Skip to Main Content
Skip Nav Destination
GEOREF RECORD

Unraveling clay-mineral genesis and climate change on Earth and Mars using machine learning-based VNIR spectral modeling

Zhao Lulu, Deng Anbei, Hong Hanlie, Zhao Jiannan, Thomas J. Algeo, Liu Fuxing, Luozhui Nanmujia and Fang Qian
Unraveling clay-mineral genesis and climate change on Earth and Mars using machine learning-based VNIR spectral modeling
American Mineralogist (February 2025) 110 (2): 217-231

Abstract

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.


ISSN: 0003-004X
EISSN: 1945-3027
Coden: AMMIAY
Serial Title: American Mineralogist
Serial Volume: 110
Serial Issue: 2
Title: Unraveling clay-mineral genesis and climate change on Earth and Mars using machine learning-based VNIR spectral modeling
Affiliation: China University of Geosciences, School of Earth Sciences, Laboratory of Biogeology and Environmental Geology, Hubei Laboratory of Critical Zone Evolution, Wuhan, China
Pages: 217-231
Published: 202502
Text Language: English
Publisher: Mineralogical Society of America, Washington, DC, United States
References: 163
Accession Number: 2025-025470
Categories: Mineralogy of silicatesExtraterrestrial geology
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 1 table, geol. sketch map
N29°00'00" - N40°00'00", E100°00'00" - E120°00'00"
Secondary Affiliation: China Geological Survey, CHN, China
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2025, American Geosciences Institute. Abstract, copyright, Mineralogical Society of America. Reference includes data from GeoScienceWorld, Alexandria, VA, United States
Update Code: 2025
Close Modal

or Create an Account

Close Modal
Close Modal