MICA – Mineral Identification and Compositional Analysis
August 13, 2018
Minerals are confusing, at least to most of us! What’s worse is that finding out exactly what we are confusing ourselves with is hard to know.
Fortunately, the good people at webmineral.com have compiled libraries of the elemental composition of a large amount of known minerals. Using this data, we can create a measure of mineral similarity to see how different and similar minerals are to each other.
We have built a web app, MICA (Mineral Identification and Compositional Analysis), that does a lot of the hard work!
MICA uses a database of 4722 minerals and 85 elements. The proportion of each element in each mineral is recorded in the database:
We then take that information and reduce the dimensionality of the data so that the relationships between the compositions of the 85 elements can be displayed on a 2-dimensional scatter plot. This means that minerals with similar composition will plot close to each other:
MICA allows you to zoom in and out of the map, colouring the minerals by whichever element you choose along the way. Selecting some minerals using the lasso tool lets you see the selections and their chemical formula:
Selecting several minerals also lets you identify the important elements that makes them similar in the first place. MICA ranks the elemental importance of the mineral similarity measure. This rank is found by running an unsupervised Random Forest model through the selected minerals and extracting the elemental importance using the mean decrease in the Gini coefficient:
Let’s get back to answering the main issue with mineral discrimination:
What else could this mineral be?
MICA lets you pick a mineral and then look at the most similar minerals and compare their elemental compositions:
Here is a quick demo of some key features of MICA: