Basalt Tectonic Discrimination Using Combined Machine Learning Approach

Abstract

Geochemical discrimination of basaltic magmatism from different tectonic settings remains an essential part of recognizing the magma generation process within the Earth’s mantle. Discriminating among mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB) is that matters to geologists because they are the three most concerned basalts. Being a supplement to conventional discrimination diagrams, we attempt to utilize the machine learning algorithm (MLA) for basalt tectonic discrimination. A combined MLA termed swarm optimized neural fuzzy inference system (SONFIS) was presented based on neural fuzzy inference system and particle swarm optimization. Two geochemical datasets of basalts from GEOROC and PetDB served as to test the classification performance of SONFIS. Several typical discrimination diagrams and well-established MLAs were also used for performance comparisons with SONFIS. Results indicated that the classification accuracy of SONFIS for MORB, OIB and IAB in both datasets could reach over 90%, superior to other methods. It also turns out that MLAs had certain advantages in making full use of geochemical characteristics and dealing with datasets containing missing data. Therefore, MLAs provide new research tools other than discrimination diagrams for geologists, and the MLA-based technique is worth extending to tectonic discrimination of other volcanic rocks.