基于无人机斜摄像度的区块挖掘方法诱导的表面沉降的预测方法
Weijia Ling1, Xinglong Feng2,3, Liguan Wang2,4
1School of Resources and Environment and Safety Engineering, University of South China, Hengyang, 421001, China.
Scientific reports
|October 20, 2024
概括
这项研究引入了一种使用无人机 (UAV) 数据进行矿山表面沉降的新预测模型. 该模型准确地预测了沉降高度和面积,提高了采矿安全.
科学领域:
- 地质科学 地质科学
- 采矿工程 采矿工程 采矿工程
- 地质技术工程 地质技术工程
背景情况:
- 无人驾驶飞行器 (UAV) 斜摄影仪在采矿中广泛用于3D重建和变化检测.
- 目前在采矿领域的无人机应用缺乏对表面沉降的预测能力.
- 表面沉降在采矿操作中构成重大危险,特别是在区块挖掘方法中.
研究的目的:
- 使用无人机斜摄像度数据开发表面沉降的预测模型.
- 为了预测最大的表面沉降高度和块洞穴下受影响的区域.
- 加强无人机技术在采矿中的应用,以减轻危险.
主要方法:
- 利用无人机斜摄像度的3D现实场景模型数据.
- 开发了一种两阶段预测模型,将概率积分方法与循环神经网络 (PIMF-RNN) 合并.
- 使用普朗铜矿 (2018-2020) 的地表数据验证了模型.
主要成果:
- 对最大表面沉降高度实现了91.47%的预测准确度.
- 实现了87.52%的表面沉降面积预测准确度.
- 证明了该模型在减轻内部和外部因素对沉降的影响方面的有效性.
结论:
- PIMF-RNN模型有效地预测了采矿中的表面沉降高度和面积.
- 无人机斜摄像度,结合先进的建模,在采矿领域提供了扩展的应用.
- 这项研究建立了一个具有成本效益和高效的程序,用于预测矿井表面沉降和减轻相关危险.
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