Import Drillholes Data¶

In [1]:

import geolime as geo

import geolime as geo

From Collar, Assay & Survey Files¶

Case 1 : With one Interval File → Assays only¶

In [3]:

collar = geo.datasets.load("rocklea_dome/collar.csv")
assay = geo.datasets.load("rocklea_dome/assay.csv")
survey = geo.datasets.load("rocklea_dome/survey.csv")
hyperspec = geo.datasets.load("rocklea_dome/hyperspec.csv")

collar = geo.datasets.load("rocklea_dome/collar.csv") assay = geo.datasets.load("rocklea_dome/assay.csv") survey = geo.datasets.load("rocklea_dome/survey.csv") hyperspec = geo.datasets.load("rocklea_dome/hyperspec.csv")

In [4]:

collar.head()

collar.head()

Out[4]:

	Hole_ID	X	Y	RL_AHD	Depth
0	RKD002	548314.8	7476791.8	467.4	45.2
1	RKD004	548095.6	7476396.0	462.3	53.0
2	RKD015	547905.0	7476800.0	470.0	41.0
3	RKD005	547505.9	7475596.0	461.6	42.4
4	RKD006	547698.1	7475588.5	458.6	42.5

In [5]:

assay.head()

assay.head()

Out[5]:

	Hole_ID	From	To	Sample_ID	Fe	Fe2o3	P	S	SiO2	Al2O3	MnO	Mn	CaO	K2O	MgO	Na2O	TiO2	LOI	LOI_100
0	RKC001	35.0	36.0	36.0	0.0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0	0.0
1	RKC001	36.0	37.0	37.0	0.0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0	0.0
2	RKC001	37.0	38.0	38.0	0.0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0	0.0
3	RKC001	38.0	39.0	39.0	0.0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0	0.0
4	RKC001	39.0	40.0	40.0	0.0	0	0	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0	0	0.0

In [6]:

survey.head()

survey.head()

Out[6]:

	Hole_ID	Distance	Azi_mag	Dip
0	RKD002	0	0	-90
1	RKD015	0	0	-90
2	RKD005	0	0	-90
3	RKD006	0	0	-90
4	RKD004	0	0	-90

In [7]:

dh = geo.create_drillholes(
    name="RCK", 
    collar=collar, 
    assays=assay, 
    survey=survey,
    method="Tangential",
    dip_convention="NEGATIVE"
)

dh = geo.create_drillholes( name="RCK", collar=collar, assays=assay, survey=survey, method="Tangential", dip_convention="NEGATIVE" )

2024-10-30 13:34:09,680 [INFO] GeoLime Project - |GEOLIME|.drillholes_api.py : Following mapping has been identified: {
    "HOLEID": "Hole_ID",
    "X_COLLAR": "X",
    "Y_COLLAR": "Y",
    "Z_COLLAR": "RL_AHD",
    "AZIMUTH": "Azi_mag",
    "DEPTH_SURVEY": "Distance",
    "DIP": "Dip",
    "FROM": "From",
    "TO": "To"
}

In [8]:

geo.plot(dh, property="Fe", agg_method="mean", width=650, height=400)

geo.plot(dh, property="Fe", agg_method="mean", width=650, height=400)

Case 2 : With multiple Interval Files → Assays & HyperSpectral¶

In [9]:

dh = geo.create_drillholes(
    name="RCK", 
    collar=collar, 
    assays=[assay, hyperspec], 
    survey=survey,
    method="Tangential",
    dip_convention="NEGATIVE"
)

dh = geo.create_drillholes( name="RCK", collar=collar, assays=[assay, hyperspec], survey=survey, method="Tangential", dip_convention="NEGATIVE" )

2024-10-30 13:34:11,328 [INFO] GeoLime Project - |GEOLIME|.drillholes_api.py : Following mapping has been identified: {
    "HOLEID": "Hole_ID",
    "X_COLLAR": "X",
    "Y_COLLAR": "Y",
    "Z_COLLAR": "RL_AHD",
    "AZIMUTH": "Azi_mag",
    "DEPTH_SURVEY": "Distance",
    "DIP": "Dip",
    "FROM": "From",
    "TO": "To"
}

In [10]:

geo.plot(dh, property="Fe", agg_method="mean", width=400, height=650)

geo.plot(dh, property="Fe", agg_method="mean", width=400, height=650)

Case 3 : Setting the name of the mandatory columns¶

By default GeoLime guesses the mapping of the mandatory columns for each columns based on the name of the columns. If one wants to directly provide the correct mapping, there is the name_dictionnary argument.

In [11]:

dh = geo.create_drillholes(
    name="RCK", 
    collar=collar, 
    assays=[assay, hyperspec], 
    survey=survey,
    method="Tangential",
    dip_convention="NEGATIVE",
    name_dictionary={
        "HOLEID": "Hole_ID",
        "X_COLLAR": "X",
        "Y_COLLAR": "Y",
        "Z_COLLAR": "RL_AHD",
        "AZIMUTH": "Azi_mag",
        "DEPTH_SURVEY": "Distance",
        "DIP": "Dip",
        "FROM": "From",
        "TO": "To"
    }
)

dh = geo.create_drillholes( name="RCK", collar=collar, assays=[assay, hyperspec], survey=survey, method="Tangential", dip_convention="NEGATIVE", name_dictionary={ "HOLEID": "Hole_ID", "X_COLLAR": "X", "Y_COLLAR": "Y", "Z_COLLAR": "RL_AHD", "AZIMUTH": "Azi_mag", "DEPTH_SURVEY": "Distance", "DIP": "Dip", "FROM": "From", "TO": "To" } )

From A Desurveyed File¶

In [12]:

desurveyed_df = geo.datasets.load("rocklea_dome/desurveyed_dh.csv")

desurveyed_df = geo.datasets.load("rocklea_dome/desurveyed_dh.csv")

In [13]:

desurveyed_df.head()

desurveyed_df.head()

Out[13]:

	FROM	TO	HOLEID	X_COLLAR	Y_COLLAR	Z_COLLAR	X_M	Y_M	Z_M	X_B	...	LOI	Fe_ox_ai	hem_over_goe	kaolin_abundance	kaolin_composition	wmAlsmai	wmAlsmci	carbai3pfit	carbci3pfit	Depth
0	0	1	RKC278	548001.3	7474002.1	453.5	548001.3	7474002.1	453.0	548001.3	...	NaN	0.1290	891.13	0.0374	0.999	NaN	NaN	NaN	NaN	47
1	1	2	RKC278	548001.3	7474002.1	453.5	548001.3	7474002.1	452.0	548001.3	...	NaN	0.1460	892.68	0.0269	1.006	NaN	NaN	NaN	NaN	47
2	2	3	RKC278	548001.3	7474002.1	453.5	548001.3	7474002.1	451.0	548001.3	...	NaN	0.1650	895.24	NaN	NaN	NaN	NaN	NaN	NaN	47
3	3	4	RKC278	548001.3	7474002.1	453.5	548001.3	7474002.1	450.0	548001.3	...	NaN	0.1000	893.34	NaN	NaN	0.0545	2211.83	NaN	NaN	47
4	4	5	RKC278	548001.3	7474002.1	453.5	548001.3	7474002.1	449.0	548001.3	...	NaN	0.0445	910.32	NaN	NaN	NaN	NaN	0.111	2312.89	47

5 rows × 35 columns

In [14]:

dh = geo.drillholes_from_dataframe(name="RCK", df=desurveyed_df)

dh = geo.drillholes_from_dataframe(name="RCK", df=desurveyed_df)

In [15]:

geo.plot(dh, property="Fe_ox_ai", agg_method="mean", width=400, height=650)

geo.plot(dh, property="Fe_ox_ai", agg_method="mean", width=400, height=650)

From GeoLime File¶

Case 1 : Loading the entire File¶

In [16]:

file_path = "../../../geolime/datasets/rocklea_dome/dh_hyper.geo"

file_path = "../../../geolime/datasets/rocklea_dome/dh_hyper.geo"

In [17]:

dh = geo.read_file(file_path)

dh = geo.read_file(file_path)

In [18]:

geo.plot(dh, property="Fe_pct", agg_method="mean", width=400, height=650)

geo.plot(dh, property="Fe_pct", agg_method="mean", width=400, height=650)

Case 2 : Loading only some properties¶

For memory management GeoLime allows to only load a subset of properties of the original object

In [19]:

dh = geo.read_file(file_path, attributes=["Fe_pct"])

dh = geo.read_file(file_path, attributes=["Fe_pct"])

In [20]:

dh.user_properties()

dh.user_properties()

Out[20]:

['Fe_pct']