压缩感知理论在地学空间数据重构中的应用进展  

Progression of Compressive Sensing Applied in Geoscience Spatial Data Reconstruction

作　　者：王灿[1,2] 李晓鹏 宣可凡[1,2] 蒋一飞 纪景纯 贾仁浩 刘建立 WANG Can;LI Xiaopeng;XUAN Kefan;JIANG Yifei;JI Jingchun;JIA Renhao;LIU Jianli(Institute of Soil Science,Chinese Academy of Sciences,Nanjing 210008,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院南京土壤研究所,南京210008 [2]中国科学院大学,北京100049

出　　处：《土壤》2022年第2期232-239,共8页Soils

基　　金：国家自然科学基金项目(42177302、41771265、41877021)资助。

摘　　要：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空间数据重构方法在地学领域的研究进展,分析了该方法在土壤特性空间数据重构中的可行性,并提出了几点潜在的研究方向。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 briefly describes mathematical theory of compressive sensing,expounds the research progress of spatial data reconstruction method based on this theory in Geoscience,analyzes the feasibility of this method in spatial data reconstruction of soil characteristics,and puts forward some potential research directions.

关 键 词：压缩感知 空间数据 土壤特性 

分 类 号：S111[农业科学—农业基础科学]