Mineral classification from quantitative X-ray maps using neural network: Application to volcanic rocks

We developed a mineral classification technique of electron probe microanalyzer (EPMA) maps in order to reveal the mineral textures and compositions of volcanic rocks. In the case of lithologies such as basalt that include several kinds of minerals, X-ray intensities of several elements derived from EPMA must be considered simultaneously to determine the mineral map. In this research, we used a Kohonen self-organizing map (SOM) to classify minerals in the thin-sections from several X-ray intensity maps. The SOM is a type of artificial neural network that is trained using unsupervised training to produce a two-dimensional representation of multi-dimensional input data. The classified mineral maps of in situ oceanic basalts of the Juan de Fuca Plate allowed us to quantify mineralogical and textural differences among the marginal and central parts of the pillow basalts and the massive flow basalt. One advantage of mineral classification using a SOM is that relatively many minerals can be estimated from limited input elements. By applying our method to altered basalt which contains multiple minerals, we successfully classify eight minerals in thin-section.