地球信息科学学报 ›› 2018, Vol. 20 ›› Issue (11): 1579-1591.doi: 10.12082/dqxxkx.2018.180289

所属专题: 气候变化与地表过程

• 地球信息科学理论与方法 • 上一篇    下一篇

基于GLM-PSO-coKriging模型的地表形变研究

牛腾1(), 岳德鹏1(), 李倩2, 于强1, 于佳鑫1, 方敏哲1   

  1. 1. 北京林业大学精准林业北京市重点实验室,北京 100083
    2. 秦皇岛市林业局,秦皇岛 066004
  • 收稿日期:2018-06-15 修回日期:2018-09-11 出版日期:2018-11-20 发布日期:2018-11-20
  • 作者简介:

    作者简介:牛 腾(1995-),男,石家庄人,硕士生,从事3S技术在资源环境中的应用研究。E-mail: niuteng21@bjfu.edu.cn

  • 基金资助:
    中央级公益性科研院所基本科研业务费专项资金项目 (CAFYBB2017MB026);“十二五”国家科技支撑计划项目(2012BAD16B00)

Research on Surface Deformation Based on GLM-PSO-coKriging Model

NIU Teng1(), YUE Depeng1(), LI Qian2, YU Qiang1, YU Jiaxin1, FANG Minzhe1   

  1. 1. Beijing Key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China
    2. Forestry bureau of Qinhuangdao, Qinhuangdao 066004, China
  • Received:2018-06-15 Revised:2018-09-11 Online:2018-11-20 Published:2018-11-20
  • Supported by:
    Special Fund for Basic Research Business Expenses of Central-level Public Welfare Research Institutes(CAFYBB2017MB026);National Science and Technology Support Program of China, No.2012BAD16B00.

摘要:

以斜坡灾害易发地带兰州市城关区为研究区,通过PS-InSAR技术提取地表形变点的地表形变速率,反映地质灾害在空间范围内的分布。以协克里金插值为基础,结合广义线性模型(GLM)和粒子群优化算法(PSO),构建PSO-GLM-coKriging插值模型,以地表形变速率为主变量,DEM,岩土疏松度和NDVI拟合参数为协变量,进行空间插值模拟。与co-Kriging模型和GLM-co-Kriging模型相比较,PSO-GLM-coKriging插值模型具有更高的精度和更好的模拟效果,消除了多维度产生的复杂度,改善了小尺度范围内的插值效果,3个模型的误差分别为1.25 mm/year,0.70 mm/year,0.47 mm/year。因此,通过PSO-GLM插值模型对形变点空白区的插值模拟,对城关区城镇化的规划建设提供一定的数据和理论支撑。

关键词: PS-InSAR技术, 地质灾害, 地表形变速率, 协克里金插值, PSO-GLM-coKriging插值模型

Abstract:

Taking the Chengguan District of Lanzhou City as a research area in the slope disaster-prone area, the surface deformation rate of surface deformation points is extracted by PS-InSAR technology, and the deformation rate can effectively reflect the distribution and uplifting of geological disasters in the spatial range. Based on the coKriging interpolation, combined with the generalized linear model (GLM) and the particle swarm optimization (PSO) algorithm, the coKrigong interpolation is optimized by fitting the linear model to construct the PSO-GLM-coKriging interpolation model to the surface deformation rate. The main variables, DEM, geotechnical porosity and NDVI fitting parameters were covariates, and spatial interpolation simulations were performed. Compared with the co-Kriging model and the GLM-co-Kriging model, the PSO-GLM-coKriging interpolation model has higher precision and better simulation effect, eliminating the complexity of multi-dimensional generation and improving the small-scale range. Interpolation effect, the error of the three models is 1.25mm/year, 0.70mm/year, 0.47mm/year. By comparison, the PSO-GLM-coKriging interpolation model has higher simulation accuracy and better simulation results. In the overall range, the interpolation results of the three models are similar in spatial distribution, in line with the actual situation of the surface. Therefore, the interpolation simulation of the blank area of ??the deformation point is carried out by the PSO-GLM interpolation model to fill the gap that the PS-InSAR technology can not extract the surface information at the non-deformation point, and the ground subsidence and uplift with sudden degeneration and sudden landslides will be completed. Geological disasters have been effectively combined, and the coupling relationship between geological disasters with high uncertainty and the monitoring of surface deformation can be established, which provides certain data and theoretical support for the planning and construction of urbanization in Chengguan District.

Key words: PS-InSAR, geological disaster, surface deformation rate, co-Kriging, PSO-GLM-coKriging interpolation model