to sort which data want to export and which format. the program support only two format (.xlsx and .csv) if one is set , it will ouptput the convenience format. Users can give a list of the name of log he want to export. Program is dynamic and flexible. It tolerates quite symbols number to

extract data logs.

Parameters:

  • data2write (*) – the search key. The default is None.

  • datafn (*) – savepath to exported file Default is current work directory.

  • write_index_on_sheet (*) – choice to write the sheet with pandas.Dataframe index.

  • writeType (*) – file type . its may .csv or *.xlsx . *Default is *.xlsx

  • add_header (*) – add head on exported sheet. set False to mask heads. Default is True.

  • csv_separateType (*) – Indicated for csv exported files , the type of comma delimited . defaut is ‘,’.