基于小波去噪的地铁变形组合预测模型分析

对地铁监测数据建立相应的预测模型，对变形可进行前瞻性预测，从而保证地铁安全的施工和运营。本文以北京市地铁某基坑工程为研究对象，首先以某一监测点为例，利用小波分析对原始监测数据进行去噪处理；然后分别利用时间序列分析模型和BP神经网络模型对去噪后的数据进行建模分析，得到原数据的拟合值和对未来变形的预测值；最后利用同期Sentinel-1A卫星影像进行相干点时序InSAR处理，得到形变结果。通过分析两个模型的预测值与实际值，并与InSAR结果进行对比，验证了两个预测模型在地铁形变监测中应用的优劣性。

Analysis of combined prediction model of subway deformation based on wavelet denoising

By analyzing the subway monitoring data and establishing the corresponding prediction model, the possible deformation in the future can be prospectively predicted, so as to ensure the safe construction and operation of the subway. Taking the foundation pit project in Beijing as an example, the deformation monitoring scheme is introduced. Taking a monitoring point as an example, the wavelet analysis is used to de-noising the original monitoring data of a monitoring point. The time series analysis model and BP neural network model are respectively used to model and analyze the de-noised data, and the fitting value of the original data and the predicted value of the future deformation are obtained. The deformation results are obtained by using the coherent point timing InSAR Sentinel-1A satellite images at the same time. Finally, by analyzing the predicted value and the actual value of the two models, comparing with InSAR results, the advantages and disadvantages of the two models in the application of subway deformation monitoring data can be compared.