小波理论在大坝安全监测数据粗差探测中的应用

Application of Wavelet Theory in Detection of Gross Errors in Dam Safety Monitoring Data

针对大坝变形监测数据,选取合适的小波基函数,利用小波分析理论对变形监测数据进行粗差探测,研究了大坝变形安全监测数据在小波分解高频系数下的细节特征和突变点的关系。通过编程实现快速高效地剔除多个粗差,提高监测数据的质量,为数据的进一步分析提供较好的前期处理工作。建立大坝监测拟合预测模型,可以使拟合与预测精度更高,具有更好的实用价值。

According to the dam deformation monitoring data,the paper selects the appropriate wavelet basis function,uses the wavelet analysis theory to detect the gross error of the deformation monitoring data,and studies relationship between the detailed features and mutation points of the dam deformation safety monitoring data under high frequency coefficients of wavelet decomposition.Through programming,multiple gross errors can be eliminated quickly and efficiently,thus to improve the quality of monitoring data and provide better preliminary processing for further data analysis.Establishing a dam monitoring fitting prediction model can improve the fitting and prediction accuracy which is of significant practical value.