在地球物理卫星数据上使用社区检测预测山体滑坡
Vrinda D Desai1, Farnaz Fazelpour1, Alexander L Handwerger2
1Physics Department, North Carolina State University, Raleigh, North Carolina 27695, USA.
Physical review. E
|August 16, 2023
概括
现在可以使用网络科学来预测灾难性的山体滑坡. 该方法分析地面变形模式,在故障发生之前识别风险区域.
科学领域:
- 地质物理学 地质物理学
- 网络科学 网络科学
- 地质危险评估项目
背景情况:
- 自然的山坡容易因极端天气事件 (如大雨) 而发生剧烈的故障.
- 预测从逐渐的地面变形 (爬行) 过渡到突然的,灾难性的斜坡故障仍然是一个重大挑战.
- 失败前的变形模式往往只在回顾时才明显,阻碍了及时的风险评估.
研究的目的:
- 通过网络科学方法,研究发生山体滑坡之前的时空变形模式.
- 为灾难性斜坡故障开发一个预测指标.
- 识别和评估失败和爬行滑坡的风险.
主要方法:
- 将卫星雷达数据 (InSAR) 和数字海拔模型数据转化为空间嵌入式网络.
- 使用多层模块化优化来识别地面变形的紧密连接的社区 (集群).
- 根据局部斜率和地面表面变形权重网络边缘,以表示故障易感性和风湿状况.
主要成果:
- 成功确定了泥溪滑坡的位置和附近的爬行式滑坡.
- 开发了一个"社区持久性"指标,以量化导致故障的变形模式.
- 观察到,在泥溪山体滑坡发生前几周,社区的坚持显著增加.
结论:
- 网络科学方法,特别是多层模块化优化,对山体滑坡预测有希望.
- 社区持久度指标可以量化故障前的地面变形模式.
- 这种方法提供了一种新的技术,用于突出高风险的地区灾难性的斜坡失败.
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