基于普适型监测预警体系中多参数模型研究——以云南省保山市大园子滑坡为例
Research on multi-parameter model based on universal monitoring and early warning system:Taking the Dayuanzi landslide in Baoshan City,Yunnan Province as an example
孙金辉 1佘涛1
作者信息
- 1. 中国地质调查局地质灾害防治技术中心,四川 成都 610081;中国地质科学院探矿工艺研究所,四川 成都 611734
- 折叠
摘要
为全面发展"人防+技防"的地质灾害防治技术体系,构建以普适型监测仪器为主的地质灾害监测预警体系,以云贵高原地区中的云南省保山市大园子滑坡为例,详细介绍了滑坡从方案设计到数据筛选及模型设计等各环节,主要选取降雨、位移变形和裂缝三类参数模型在监测预警体系中的应用,通过采集地质灾害体的关键信息,优化监测预警模型及调整阈值,及时捕捉地质灾害体的变化信息,依靠平台判据模型自主触发预警,完成了普适型监测预警体系建设的关键环节,从而实现了普适型监测预警的效果,极大的提升了地质灾害防治体系的自动化和智能化能力.
Abstract
In order to comprehensively develop the geological disaster prevention and control technology system of"civil air defense+technical defense",and build a geological disaster monitoring and early warning system based on universal monitoring instruments,this paper takes the Dayuanzi landslide in Baoshan City,Yunnan-Guizhou Plateau as an example,introduces the various links of the geological disaster landslide from scheme design to monitoring data screening and mode model design in detail,and the application of three parameters model in the monitoring and early warning system namely rainfall,displacement deformation and crack are expouneded.Through collecting key information of the geological disaster body,such as rainfall,displacement deformation and crack change,the monitoring and early warning model is optimizd,the threshold is adjusted,and the change information of the geological disaster body is captured in time.Moreover,the key link of building a universal monitoring and early warning system is completed relying on the self triggered early warning of the platform criterion model,thereby,the effect of universal monitoring and early warning is achieved and greatly improved the automation and intelligence of the geological disaster prevention system.
关键词
地质灾害监测/普适型监测/监测预警体系/多参数模型/大园子滑坡Key words
geological disaster monitoring/universal monitoring/monitoring and early warning system/multi-parameter model/Dayuanzi landslides引用本文复制引用
基金项目
中国地调局地质调查项目(DD20230087)
出版年
2024
国家科技期刊平台
NSTL
万方数据