通过图形卷积网络进行多点关系融合的预测模型:关于采矿诱导的表面沉降的案例研究
Baoxing Jiang1,2,3, Kun Zhang1,2, Xiaopeng Liu1,2
1State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Huainan, China.
PloS one
|August 16, 2023
概括
一个新的多点关系融合图卷积网络 (MRF-GCN) 模型准确地预测了采矿引起的表面沉降. 这种先进的模型优于传统方法,为大规模的表面变形分析提供了更高的准确性.
科学领域:
- 地质科学 地质科学
- 采矿工程 采矿工程 采矿工程
- 遥感 遥感 遥感 遥感
背景情况:
- 准确预测采矿引起的地表沉降对于基础设施安全和环境管理至关重要.
- 现有的模型经常忽视沉降点之间的空间相关性,导致预测不准确.
- 该研究解决了对更强大的沉降预测模型的需求,这些模型可以考虑点际关系.
研究的目的:
- 提出和评估一种新的多点关系融合图卷积网络 (MRF-GCN) 模型,用于预测采矿引起的表面沉降.
- 通过结合观测点之间的相关性来提高表面沉降预测的准确性.
- 为了证明模型在现实世界采矿场景中的适用性.
主要方法:
- 利用来自Sentinel-1A和GNSS (全球导航卫星系统) 观测的Interferometric Synthetic Aperture Radar (INSAR) 数据进行表面变形分析.
- 采用长期短期记忆 (LSTM) 编码器来捕获单个点的变形模式.
- 开发了一个MRF-GCN模型,将点相关性集成到图形结构中,以提高预测.
主要成果:
- 该MRF-GCN模型实现了0.8650.0.2的高确定系数 (R2).
- 与传统模型相比,该模型的平均平方误差 (MSE) 显著降低,为1.59899 .
- MRF-GCN 显示出比标准的长短记忆 (LSTM) 和其他传统方法更高的预测准确度.
结论:
- 该MRF-GCN模型在预测采矿引起的表面沉降方面取得了重大进展.
- 该模型能够融合多点关系,从而提高大规模区域的预测准确性.
- 这种方法为分析和减轻与采矿引起的表面变形相关的风险提供了可靠的工具.
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