使用改进的LSTM深度学习模型预测InSAR变形时间序列
Rupika Soni1, Mohammad Soyeb Alam2, Gajendra K Vishwakarma3
1Department of Mining Engineering, Indian Institute of Technology (ISM), Dhanbad, 826004, India.
Scientific reports
|February 13, 2025
概括
一个经过修改的LSTM模型使用InSAR数据准确地预测了矿井沉降情况. 与传统的RNN和LSTM方法相比,这种先进的模型提供了提高效率和减少误差,用于变形时间序列分析.
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:
- 采矿引发的沉降对采矿业务,利益相关者和环境构成重大风险.
- 有效的管理需要精确的监测和沉降预测.
- 干涉测量合成孔径雷达 (InSAR) 提供了有价值的变形时间序列数据.
研究的目的:
- 开发和评估一个修改的长短期记忆 (LSTM) 模型,用于预测INSAR变形时间序列.
- 评估模型的性能与现有的循环神经网络 (RNN) 和LSTM模型相比.
- 探索修改后的LSTM在一般时间序列预测方面的潜力.
主要方法:
- 一个修改后的LSTM (mLSTM) 模型被开发用于InSAR变形时间序列预测.
- 该模型使用来自印度Mine-A的26个TSX/TDX数据集进行了训练和测试.
- 通过将mLSTM预测与RNN和LSTM模型进行比较来评估性能,使用效率指标和根平均平方 (RMS) 误差.
主要成果:
- 修改后的LSTM模型实现了最高的预测效率 (98.57%) 和最低的RMS误差 (4.22毫米/年).
- 与RNN和LSTM相比,mLSTM的预测与观察到的变形速度值非常接近.
- 五年预测表明整体区域稳定,在工厂区域附近局部变形.
结论:
- 经过修改的LSTM模型在预测矿山沉降的INSAR变形时间序列方面表现出卓越的性能.
- 这种方法为矿山沉降监测和管理提供了更准确,更可靠的方法.
- mLSTM模型的功能可以扩展到地球科学中的其他时间序列预测应用.
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