基于时空注意力克里金的边坡形变数据插值方法  

Spatio-Temporal Attention-based Kriging for Land Deformation Data Interpolation

作　　者：黎嵘繁 钟婷[1] 吴劲 周帆[1] 匡平 LI Rong-fan;ZHONG Ting;WU Jin;ZHOU Fan;KUANG Ping(School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China)

机构地区：[1]电子科技大学信息与软件工程学院,成都610054

出　　处：《计算机科学》2022年第8期33-39,共7页Computer Science

基　　金：国家自然科学基金(62072077);国家重点研发计划(2019YFB1406202);四川省科技计划项目专项资金(2020YFG0234)。

摘　　要：山体滑坡每年都会对人们的生命财产安全造成重大损失,是常见的地质灾害之一。为了对山体滑坡进行防控,需要广泛地监测山体表面的沉降过程,但是由于恶劣气候和监测成本等难以克服的困难,山体沉降数据的收集呈现出局部数据不完整、数据采样不均衡和监测点动态变化等特点,使得山体滑坡的防控研究受到阻碍,给数据的采集和分析工作提出了新的要求。现有方法从空间角度对缺失进行补充,但忽略了时间维度的依赖关系。为了解决上述问题,对不完整的INSAR数据填充进行了研究,利用时空掩码矩阵对时空依赖关系进行建模,利用多头注意力对多层次的空间关系进行综合学习,并在克里金法(Kriging)的基础上提出了新的使用时空注意力的克里金插值法,实现了对复杂时空特征的深层理解。在真实数据集上的数据恢复实验验证了该算法可以有效地学习复杂的时空特征,并在3种不同的数据缺失情景下都取得了优于现存插值算法的表现。Landslide is one of the most common geological hazards,it causes significant damage to people’s life and property everyyear.In order to prevent and control landslides,it is necessary to monitor the land surface extensively.However,insurmountable difficulties such as severe climate and high monitoring cost impede the collection of land surface data,resulting in incomplete local data,unbalanced data sampling and dynamic changes of monitoring points,which hinder the prevention and control research of landslide and put forward new demand to the data collection and analysis.Existing methods try to handle incomplete data from spatial perspective,which,however,ignore temporal dependencies that are important for data interpolation.To solve the above problems,the incomplete INSAR data filling is studied,the spatio-temporal dependence is modeled by using the spatio-temporal mask matrix,the multi-level spatial relationship is comprehensively studied by using multi-head attention,and a novel Kriging interpolation method using spatio-temporal attention is proposed on the basis of Kriging.It realizes the deep understanding of complex temporal and spatial features.Interpolation experiments on real-world INSAR datasets show that the proposed model is capable to learn sophisticated spatial and temporal features effectively,and achieves better performance than the state-of-the-art methods in three different data interpolation scenarios.

关 键 词：时空数据挖掘 时空注意力 克里金 山体滑坡 插值法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]