Comparison of the assay and hyperspectral datas

Comparison of the assay and hyperspectral datas#

The assay file contains the Hole_ID of every drill core, their position in the drill, the chemical composition of the sample (Fe, Fe2O3, P, S, SiO2, Al2O3, MnO, Mn, CaO, K2O, MgO, Na2O, TiO2) and the loss-on-ignition percentage. The hyperspectral file contains the Hole_ID of every borehole, their position in the drill, the chemical composition of each sample (Fe, Al2O3, SiO2, K2O, CaO, MgO, TiO2, P, S, Mn), the loss-on-ignition percentage and other informations about the minerals in the sample.

The common data between these two files are the Hole_ID, the depth, most columns about chemical composition (Fe, Al2O3, SiO2, K2O, CaO, MgO, TiO2, P, S, Mn) and the loss-on-ignition percentage.

import numpy as np
import pandas as pd
import geolime as geo
from pyproj import CRS
import seaborn as sns
import pyvista as pv

pv.set_jupyter_backend('html')

geo.Logger().set_level(25)
# Format Pandas display for clarity and readability
pd.set_option("display.max_rows", 10)
pd.set_option("display.max_columns", 500)

We want to compare the Hole_ID of the assay and hyperspectral file to know if they have some drill core in common.

First we need to import these data from .csv files.

Merging Interval Files#

Assay File reading and formatting#

assay = geo.datasets.load("rocklea_dome/assay.csv")
assay
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
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17461 RKD010 0.0 44.0 NaN 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
17462 RKD011 0.0 42.5 NaN 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
17463 RKD013 0.0 30.0 NaN 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
17464 RKD014 0.0 44.0 NaN 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
17465 RKD015 0.0 41.0 NaN 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

17466 rows × 19 columns

Some columns have string values named LNR, replacing them to NaN will allow comparisons with other number columns

assay.dtypes

Hole_ID       object
From         float64
To           float64
Sample_ID    float64
Fe           float64
              ...   
MgO          float64
Na2O         float64
TiO2          object
LOI           object
LOI_100      float64
Length: 19, dtype: object

assay.eq("LNR").any()

Hole_ID      False
From         False
To           False
Sample_ID    False
Fe           False
             ...  
MgO          False
Na2O         False
TiO2          True
LOI           True
LOI_100      False
Length: 19, dtype: bool

assay.replace("LNR", np.nan, inplace=True)
assay[["P", "S", "TiO2", "LOI"]] = assay[["P", "S", "TiO2", "LOI"]].apply(pd.to_numeric)

Hyperspec File reading#

hyperspec = pd.read_csv("../data/hyperspec.csv")
hyperspec
Hole_ID From To Fe_pct Al2O3 SiO2_pct K2O_pct CaO_pct MgO_pct TiO2_pct P_pct S_pct Mn_pct LOI Fe_ox_ai hem_over_goe kaolin_abundance kaolin_composition wmAlsmai wmAlsmci carbai3pfit carbci3pfit
0 RKC278 0.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1290 891.13 0.0374 0.999 NaN NaN NaN NaN
1 RKC278 1.0 2.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1460 892.68 0.0269 1.006 NaN NaN NaN NaN
2 RKC278 2.0 3.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1650 895.24 NaN NaN NaN NaN NaN NaN
3 RKC278 3.0 4.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1000 893.34 NaN NaN 0.0545 2211.83 NaN NaN
4 RKC278 4.0 5.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0445 910.32 NaN NaN NaN NaN 0.111 2312.89
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7103 RKC485 41.0 42.0 28.0 9.80 39.05 0.037 0.08 0.22 1.078 0.027 0.006 0.12 9.00 0.2210 912.17 0.0322 1.004 NaN NaN NaN NaN
7104 RKC485 42.0 43.0 39.0 7.67 25.15 0.029 0.09 0.25 0.689 0.046 0.008 0.11 10.11 0.1200 912.43 0.1140 1.027 NaN NaN NaN NaN
7105 RKC485 43.0 44.0 16.0 20.23 43.41 0.177 0.17 0.34 1.617 0.020 0.018 0.05 10.80 0.0956 888.81 0.0306 1.012 NaN NaN NaN NaN
7106 RKC485 44.0 45.0 12.0 12.53 50.24 0.060 0.26 0.25 1.473 0.006 0.779 0.03 16.18 0.0378 958.21 0.1100 1.025 NaN NaN NaN NaN
7107 RKD015 0.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

7108 rows × 22 columns

Merge Preparation#

The comparison of the two datasets require to have the list of the different Hole_ID of both files. These lists also need to be turned into sets in order to compare them.

colonne_assay = set(assay["Hole_ID"])
colonne_hyperspec = set(hyperspec["Hole_ID"])

Now we have two sets with the different drill core that have been analyzed for each method.

len(colonne_assay)

500

len(colonne_hyperspec)

192

Next command will search all the Hole_ID both in the assay and hyperspec file.

intersection = colonne_assay.intersection(colonne_hyperspec)

len(intersection)

192

The intersection of these two files has the same length as the smallest set, so every Hole_ID of the hyperspectral file are in the assay.

The drill cores of the hyperspectral file are included in the assay file. Now we want to see if the common columns between these two files have the same values. First we need to filter the DataFrame of the assay file in order to keep only the Hole_ID that are in common with the hyperspectral file.

intersection = list(intersection)
filtred_assay = assay[assay["Hole_ID"].isin(intersection)]
filtred_assay
Hole_ID From To Sample_ID Fe Fe2o3 P S SiO2 Al2O3 MnO Mn CaO K2O MgO Na2O TiO2 LOI LOI_100
9778 RKC278 0.0 1.0 10001.0 0.00 0 0.000 0.000 0.00 0.00 0.0 0.000 0.00 0.000 0.00 0.0 0.000 0.00 0.0
9779 RKC278 1.0 2.0 10002.0 0.00 0 0.000 0.000 0.00 0.00 0.0 0.000 0.00 0.000 0.00 0.0 0.000 0.00 0.0
9780 RKC278 2.0 3.0 10003.0 0.00 0 0.000 0.000 0.00 0.00 0.0 0.000 0.00 0.000 0.00 0.0 0.000 0.00 0.0
9781 RKC278 3.0 4.0 10004.0 0.00 0 0.000 0.000 0.00 0.00 0.0 0.000 0.00 0.000 0.00 0.0 0.000 0.00 0.0
9782 RKC278 4.0 5.0 10005.0 0.00 0 0.000 0.000 0.00 0.00 0.0 0.000 0.00 0.000 0.00 0.0 0.000 0.00 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17379 RKC485 13.0 14.0 17215.0 10.73 0 0.003 0.021 48.30 21.94 0.0 0.001 0.92 0.055 2.04 0.0 1.379 9.84 0.0
17380 RKC485 10.0 11.0 17212.0 11.74 0 0.005 0.023 46.01 19.45 0.0 0.001 2.23 0.063 2.39 0.0 1.699 10.59 0.0
17381 RKC485 11.0 12.0 17213.0 11.25 0 0.002 0.010 48.25 18.76 0.0 0.001 1.84 0.054 2.49 0.0 1.407 9.92 0.0
17382 RKC485 12.0 13.0 17214.0 8.41 0 0.001 0.007 49.28 19.61 0.0 0.001 3.75 0.058 2.58 0.0 1.239 11.19 0.0
17465 RKD015 0.0 41.0 NaN 0.00 0 0.000 0.000 0.00 0.00 0.0 0.000 0.00 0.000 0.00 0.0 0.000 0.00 0.0

7235 rows × 19 columns

The next operation will delete every sample of the assay file that is not in the hyperspectral file and make sure that we have the exact same number of Hole_ID in both files.

assay_final = hyperspec.merge(filtred_assay, on=["Hole_ID", "From", "To"], how="left")
assay_final.sort_values(by=["Hole_ID", "From"], inplace=True)
assay_final
Hole_ID From To Fe_pct Al2O3_x SiO2_pct K2O_pct CaO_pct MgO_pct TiO2_pct P_pct S_pct Mn_pct LOI_x Fe_ox_ai hem_over_goe kaolin_abundance kaolin_composition wmAlsmai wmAlsmci carbai3pfit carbci3pfit Sample_ID Fe Fe2o3 P S SiO2 Al2O3_y MnO Mn CaO K2O MgO Na2O TiO2 LOI_y LOI_100
0 RKC278 0.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1290 891.13 0.0374 0.999 NaN NaN NaN NaN 10001.0 0.00 0.0 0.000 0.000 0.00 0.00 0.0 0.00 0.00 0.000 0.00 0.0 0.000 0.00 0.0
1 RKC278 1.0 2.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1460 892.68 0.0269 1.006 NaN NaN NaN NaN 10002.0 0.00 0.0 0.000 0.000 0.00 0.00 0.0 0.00 0.00 0.000 0.00 0.0 0.000 0.00 0.0
2 RKC278 2.0 3.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1650 895.24 NaN NaN NaN NaN NaN NaN 10003.0 0.00 0.0 0.000 0.000 0.00 0.00 0.0 0.00 0.00 0.000 0.00 0.0 0.000 0.00 0.0
3 RKC278 3.0 4.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.1000 893.34 NaN NaN 0.0545 2211.83 NaN NaN 10004.0 0.00 0.0 0.000 0.000 0.00 0.00 0.0 0.00 0.00 0.000 0.00 0.0 0.000 0.00 0.0
4 RKC278 4.0 5.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0445 910.32 NaN NaN NaN NaN 0.111 2312.89 10005.0 0.00 0.0 0.000 0.000 0.00 0.00 0.0 0.00 0.00 0.000 0.00 0.0 0.000 0.00 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7103 RKC485 41.0 42.0 28.0 9.80 39.05 0.037 0.08 0.22 1.078 0.027 0.006 0.12 9.00 0.2210 912.17 0.0322 1.004 NaN NaN NaN NaN 17244.0 28.01 0.0 0.027 0.006 39.05 9.80 0.0 0.12 0.08 0.037 0.22 0.0 1.078 9.00 0.0
7104 RKC485 42.0 43.0 39.0 7.67 25.15 0.029 0.09 0.25 0.689 0.046 0.008 0.11 10.11 0.1200 912.43 0.1140 1.027 NaN NaN NaN NaN 17245.0 38.60 0.0 0.046 0.008 25.15 7.67 0.0 0.11 0.09 0.029 0.25 0.0 0.689 10.11 0.0
7105 RKC485 43.0 44.0 16.0 20.23 43.41 0.177 0.17 0.34 1.617 0.020 0.018 0.05 10.80 0.0956 888.81 0.0306 1.012 NaN NaN NaN NaN 17246.0 15.53 0.0 0.020 0.018 43.41 20.23 0.0 0.05 0.17 0.177 0.34 0.0 1.617 10.80 0.0
7106 RKC485 44.0 45.0 12.0 12.53 50.24 0.060 0.26 0.25 1.473 0.006 0.779 0.03 16.18 0.0378 958.21 0.1100 1.025 NaN NaN NaN NaN 17247.0 12.22 0.0 0.006 0.779 50.24 12.53 0.0 0.03 0.26 0.060 0.25 0.0 1.473 16.18 0.0
7107 RKD015 0.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

7108 rows × 38 columns

Merged Data Verification#

This new file allows to make comparison between the data from the assay file (obtain by a XRF analysis) and the data from the hyperspectral analysis.

First we can compare the Fe weight percentage :

sns.scatterplot(data=assay_final, x="Fe", y="Fe_pct");
../_images/7cd96e41fd93bfb372a0e58cf1b509519936af444796a8562f4e1689d37f500f.png

Most of the measures are similar for both methods. We can do the same for the Al2O3 weight percentage :

sns.scatterplot(data=assay_final, x="Al2O3_x", y="Al2O3_y");
../_images/51c9b382cb8467b826ec38163f7a207bfbc76c80c6659c5caa5e89f96a5562b9.png 
assay_final.drop(columns=["Al2O3_y", "LOI_x", "LOI_y"], inplace=True)
assay_final.rename(columns={"Al2O3_x": "Al2O3"}, inplace=True)

GeoLime Drillholes Creation#

The next command will use the Geo Lime dictionary to draw an interactive map of the ferric concentration for each method (one for the hyperspectral datas and one for the assay datas). Firs we need to import the survey and collar file to have all the informations about the drill cores.

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

Project Definition#

geo.Project().set_crs(CRS("EPSG:20350"))

Drillholes Creation#

Using Assay Data Only#

da = geo.create_drillholes(
    name="rck_assay", 
    collar=collar, 
    assays=assay, 
    survey=survey
)

da.describe()
__TOPO_LEVEL__ __TOPO_TYPE__ __TOPO_ELEMENT_V1__ __TOPO_ELEMENT_V2__ FROM TO X_COLLAR Y_COLLAR Z_COLLAR X_M Y_M Z_M X_B Y_B Z_B X_E Y_E Z_E Sample_ID Fe Fe2o3 P S SiO2 Al2O3 MnO Mn CaO K2O MgO Na2O TiO2 LOI LOI_100 Depth
count 17466.0 17466.0 17466.000000 17466.000000 17466.000000 17466.000000 17466.000000 1.746600e+04 17466.000000 17466.000000 1.746600e+04 17466.000000 17466.000000 1.746600e+04 17466.000000 17466.000000 1.746600e+04 17466.000000 15061.000000 17466.000000 17466.0 16797.000000 16796.000000 17466.000000 17466.000000 17466.000000 17466.000000 17466.000000 17466.000000 17466.000000 17466.000000 17465.000000 17459.000000 17466.000000 17466.000000
mean 2.0 1.0 8974.469655 8975.469655 20.102900 21.138741 543919.388074 7.475144e+06 448.434576 543919.388074 7.475144e+06 427.813755 543919.388074 7.475144e+06 428.331676 543919.388074 7.475144e+06 427.295835 8460.010026 20.829760 0.0 0.014703 0.048990 20.605162 7.200063 0.002967 0.061429 1.049226 0.127557 0.853192 0.000698 0.418751 8.261108 8.390285 41.334051
std 0.0 0.0 5187.058469 5187.058469 14.265524 14.271989 5989.064771 1.926010e+03 18.584016 5989.064771 1.926010e+03 20.738886 5989.064771 1.926010e+03 20.750207 5989.064771 1.926010e+03 20.736974 5088.312571 20.344777 0.0 0.025723 0.346264 22.467024 7.547804 0.392103 0.146773 3.343146 0.460913 1.828060 0.072275 0.514699 93.754093 26.125344 13.671429
min 2.0 1.0 0.000000 1.000000 0.000000 1.000000 525400.000000 7.469518e+06 385.900000 525400.000000 7.469518e+06 362.600000 525400.000000 7.469518e+06 363.100000 525400.000000 7.469518e+06 362.100000 -8888.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 7.000000
25% 2.0 1.0 4486.250000 4487.250000 9.000000 10.000000 540200.000000 7.473600e+06 440.000000 540200.000000 7.473600e+06 413.200000 540200.000000 7.473600e+06 413.700000 540200.000000 7.473600e+06 412.700000 3979.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 31.000000
50% 2.0 1.0 8971.500000 8972.500000 18.000000 19.000000 547197.500000 7.475598e+06 454.100000 547197.500000 7.475598e+06 429.700000 547197.500000 7.475598e+06 430.200000 547197.500000 7.475598e+06 429.100000 8043.000000 14.595000 0.0 0.005000 0.006000 12.370000 5.520000 0.000000 0.010000 0.100000 0.025000 0.290000 0.000000 0.241000 9.320000 0.000000 43.000000
75% 2.0 1.0 13465.500000 13466.500000 29.000000 30.000000 547900.500000 7.476399e+06 461.700000 547900.500000 7.476399e+06 443.800000 547900.500000 7.476399e+06 444.400000 547900.500000 7.476399e+06 443.300000 12941.000000 41.250000 0.0 0.024000 0.020000 35.400000 11.550000 0.000000 0.090000 0.320000 0.095000 0.750000 0.000000 0.650000 11.550000 0.000000 49.000000
max 2.0 1.0 17964.000000 17965.000000 108.000000 109.000000 549297.900000 7.479595e+06 478.600000 549297.900000 7.479595e+06 478.100000 549297.900000 7.479595e+06 478.600000 549297.900000 7.479595e+06 477.600000 17330.000000 62.340000 0.0 0.845000 14.145000 96.360000 39.430000 51.820000 6.370000 40.820000 21.250000 20.010000 9.000000 10.000000 12367.000000 100.000000 109.000000 
da.plot_2d(property="Fe", agg_method="mean", interactive_map=True)
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Using Merged Data (Assay data & Hyperspectral data)#

df = geo.create_drillholes(
    name="rck_assay_final", 
    collar=collar, 
    assays=assay_final, 
    survey=survey
)

df.to_file("../data/dh_hyper")

df.plot_2d(property="Fe", agg_method="mean", interactive_map=True)
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Using Hyperspectral Data only#

We can do the same with hyperspectral data and draw the map of the average iron weight concentration given by these measures.

dh = geo.create_drillholes(
    name="rck_hs", 
    collar=collar, 
    assays=hyperspec, 
    survey=survey
)

dh.describe()
__TOPO_LEVEL__ __TOPO_TYPE__ __TOPO_ELEMENT_V1__ __TOPO_ELEMENT_V2__ FROM TO X_COLLAR Y_COLLAR Z_COLLAR X_M Y_M Z_M X_B Y_B Z_B X_E Y_E Z_E Fe_pct Al2O3 SiO2_pct K2O_pct CaO_pct MgO_pct TiO2_pct P_pct S_pct Mn_pct LOI Fe_ox_ai hem_over_goe kaolin_abundance kaolin_composition wmAlsmai wmAlsmci carbai3pfit carbci3pfit Depth
count 7108.0 7108.0 7108.000000 7108.000000 7108.000000 7108.000000 7108.000000 7.108000e+03 7108.000000 7108.000000 7.108000e+03 7108.000000 7108.000000 7.108000e+03 7108.000000 7108.000000 7.108000e+03 7108.000000 4962.000000 4962.000000 4962.000000 4945.000000 4962.000000 4961.000000 4962.000000 4582.000000 4635.000000 3696.000000 4955.000000 6896.000000 6639.00000 3841.000000 3841.000000 1689.000000 1689.000000 343.000000 343.000000 7108.000000
mean 2.0 1.0 3649.240293 3650.240293 20.568655 21.568655 547413.687521 7.475481e+06 458.500774 547413.687521 7.475481e+06 437.432119 547413.687521 7.475481e+06 437.932119 547413.687521 7.475481e+06 436.932119 31.597340 10.943720 28.319313 0.181180 1.501790 1.254634 0.631873 0.027161 0.054300 0.109976 11.297941 0.175376 910.74075 0.162194 1.023886 0.053203 2207.762297 0.070173 2319.090816 42.645751
std 0.0 0.0 2111.443717 2111.443717 13.834178 13.834178 685.729896 1.587386e+03 7.467718 685.729896 1.587386e+03 15.848056 685.729896 1.587386e+03 15.848056 685.729896 1.587386e+03 15.848056 17.845885 7.396816 19.816311 0.494112 4.219235 2.269107 0.524281 0.038540 0.371307 0.165558 4.640690 0.083901 8.87125 0.082431 0.046539 0.038201 3.049294 0.024599 9.787246 11.275263
min 2.0 1.0 0.000000 1.000000 0.000000 1.000000 545494.900000 7.472793e+06 441.000000 545494.900000 7.472793e+06 389.700000 545494.900000 7.472793e+06 390.200000 545494.900000 7.472793e+06 389.200000 0.000000 0.320000 2.380000 0.003000 0.010000 0.020000 0.003000 0.001000 0.001000 0.010000 0.710000 0.001300 869.78000 0.017200 0.937000 0.020000 2186.280000 0.040100 2296.430000 7.000000
25% 2.0 1.0 1816.750000 1817.750000 9.000000 10.000000 546904.700000 7.474003e+06 453.800000 546904.700000 7.474003e+06 425.900000 546904.700000 7.474003e+06 426.400000 546904.700000 7.474003e+06 425.400000 14.000000 5.370000 11.180000 0.022000 0.100000 0.280000 0.227000 0.009000 0.006000 0.030000 9.670000 0.107000 907.70000 0.091100 0.991000 0.028100 2206.370000 0.052750 2312.235000 36.000000
50% 2.0 1.0 3650.500000 3651.500000 19.000000 20.000000 547586.100000 7.475604e+06 458.200000 547586.100000 7.475604e+06 439.000000 547586.100000 7.475604e+06 439.500000 547586.100000 7.475604e+06 438.500000 35.000000 8.880000 22.660000 0.049000 0.190000 0.480000 0.484000 0.021000 0.013000 0.080000 11.200000 0.185000 911.14000 0.161000 1.007000 0.039600 2206.910000 0.063600 2321.900000 46.000000
75% 2.0 1.0 5479.250000 5480.250000 31.000000 32.000000 547947.600000 7.476004e+06 463.600000 547947.600000 7.476004e+06 449.500000 547947.600000 7.476004e+06 450.000000 547947.600000 7.476004e+06 449.000000 48.000000 15.255000 42.560000 0.137000 0.500000 1.020000 0.897000 0.036000 0.025000 0.150000 11.900000 0.246000 914.48000 0.226000 1.050000 0.064300 2208.140000 0.080500 2324.615000 51.000000
max 2.0 1.0 7298.000000 7299.000000 60.000000 61.000000 548498.100000 7.479595e+06 478.600000 548498.100000 7.479595e+06 478.100000 548498.100000 7.479595e+06 478.600000 548498.100000 7.479595e+06 477.600000 62.000000 39.430000 92.810000 9.504000 40.820000 20.010000 3.742000 0.845000 14.145000 6.160000 45.280000 0.355000 974.08000 0.413000 1.192000 0.285000 2233.120000 0.182000 2338.390000 61.000000 
dh.plot_2d(property="Fe_pct", agg_method="mean", interactive_map=True)
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The first thing we can notice is that there is a concentration of high average iron concentration in the eastern drill core of the original assay data. That is on those drill core that the hyperspectral data have been taken. Then we can also notice the strong similarity between the iron average concentration between the hyperspectral data and the assay data for the same drill holes.

3D Visualisation#

df_pv = df.to_pyvista('Fe_pct')

pl = pv.Plotter()
pl.add_mesh(df_pv.tube(radius=10))
pl.set_scale(zscale=20)

pl.show()
Contents