基于卷积神经网络和火山岩大数据的构造源区判别

早先的构造源区判别图由于受时代、研究区域、研究思路以及研究手段、分析技术和样本数量的限制, 存在某些不足，导致部分学者在研究中遇到各种困惑和矛盾。在大数据的冲击下，部分传统图解的可靠性正在接受考验。本文提出了一种将地球化学数据二维图像化的方法，将GEOROC数据库中来自11个构造环境的火山岩数据生成了34 468张灰度二维码图像。根据深度学习理论和方法构造了卷积神经网络(CNN)模型，利用其中75%的二维码数据进行自动学习和训练。该模型可以对不同来源的火山岩数据进行有效分类，总体分类准确度可达95%以上。该模型具备较好的泛化能力，可以作为日常工具辅助人工进行火山岩样本的构造源区的判别。

Tectonic discrimination based on convolution neural network and big data of volcanic rocks

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.