基于改进遗传算法一神经网络的玄武岩构造环境判别及对比实验

通过岩浆岩的地球化学特征判别岩浆形成的大地构造环境和岩浆源区的化学性质是地球化学全岩分析最重要的应用之一。该方法利用全岩地球化学数据，包括主量元素、微量元素和同位素组成数据，对给定岩浆岩(玄武岩、花岗岩等)的大地构造环境进行判别。作为人工智能技术在地球化学研究领域中的新尝试，机器学习判别方法逐渐成为经典判别图解法的补充研究手段。然而，高维数据特征筛选和繁多未知参数确定是影响算法分类准确性的两个主要因素。为此，提出一种遗传算法优化神经网络耦合判别方法(GA-NNDM)。该方法利用特征选择、参数确定和分类性能之间的反馈联系，将分类准确率、所选特征数量和特征代价作为适应度函数，通过迭代演化寻求最佳特征子集和未知参数，从而达到减少特征、优化参数和提高性能的目的。此外，根据公开玄武岩样品地球化学数据，通过K折交叉验证等方法设置纵向、横向比较实验来验证GA-NNDM在玄武岩构造环境判别方面的准确性、稳定性和外延性。仿真实验结果表明，GA-NNDM具有优良的判别效果和泛化能力，其总体分类准确率能达到90%。因此，GA-NNDM值得在地球化学领域做进一步推广应用。

Discrimination and comparison experiments of basalt tectonic setting based on improved genetic algorithm-optimized neural network

One of the most important applications of geochemical whole-rock analysis is to discriminate the tectonic settings for magma formation and properties of magmatic source areas through geochemical characteristics of magmatic rocks. This approach allows discrimination of tectonic setting of a given suite of magmatic rocks (basalt, granite, etc.) using whole-rock geochemical data including major and trace elemental and isotopic compositions. As a new application of artificial intelligence technique in the field of geochemistry, machine learning discrimination algorithm has gradually become a research tool supplementary to the classical discrimination diagram approach. However, the algorithms classification accuracy is affected by two main factors: high-dimensional data feature screening and multiple unknown parameter determination. To this end, we propose here a coupling discrimination method involving improved genetic algorithm and optimized neural network (GA-NNDM), based on genetic algorithm (GA) and neural network discrimination method (NNDM). The proposed method uses the feedback links between feature selection, parameter determination and classification performance. It treats classification accuracy as a fitness function and seeks the best feature subset and unknown parameters through iterative evolution. As a result, data features are reduced, unknown parameters are optimized and classification performance is improved. In addition, according to the published geochemical data of basalt samples, vertical and horizontal comparison experiments are set up through K-fold cross-validation method to verify accuracy, stability and extensibility of GA-NNDM in the application of basalt tectonic setting discrimination. Simulation results show that GA-NNDM has an excellent discrimination effect and generalization ability, with the overall classification accuracy near 90%. We conclude that, as a whole, GA-NNDM can be applied widely in geochemistry.