¹Lulu Zhao,¹Anbei Deng; Hanlie Hong,²Jiannan Zhao,^1,3,4Thomas J. Algeo,¹Fuxing Liu,⁵Nanmujia Luozhui,¹Qian Fang
American Mineralogist 110, 217-231 Link to Article [https://doi.org/10.2138/am-2023-9299]
¹State Key Laboratory of Biogeology and Environmental Geology, Hubei Key Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
²Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China
³Department of Geosciences, University of Cincinnati, Cincinnati, Ohio 45221-0013, U.S.A.
⁴State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China
⁵Military-Civilian Integrated Geological Survey Center, China Geological Survey, Lhasa 850006, China
Copyright: The Mineralogical Society of America

Clay minerals are common in martian geological units and are globally widespread on Earth. Understanding the origin, formation, and alteration of clay minerals is crucial for unraveling past environmental conditions on Earth and Mars, in which the composition and crystallinity of clay minerals serve as important surrogate indicators for addressing these issues. Here, 621 soil and sediment samples from five chronosequences representing different climatic zones of China were investigated using visible to near-infrared reflectance (VNIR) in combination with X-ray diffraction (XRD) analysis. The crystallinity of clay minerals (i.e., illite crystallinity, illite chemistry index, kaolinite crystallinity) and clay mineral alteration index (CMAI) were analyzed with conventional methods and then predicted through a spectral modeling approach. Our results show that kaolinite with a pedogenic or sedimentary origin is characterized by a broad crystallinity range and a poorly ordered structure, especially when generated in an intense weathering environment. Predictive models were constructed with data-mining methods, including partial least-squares regression (PLSR), random forest (RF), and Cubist algorithms. The predictive performance of the crystallinity and CMAI proxies is robust, with an overall accuracy of 78% and a residual prediction deviation (RPD) of 2.57. We also found that the model’s accuracy in predicting clay-mineral-related proxies increased by 45% using random forest (RF) and Cubist compared to the PLSR models. We suggest that VNIR spectroscopy combined with RF and Cubist methods has the potential to be an alternative and broadly applicable tool for analyzing typical clay-mineral proxies, substituting for a series of common mineralogic analyses. Spectral modeling can reveal genetic and climatic information at both field and regional scales, which has profound implications for Mars missions and other space exploration programs.