基于FastICA算法的高光谱矿物丰度反演

高光谱分离是定性与定量岩矿分析的核心基础。仿真数据和测量数据可通过FastICA算法处理,计算混合像元中端元数量及端元光谱,并根据计算结果进行丰度反演。实验结果表明：FastICA算法即使在端元光谱、混合矩阵未知且没有任何先验知识的情况下,也能有效分离岩矿高光谱混合像元并得到端元的估计光谱,从而实现矿物识别和矿物丰度反演;但当混合像元中参与混合的端元光谱相似度较高或端元光谱的非高斯性较低时,混合像元的分离精度将降低,混合像元的分离精度越高,端元丰度反演结果越准确。     

Prediction of load-displacement performance of grouted anchors in weathered granites using FastICA-MARS as a novel model

With the rising needs of better prediction of the load-displacement performance of grouted anchors in an era of developing large-scale underground infrastructures,the existing methods in literature lack an accurate analytical model for the real-life projects or rigorous understanding of the parameters such as grouting pressures.This paper proposes Fast ICA-MARS as a novel data-driven approach for the prediction of the load-displacement performance of uplift-resisting grouted anchors.The hybrid and data-driven Fast ICA-MARS approach integrates the multivariate adaptive regression splines(MARS)technique with the Fast ICA algorithm which is for Independent Component Analysis(ICA).A database of 4315 observations for 479 different anchors from 7 different projects is established.The database is then used to train,validate and compare the Fast ICA-MARS approach with the classical MARS approach.The developed Fast ICA-MARS model can provide more accurate predictions than MARS.Moreover,the developed Fast ICA-MARS model is easy to interpret since the evaluation of the parameter importance of the independent components can be conducted along with the considerations of the correlations with the original variables.It is noteworthy to point out that the grouting pressures play a central role in the proposed model,which is considered of paramount importance in engineering practices but has not been properly taken into account in any prior analytical or empirical predictive models for the load-displacement relationships.