Evolution state identification of deep landslide displacement based on a quadratic wavelet reconstruction and bispectrum analysis method

Deformation monitoring has been proven to be an effective way to forecast and mitigate landslide geohazards. With the development of monitoring technology and equipment, the GPS technology have been widely adopted in landslide surface displacement monitoring, and borehole inclinometer methods are often used to measure deep displacements. However, for landslides with large and abrupt deformations, a large amount of landslide deep displacement data can hardly be processed by traditional methods because of the shearing failures of inclinometers, which cause serious data redundancy. Considering the time-frequency characteristics of deep displacement data obtained from typical rainfall-reservoir induced landslides in China Three Gorges Reservoir (CTGR) area, a quadratic wavelet reconstruction and bispectrum analysis (QWRBA) method is designed for feature extraction and landslide state classification. During this process, two wavelet decompositions are first used to decompose the input deep displacement data into components with different physical meanings. Then, some reconstructed components and non-reconstructed components are analysed with a bispectrum. The deep displacement bispectrum features generated by the bispectrum analysis of each component are fused to obtain the eigenvalues of these bispectrum features, and the eigenvalues of the fused bispectrum features are used as the characteristic landslide deep displacement data. By utilizing the fused bispectrum features as the inputs of an adaptive moment estimation-based convolutional neural network (CNN), different deep displacement conditions are recognized as corresponding deformation states.