New Maps of Lunar Surface Oxide Abundances and Mg# Using an Optimized Ensemble Learning Algorithm
The oxide abundance and Mg# (Mg/(Mg + Fe)) of the lunar surface are critical for understanding the Moon's petrology and evolution. Previous studies primarily relied on a single remote sensing data source and traditional regression algorithms for lunar oxide abundance inversion, potentiall...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10919024/ |
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| author | Chaofa Bian Kefei Zhang Yunzhao Wu Suqin Wu Yu Lu Yabo Duan Huajing Wu Zhenxing Zhao Wei Wu |
| author_facet | Chaofa Bian Kefei Zhang Yunzhao Wu Suqin Wu Yu Lu Yabo Duan Huajing Wu Zhenxing Zhao Wei Wu |
| author_sort | Chaofa Bian |
| collection | DOAJ |
| description | The oxide abundance and Mg# (Mg/(Mg + Fe)) of the lunar surface are critical for understanding the Moon's petrology and evolution. Previous studies primarily relied on a single remote sensing data source and traditional regression algorithms for lunar oxide abundance inversion, potentially compromising the accuracy of chemical composition results. In this study, multisource data, specifically KAGUYA multiband imager (MI) data and Diviner Christiansen feature (CF) products, referred to as MI-CF, combined with an ensemble learning algorithm optimized by improved particle swarm optimization (IPSO), were utilized to produce new maps of six oxides (FeO, TiO<sub>2</sub>, Al<sub>2</sub>O<sub>3</sub>, CaO, MgO, and SiO<sub>2</sub>) and Mg# at a resolution of 59 m/pixel within the Moon's 65° N/S range. The models were constructed using oxide abundances measured from lunar samples collected by the Apollo, Luna, and Chang'E missions. Among the models tested, the SXL algorithm (stacking of support vector machine regression, extreme gradient boosting, and linear regression), which was selected from a stack of 2 or 3 out of six typical algorithms, achieved the highest inversion accuracy. The SXL algorithm's accuracy was further enhanced using IPSO with nonlinear weights and learning factors, resulting in a significant improvement over previous studies. The results confirm that the IPSO-SXL approach effectively improves the accuracy of lunar oxide inversion. Furthermore, the study demonstrates that using MI-CF data together yields better results than using MI or CF data alone. The oxide and Mg# distributions in three craters from different geological units—Highlands, SPA basin, and Maria—indicate different magmatic evolution processes, providing new insights into lunar geological evolution and volcanism. |
| format | Article |
| id | doaj-art-843d0ab5d3dc4222b3dcd7f19bec4132 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-843d0ab5d3dc4222b3dcd7f19bec41322025-08-20T03:19:12ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01189119913410.1109/JSTARS.2025.354969110919024New Maps of Lunar Surface Oxide Abundances and Mg# Using an Optimized Ensemble Learning AlgorithmChaofa Bian0https://orcid.org/0000-0001-6979-349XKefei Zhang1https://orcid.org/0000-0001-9376-1148Yunzhao Wu2https://orcid.org/0000-0001-8408-1204Suqin Wu3https://orcid.org/0000-0002-0994-402XYu Lu4https://orcid.org/0000-0003-1884-1724Yabo Duan5https://orcid.org/0009-0002-8370-1277Huajing Wu6https://orcid.org/0000-0002-7248-680XZhenxing Zhao7Wei Wu8School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaPurple Mountain Observatory, Chinese Academy of Sciences, Nanjing, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaPurple Mountain Observatory, Chinese Academy of Sciences, Nanjing, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaState Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, ChinaSchool of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang, ChinaThe oxide abundance and Mg# (Mg/(Mg + Fe)) of the lunar surface are critical for understanding the Moon's petrology and evolution. Previous studies primarily relied on a single remote sensing data source and traditional regression algorithms for lunar oxide abundance inversion, potentially compromising the accuracy of chemical composition results. In this study, multisource data, specifically KAGUYA multiband imager (MI) data and Diviner Christiansen feature (CF) products, referred to as MI-CF, combined with an ensemble learning algorithm optimized by improved particle swarm optimization (IPSO), were utilized to produce new maps of six oxides (FeO, TiO<sub>2</sub>, Al<sub>2</sub>O<sub>3</sub>, CaO, MgO, and SiO<sub>2</sub>) and Mg# at a resolution of 59 m/pixel within the Moon's 65° N/S range. The models were constructed using oxide abundances measured from lunar samples collected by the Apollo, Luna, and Chang'E missions. Among the models tested, the SXL algorithm (stacking of support vector machine regression, extreme gradient boosting, and linear regression), which was selected from a stack of 2 or 3 out of six typical algorithms, achieved the highest inversion accuracy. The SXL algorithm's accuracy was further enhanced using IPSO with nonlinear weights and learning factors, resulting in a significant improvement over previous studies. The results confirm that the IPSO-SXL approach effectively improves the accuracy of lunar oxide inversion. Furthermore, the study demonstrates that using MI-CF data together yields better results than using MI or CF data alone. The oxide and Mg# distributions in three craters from different geological units—Highlands, SPA basin, and Maria—indicate different magmatic evolution processes, providing new insights into lunar geological evolution and volcanism.https://ieeexplore.ieee.org/document/10919024/Chang'e-5 (ce-5)christiansen feature (CF)improved ensemble learning algorithmKAGUYA multiband imager (MI)lunar oxide |
| spellingShingle | Chaofa Bian Kefei Zhang Yunzhao Wu Suqin Wu Yu Lu Yabo Duan Huajing Wu Zhenxing Zhao Wei Wu New Maps of Lunar Surface Oxide Abundances and Mg# Using an Optimized Ensemble Learning Algorithm IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Chang'e-5 (ce-5) christiansen feature (CF) improved ensemble learning algorithm KAGUYA multiband imager (MI) lunar oxide |
| title | New Maps of Lunar Surface Oxide Abundances and Mg# Using an Optimized Ensemble Learning Algorithm |
| title_full | New Maps of Lunar Surface Oxide Abundances and Mg# Using an Optimized Ensemble Learning Algorithm |
| title_fullStr | New Maps of Lunar Surface Oxide Abundances and Mg# Using an Optimized Ensemble Learning Algorithm |
| title_full_unstemmed | New Maps of Lunar Surface Oxide Abundances and Mg# Using an Optimized Ensemble Learning Algorithm |
| title_short | New Maps of Lunar Surface Oxide Abundances and Mg# Using an Optimized Ensemble Learning Algorithm |
| title_sort | new maps of lunar surface oxide abundances and mg using an optimized ensemble learning algorithm |
| topic | Chang'e-5 (ce-5) christiansen feature (CF) improved ensemble learning algorithm KAGUYA multiband imager (MI) lunar oxide |
| url | https://ieeexplore.ieee.org/document/10919024/ |
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