中文摘要：

地学特征因子的提取是定量化数学地质分析的重要基础，可以为地貌类型识别提供有效的客观依据。基于谱矩分析，本文提出了一种描述表面数据粗糙程度的特征因子，并且分析了新特征因子的特点和其应用可能性。该方法以随机过程理论为基础，通过计算表面各阶谱矩以及相应的统计不变量来描述三维表面形貌的特征。以中国卫星重力测量数据和DEM数据为例，试验该方法运用于地貌类型识别的效果。理论模型数据与实际数据结果均表明，基于谱矩的新的地学特征因子不仅可以有效地反映数据起伏与变异特征，而且提取出的特征可以为地貌及重力构造单元划分提供客观依据。

英文摘要：

Geo-science features extraction plays an important role in the quantitative analysis of mathematical geology, which provides an objective basis for identifying the types of surface units. In this paper, a new type of Geo-science features is defined to characterize the roughness of surfaces. In addition, the mathematical characteristic of the new feature is discussed. The new method relies on the theory of stochastic processes. Both the spectrum moments and its statistical invariants are considered as the indicators for characterizing the surface physiography. The experiments are performed on both the satellite gravity data in China and Digital Elevation Model （DEM） data. And the process of geomorphology recognition and steps for the calculation process are demonstrated. The results show that the new feature can reflect the relief characteristics of landforms, and the method can also be applied to create Gravity tectonic units and segmentation-based maps of physiography.