A Method for Classification of Heavy Mineral Based on Machine Learning

With the development of machine learning, this technology is more and more used in people's daily life, such as object classification, portrait recognition, text analysis and so on. In recent years, researchers have tried to apply machine learning to Earth Sciences, such as image recognition of geological ores. In this study, we apply the method of machine learning to the classification and recognition mineral based on chemical composition, and propose a classification algorithm based on feature selection. we obtain the key attribute of classification, namely feature selection, through accurate data, and add a classification algorithm on this basis, so that the category can be obtained quickly when new samples are encountered. At the same time, our experiments are based on two real types of data in geosciences: EMPA data and EDS data. Our algorithm is verified by using real experimental data, and a large number of test results show that our algorithm has high accuracy and can quickly identify the categories of mineral through EDS composition data, which also shows that our algorithm is effective.