Abstract: The traditional tectonic discrimination graphs have some shortcomings due to the limitation of available analytical methods and techniques of times. This has led to some confusions and contradictions for scholars using the graphs in their researches. Under the impact of big data, the reliability of some traditional tectonic discrimination graphs is being tested. In this paper, we proposed a method for the two-dimensional visualization of geochemical data. Using this method, we converted the geochemical compositions of volcanic rocks from 11 tectonic environments registered in the GEOROC database into 34468 two-dimensional coded images. Relying on deep learning method, 75% of the images were used to learn and train automatically to construct the convolution neural network (CNN) model, which can be employed to classify volcanic rocks into tectonic groups at an overall accuracy of 95%. This model has good generalization capability and can be routinely used to distinguish the tectonic source regions of volcano rock samples.

Key words: big data, two-dimensional code, convolution neural network, tectonic discrimination