Introduction

Demand for rare earth elements (REEs) is estimated to grow by 400-600% over the next decade”. (quote). These elements occur in a series of critical minerals to support the “electrification targets” around the world in support of the Green Revolution.

Questions: Do REEs occur in Tasmania? If so which REEs occur and where? Are there other elements that occur with these elusive minerals to aid mineral exploration geologists to find REEs?

Setup

Imports dataset from data folder, ensures numeric variables are numeric and factors are factors.

Load packages

library(tidyverse)
library(here)
library(ggbeeswarm)
library(RColorBrewer)

Read in Rare Earth Element csv data

plotREE <- read_csv(here("data", "dolerite2.csv"))

Ensures numeric variables are numeric and factors are factors

plotREE$Lappm <- as.numeric(plotREE$Lappm)
plotREE$Ceppm <- as.numeric(plotREE$Ceppm)
plotREE$Yppm <- as.numeric(plotREE$Yppm)
plotREE$K2Opct <- as.numeric(plotREE$K2Opct)
plotREE$Area <- as.factor(plotREE$Area)

Explore the raw REEs data

Investigate which Areas to focus on to find REEs of interest and establish other-mineral compounds that might guide geologists to potential areas with REEs like potassium and iron. Potassium is a radiogenic mineral that can be found using a geophysical survey called Airborne Radiometrics that can cover large areas by air for a nominal fee. Iron Oxide is a common mineral compound analyzed in a classic analytic whole rock suite.

Plot raw Lanthanum (Lappm) values by area

plotREE %>%
  ggplot(aes(x = Lappm, y = Area, color = Lappm)) + 
  geom_jitter() +
  labs(title = " Lanthanum values in Tasmania raw data")

Plot Cesium (Ceppm) values by area

plotREE %>%
  ggplot(aes(x = Ceppm, y = Area, color = Ceppm)) + 
  geom_jitter() +
  labs(title = " Cesium values in Tasmania raw data")

Plot Yttrium (Yppm) values by area

plotREE %>%
  ggplot(aes(x = Yppm, y = Area, color = Yppm)) + 
  geom_jitter() +
  theme_grey() +
  labs(title = " Yttrium values in Tasmania raw data")

Plot Potassium oxide values (K2Opct) (a non-REE mineral compound) by area

plotREE %>%
  ggplot(aes(x = K2Opct, y = Area, color = K2Opct)) + 
  geom_jitter() +
  labs(title = " Potassium values in Tasmania raw data")

By plotting the raw data in a variable point plot we can see a similar pattern in elevated REEs including Lanthanum (Lappm), Cesium(Ceppm), Yttrium(Yppm) and a useful mineral compound K2O percent by area.

Plot Iron Oxide (a non-REE mineral compound) levels by Area

plotREE %>%
  ggplot(aes(x = FeOpct, y = Area, color = FeOpct)) + 
  geom_jitter() +
  labs(title = " Iron oxide values in Tasmania raw data")

The raw data plots above show lighter tone of blue for higher values and darker blue tone for lower values of the variable shown in the plots by area. The variables Lappm, Ceppm, Yppm correspond show similar patterns.High levels of iron oxide (FeO) correspond to low levels of Lappm, Ceppm, Yppm (REEs) and K2Opct. A negative anomaly for iron oxide may suggest high potential for REEs.

Save the raw data plots for reference

ggsave(here("output", "RawLanthanum.png"))
ggsave(here("output", "RawCesium.png"))
ggsave(here("output", "RawYttrium.png"))
ggsave(here("output", "RawPotassium.png"))
ggsave(here("output", "RawIronOxide.png"))

Potassium is a radiogenic element that can be defined using a geophysical method called an Airborne Radiometric Survey where radiogenic elements including potassium (K), uranium(U), thorium(Th) are captured. Large areas can be flown at a nominal cost. Having elevated potassium in any mineral compound would aid exploration geologists in prioritizing areas with high potential for Rare earth elements.

How does Lanthanum relative to potassium compare across all areas?

plotREE %>%
  na.omit() %>%
  ggplot(aes(x = Lappm, y = K2Opct, color = Lappm)) + 
  geom_jitter() + 
  facet_wrap(~ Area) +
  scale_color_gradient(low = "blue", high = "red") +
  labs(title = " Lanthanum values in Tasmania raw data by area")

How does Cesium relative to potassium compare across all areas?

plotREE %>%
  na.omit() %>%
  ggplot(aes(x = Ceppm, y = K2Opct, color = Ceppm)) + 
  geom_jitter() + 
  facet_wrap(~ Area) +
  scale_color_gradient(low = "blue", high = "red") +
  labs(title = " Cesium values in Tasmania raw data by area")

How does Yttrium relative to potassium compare across all areas?

plotREE %>%
  na.omit() %>%
  ggplot(aes(x = Yppm, y = K2Opct, color = Yppm)) + 
  geom_jitter() + 
  facet_wrap(~ Area) +
  scale_color_gradient(low = "blue", high = "red") +
  labs(title = " Yttrium values in Tasmania raw data by area")

High Lanthanum, Cesium and Yttrium occurs in the same areas defined by hotter red colors. A few of these areas are Red Hill, Red Hill Intrusion, and O’Briens Hill.

How does Iron Oxide relative to potassium compare across all areas?

plotREE %>%
  na.omit() %>%
  ggplot(aes(x = FeOpct, y = K2Opct, color = FeOpct)) + 
  geom_jitter() + 
  facet_wrap(~ Area) +
  scale_color_gradient(low = "blue", high = "red") +
  labs(title = " Iron Oxide values in Tasmania raw data by area")

The very high iron oxide defined by red show the inverse pattern to the La ppm, Ce ppm and Y ppm plots.

Save plots of raw data by area relative to potassium oxide

ggsave(here("output", "FacetPotassiumLanthanumAllAreas.png"))
ggsave(here("output", "FacetPotassiumCesiumAllAreas.png"))
ggsave(here("output", "FacetPotassiumYttriumAllAreas.png"))
ggsave(here("output", "FacetPotassiumIronAllAreas.png"))

Filter three areas for potassium versus Lanthanum levels

plotREE %>%
  na.omit() %>%
  filter(Area %in% c("Red Hill", "Red Hill Intrusion", "O'Briens Hill")) %>%
  ggplot(aes(x = K2Opct, y = Lappm, color = Area)) +
  geom_jitter() + 
  facet_wrap(~ Area) +
  theme_grey() +
  labs(title = " Lanthanum (La) versus K2O percent filtered by areas of interest")

K2O pct versus La ppm. Each dot corresponds to one sample. There is a high tendency to have higher La ppm levels with higher potassium levels. At the Red Hill Intrusion area there is a cluster of K2O pct less than 1 percent with La ppm < 12ppm. This suggests that while an Airborne Radiometric Survey (K-U-Th) would highlight areas elevated in the radiogenic potassium, the K2O anomaly might be weak but could be used to find Lanthanum.The Red Hill has the highest La ppm outlier at 40ppm La.

Filter three areas for potassium versus Cesium

plotREE %>%
  na.omit() %>%
  filter(Area %in% c("Red Hill", "Red Hill Intrusion", "O'Briens Hill")) %>%
  ggplot(aes(x = K2Opct, y = Ceppm, color = Area)) +
  geom_jitter() + 
  facet_wrap(~ Area) +
  theme_grey() +
  labs(title = " Cesium (Ce) versus K2O percent filtered by areas of interest")

K2O pct vs Ceppm. Each dot corresponds to one sample. There is a strong tendency to have higher Cesium values with high potassium values.At the Red Hill Intrusion area there is a cluster of samples with K2O pct less than 1.25 percent with Cesium ranging between 25-30ppm. This suggests that while an Airborne Radiometric Survey (K-U-Th) would highlight areas elevated in the radiogenic potassium to find potential areas of Cesium. The Red Hill area has the highest outlier at close to 90ppm Ce.

Filter three areas for potassium versus Yttrium

plotREE %>%
  na.omit() %>%
  filter(Area %in% c("Red Hill", "Red Hill Intrusion", "O'Briens Hill")) %>%
  ggplot(aes(x = K2Opct, y = Yppm, color = Area)) +
  geom_jitter() + 
  facet_wrap(~ Area) +
  labs(title = " Yttrium(Y) versus K2O percent filtered by areas of interest")

K2O pct versus Yppm. Each dot corresponds to one sample. There is a strong tendency to have high Yttrium values with high potassium values. The potassium values increase with the Yttrium values in support of using an Airborne Radiometric Survey (K-U-Th) to find potential areas of Yttrium.

Filter the areas for potassium versus iron oxide(FeOpct)

plotREE %>%
  na.omit() %>%
  filter(Area %in% c("Red Hill", "Red Hill Intrusion", "O'Briens Hill")) %>%
  ggplot(aes(x = K2Opct, y = FeOpct, color = Area)) +
  geom_jitter() + 
  facet_wrap(~ Area) +
  labs(title = " FeO percent versus K2O percent in three areas of interest")

K2O pct versus FeO pct. Each dot corresponds to one sample. At the Red Hill Intrusion and O’Briens Hill high levels of FeO percent (above 7.75 percent FeO) show the opposite pattern to the other REE plots. There is a tendency for lower potassium (therefore rare earths) with higher iron oxide values.

To access plots of subset

ggsave(here("output", "SelectPotassiumLanthanum.png"))
ggsave(here("output", "SelectPotassiumCesium.png"))
ggsave(here("output", "SelectPotassiumYttrium.png"))
ggsave(here("output", "SelectPotassiumIron.png"))

Lanthanum data in a series of visualizations

Show Lanthanum by Area using Boxplot

plotREE %>%
  na.omit() %>%
  ggplot(aes(x = Area, y = Lappm)) +
  geom_boxplot() +
  coord_flip() + 
  theme_classic() +
  labs(title = "Lanthanum value distribution by area")

ggsave(here("output", "BOXLanthanumByArea.png"))

The Box plot above of Lanthanum by area shows the highest values and greatest spread of Lanthanum values occur at the Red Hill and O’Brien areas. The mean is shown in the solid black line in each box (mean = 18ppmLa at Red Hill area)

Look at correlations between variables

Correlation between Lappm and K20 and colored by Ceppm

plotREE %>%
  na.omit() %>%
  filter(Lappm > 10) %>%
  ggplot(aes(x = K2Opct, y = Lappm, color = Ceppm)) +
  geom_point() +
  geom_smooth() +
  scale_color_gradient(low = "blue", high = "red") +
  labs(title = " Lanthanum values in Tasmania relative to potassium and cesium",
       subtitle = "Only La ppm values over 10 ppm",
       caption = "data from https://www.pdac.ca/convention/programming/short-courses")

ggsave(here("output", "CORLa_K2O_Ce.png"))

The really extreme Lanthanum values in the data set correlates well with the extreme values of potassium and both correlate with high Cesium.