A multi-feature fusion transfer learning method for displacement prediction of rainfall reservoir-induced landslide with step-like deformation characteristics

Application of a multi-smartphone measurement system in slope model tests

A fast and practical method for determining particle size and shape by using smartphone photogrammetry

A large-scale obliquely inclined bedding rockslide triggered by heavy rainstorm on the 8th of July 2020 in Shiban Village, Guizhou, China

Synthetic Aperture Radar (SAR) is one of the most widely utilized methods to extract elevation information and identify large-scale deformations in mountainous areas. Homologous points in stereo SAR image pairs are difficult to identify due to complex geometric and radiometric distortions. In this paper, a new approach for mountainous area images is suggested. Firstly, a simulated SAR image and a look-up table based on DEM data are generated by a range-Doppler model and an empirical formula. Then, a point matching RPM-L2E algorithm is used to match images obtained by the simulation and in real-time to indirectly obtain the feature points of the real SAR images. Finally, the accurate registration of mountainous areas in the SAR images is achieved by a polynomial transform. Experimental verification is performed by using the data of mountainous SAR images from the same sensor and different sensors. When the registration accuracy of the method is compared with that of two state-of-the-art image registration algorithms, better outcomes are experimentally shown. The suggested approach can effectively solve the registration problem of SAR images of mountainous areas, and can overcome the disadvantages of poor adaptability and low accuracy of traditional SAR image registration methods for mountainous areas.

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Mountainous SAR Image Registration Using Image Simulation and an L2E Robust Estimator

An increasing number of landslides are occurring during the construction of highways in mountainous areas all over the world. A recent highway landslide located in the mountainous region of Southwest China is taken as a representative example to carry out the initiation mechanism and deformation characteristics analysis, and study the intelligent partitioning method in this paper. The geological conditions of the landslide are obtained through detailed geological surveys and investigation. A three-dimensional (3D) monitoring network with a total of 72 multi-points, including 53 surface displacement monitoring points and 19 deep displacement monitoring holes, is established to examine the spatial deformation characteristics of the landslide for nearly 25 months. The monitoring results of the surface displacement show that the dynamic deformation of the landslide can be divided into three stages, namely initial deformation stage, accelerated deformation stage, and stabilization stage. Based on the strata distribution obtained by the borehole investigation and the results of deep displacement monitoring, the depth of the slip surface can be reliably determined. The smart contract method in blockchain technology combined with the multi-point monitoring dataset is novelty applied to realize the intelligent automatic partition of the highway landslide without any human involvement. This method can provide the basis for further independent analysis of different landslide zones. According to the investigation and analysis, the initiation mechanism for the highway landslides is attributed to both internal poor geological conditions and external intense precipitation and engineering excavation.

Mechanism analysis and partition characteristics of a recent highway landslide in Southwest China based on a 3D multi-point deformation monitoring system

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

Jinye Xian

Papers

Mechanism analysis and partition characteristics of a recent highway landslide in Southwest China based on a 3D multi-point deformation monitoring system

Evolution State Identification of Deep Landslide Displacement Based on a Quadratic Wavelet Reconstruction and Bispectrum Analysis Method