Explore with intention and purpose
We strive to build projects that are efficient and cost-effective with current technologies, innovative approaches and practical solutions that will ensure your boots on the ground are well-directed.
R Statistics-RQ Principal Component Analysis
Exploratory data analysis (EDA) and RQ-Principal Component Analysis (PCA) of geochemical and biogeochemical data for use in mineral exploration and environmental studies.
These scripts are designed to take you through censoring (the replacement of missing values using k-nearest neighbour and the imputation of upper or lower detection limit values using ilr), clr-transformation and RQ-PCA.
Please note that these scripts use R-packages that are protected under their own licenses and it's recommended that each license is reviewed prior to use.
Python-DIM Neural Nework
These coding companions are designed to take you through the creation of neural networks to classify associated diamond contents for 5 different diamond indicator silicate minerals: G10 garnet (G10), eclogitic garnet (EG), chrome diopside (CRD), olivine (OL) and orthopyroxene (OPX). The csv data files are also available.
Using data collected from Benz (2006), neural networks were created to assist in the classification of diamond indicator silicate minerals associated with high or low diamond contents. The models created would greatly benefit from more samples from a greater variety of areas and future research would include testing the chemical complexity of SEM data for the ability to create reliable neural network models that could be used to assist exploration planning.
Please note that these scripts use Python-packages that are protected under their own licenses and it's recommended that each license is reviewed prior to use.