Modeling hydrous mineral distribution on Mars with extremely sparse data: A multi-scale spatial association modeling framework

Abstract

Identifying minerals on Mars is crucial for finding evidence of water on the planet. Currently, spectral inversion methods based on remote sensing data are primarily used; however, they only provide sparse and scattered maps of mineral exposures. To address this limitation, we propose a multi-scale spatial association modeling framework (MSAM) that couples the geographical distribution of Martian hydrous minerals with environmental factors based on the existence of spatial dependence, to achieve dense and continuous mapping of hydrous minerals. Our approach leverages explanatory variables – such as elevation, slope, and aspect – to establish spatial associations with potential areas of hydrous minerals, selected via multiscale search ranges from existing hydrous mineral exposures. These association results are used to identify potential hydrous mineral locations and estimate probabilities for potential hydrous mineral points. High-probability points are then combined with known exposures, and Kriging interpolation is applied to produce a continuous surface map. Finally, the interpolation results are evaluated using geomorphological maps, along with correlation analysis. The proposed MSAM enhances prediction accuracy and addresses the challenges of incomplete detection and undetected areas inherent in remote sensing-based spectral inversion. Results reveal that incorporating environmental factors reduces the RMSE by 25% and improves spatial correlation by 30% compared to traditional interpolation techniques. An overlay analysis intersecting the interpolated results with geomorphologic features obtained through semantic segmentation further demonstrates a coupling relationship between hydrous minerals and geomorphologic features within a specific spatial range.