压缩感知理论在地学空间数据重构中的应用进展

空间数据重构是根据离散、稀疏的点位数据构建介质属性完整空间分布的过程,地学领域中通常采用基于地质统计学的方法.压缩感知是21世纪信号处理领域的重大理论突破,地学领域的学者将其作为一种空间数据重构的新方法,在流体运动模型的静态参数反演和土力学性质重构中取得了良好效果.本文在简述压缩感知数学理论的基础上,阐述了基于该理论的...

Progression of Compressive Sensing Applied in Geoscience Spatial Data Reconstruction

Spatial data reconstruction is a process of constructing complete spatial distribution of media attributes based on discrete and sparse point data. Methods based on geostatistics are commonly used in the field of Geosciences. Compressive sensing is a major theoretical breakthrough in the field of signal processing in the 21st century, and has been regarded as a new method of spatial data reconstruction by scholars in the field of Geosciences. This method has achieved good results in static parameter inversion of flow models and the reconstruction of soil mechanical properties. This paper will briefly describe mathematical theory of compressive sensing, expound the research progress of spatial data reconstruction method based on this theory in Geoscience, analyze the feasibility of this method in spatial data reconstruction of soil characteristics, and put forward some potential research directions.