Study of the chemical and mineralogical correlations

The correlation coefficient measure the strength of the linear relationship between two variables, here between two elements concentration. We will use the GeoLime dictionary to analyse the correlations between the differents elements present in the Rocklea Dome datas.

import pandas as pd
import geolime as geo
from pyproj import CRS
import numpy as np

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

First we need to import our data from Geolime .geo files.

dh = geo.read_file("../data/dh_hyper.geo")

dh.user_properties()

['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',
 'Fe_ox_ai',
 'hem_over_goe',
 'kaolin_abundance',
 'kaolin_composition',
 'wmAlsmai',
 'wmAlsmci',
 'carbai3pfit',
 'carbci3pfit',
 'Sample_ID',
 'Fe',
 'Fe2o3',
 'P',
 'S',
 'SiO2',
 'MnO',
 'Mn',
 'CaO',
 'K2O',
 'MgO',
 'Na2O',
 'TiO2',
 'LOI_100',
 'Depth']

Chemical Analysis

Correlation Analysis

Correlation Matrix

Now we can create the correlation matrix for the hyperspectral data:

geo.correlation_heatmap(
    geo_object=dh, 
    properties=["Mn_pct","P_pct","Fe_pct","CaO_pct","MgO_pct","S_pct","Al2O3","TiO2_pct","SiO2_pct","K2O_pct"]
)