New insight into the composition of the lunar surface from SiO2 map

The distribution of FeO and TiO 2 reveals mantle source characteristics and basaltic diversity, whereas SiO 2 distribution is closely related to magma evolution and crustal differentiation. However, global studies on SiO 2 remain limited. To address this gap, we combined the latest lunar sample data, Kaguya Multiband Imager spectra, and Christiansen Feature to develop a One-Dimensional Convolutional Neural Network (1D-CNN) for mapping lunar SiO 2 . The results reveal a clear spatial asymmetry in SiO 2 : high-latitude regions on the nearside show higher SiO 2 , while low-latitude regions exhibit lower values. Mare Tranquillitatis and Oceanus Procellarum have low SiO 2 but distinct TiO 2 variations, indicating different basaltic types and magmatic sources. The highlands are dominated by ferroan anorthosite, whereas the maria mainly consist of mafic to ultramafic rocks. Incorporating Chang’E-6 samples and Christiansen Feature data improved model accuracy. Future work on silicic volcanic rocks will further refine the global SiO 2 map and deepen understanding of lunar crustal evolution.

Authors

Institutions

Publication Details

Journal
Frontiers in Astronomy and Space Sciences
Published
2025-11-14
DOI
https://doi.org/10.3389/fspas.2025.1696995
Primary Topic
Planetary Science and Exploration
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

New insight into the composition of the lunar surface from SiO2 map

Wencong Chen, Jianguo Yan, Denggao Qiu, Sihai Yi
Frontiers in Astronomy and Space Sciences
Planetary Science and Exploration
article

New insight into the composition of the lunar surface from SiO2 map

Wencong Chen, Jianguo Yan, Denggao Qiu, Sihai Yi
article en

Abstract

The distribution of FeO and TiO 2 reveals mantle source characteristics and basaltic diversity, whereas SiO 2 distribution is closely related to magma evolution and crustal differentiation. However, global studies on SiO 2 remain limited. To address this gap, we combined the latest lunar sample data, Kaguya Multiband Imager spectra, and Christiansen Feature to develop a One-Dimensional Convolutional Neural Network (1D-CNN) for mapping lunar SiO 2 . The results reveal a clear spatial asymmetry in SiO 2 : high-latitude regions on the nearside show higher SiO 2 , while low-latitude regions exhibit lower values. Mare Tranquillitatis and Oceanus Procellarum have low SiO 2 but distinct TiO 2 variations, indicating different basaltic types and magmatic sources. The highlands are dominated by ferroan anorthosite, whereas the maria mainly consist of mafic to ultramafic rocks. Incorporating Chang’E-6 samples and Christiansen Feature data improved model accuracy. Future work on silicic volcanic rocks will further refine the global SiO 2 map and deepen understanding of lunar crustal evolution.

Frontiers in Astronomy and Space SciencesVol. 12
North China Institute of Science and Technology (CN), Chinese Academy of Sciences (CN), Xinjiang Astronomical Observatory (CN), Wuhan University (CN), State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing (CN)
Openalex Percentile: Top 76%
Planetary Science and Exploration
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.