卫星监测时空大数据蠕变特征提取及预警算法

Research on Creep Feature Extraction and Early Warning Algorithm Based on Satellite Monitoring Spatial-Temporal Big Data

针对山体滑坡等地质灾害发生时间、趋势难以及时精准预警的难题,提出采用最新北斗卫星高精度形变监测技术,开展蠕变运动特征提取及预警算法研究。对卫星监测高精度时空大数据进行分析、清洗,重点研究监测点数据的时间属性、空间属性、不同监测点之间的变化规律;提取蠕变运动多维特征,如位移、位移方向角、瞬间速度、加速度等,并以多维的方式展示监测数据内在的变化趋势。蠕变灾害预警算法能够发现和预警形变过程中的潜在灾害,确保及时防治地质灾害,保障人员生命和财产安全。该研究成果可以在多个不同领域得到广泛的应用,具有很大的理论意义和应用价值。

This paper presents a new methodology for overcoming the difficulties to issue warning of the occurrence time and tendency of the geologic hazards timely and accurately,employs the latest BeiDou satellite deformation monitoring technology,and considers a novel feature extraction of creep deformation method and algorithm for hazards warning.Based on the data analysis and data cleansing of the data from satellite monitoring spatial-temporal big data,we focus on time and spatial attribute and variation between different monitoring points.Moreover,we extract multi-dimensional of creep deformation such as displacement,displacement angle,instantaneous velocity,acceleration,etc.And the internal variation trend of the monitored data is displayed in a multi-dimensional manner.Research on creep disaster warning algorithm can find and warn potential disasters in deformation process,this finding is helpful to take measures to ensure the personal and property safety timely.Our research findings have important application value and theoretical significance in many fields.