一种基于SBAS-InSAR和STL-XGBoost的采矿区表面沉降预测方法
Jiachun Guo1, Chenfeng Li2,3
1College of Civil Engineering, Anhui Jianzhu University, Hefei, 230601, China.
Scientific reports
|October 31, 2025
概括
这项研究引入了一种新的STL-XGBoost模型,用于预测采矿区的表面沉降. 综合方法显著提高了预测准确性,为采矿业务提供了更好的风险管理.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 采矿造成的地表沉降对基础设施和安全构成风险.
- 准确的预测对于缓解至关重要,但由于复杂的时空模式而具有挑战性.
- 现有的模型往往无法有效地捕捉非线性动态和趋势.
研究的目的:
- 开发一个先进的表面沉降预测模型.
- 通过将季节性趋势分解与极端梯度增强树木相结合,提高预测准确度.
- 为矿区的表面沉降管理提供可靠的技术支持.
主要方法:
- 利用小基线子集交叉测量合成孔径雷达 (SBAS-InSAR) 来获取沉降数据 (2020-2023年).
- 应用季节性趋势分解 (STL) 来将沉降序列分为趋势和非趋势组件.
- 开发了一种混合STL-XGBoost模型,单独预测趋势和非趋势术语,并将预测结合起来.
主要成果:
- 该STL-XGBoost模型表现出优越的性能,超过单一的XGBoost.
- 平均绝对误差 (MAE) 减少了31%,根平均平方误差 (RMSE) 减少了38%.
- 预测结果显示与原始时间序列有很强的相关性 (>0.9),证实了高准确性.
结论:
- 拟议的STL-XGBoost模型有效地捕捉了表面沉降的复杂时空特征.
- 这种混合方法在预测准确性和可靠性方面提供了显著的改进.
- 该方法提供了强大的技术支持,以减轻与采矿引起的表面沉降相关的风险.
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