摘要
灰色模型可在"贫信息"的条件下发现变形观测数据中的规律,但不能去除数据中噪声的影响;而小波分析可有效识别并剔除变形监测数据中的噪声(误差),使变形规律更加明显;结合两种方法的优势能增强数据分析的可靠性,提高预测精度。将小波去噪与灰色模型相结合,研究了该分析方法在高铁隧道变形监测中的应用,并得到了较可靠的预测结果。
The gray model can find the law in the deformation observation data under the condition of"Poor Information".However,it can’t remove the influence of noise in the data.Wavelet analysis can effectively identify and eliminate the noise(error)in the deformation monitoring data and make the deformation law more obvious.Combining the advantages of two methods can enhance the reliability of data analysis and improve prediction accuracy.In this paper,we combined wavelet denoising with gray model,studied the application of this analysis method in deformation monitoring of high speed railway tunnel,and obtained more reliable prediction results.
出处
《地理空间信息》
2021年第5期85-88,I0011,共5页
Geospatial Information
基金
国家自然科学基金资助项目(41201193)。
关键词
小波去噪
灰色模型
高铁隧道沉降
变形预测
wavelet denoising
gray model
high speed railway tunnel settlement
deformation prediction
