A Method for Predicting Landslides Based on Micro-Deformation Monitoring Radar Data

Tan, Weixian; Wang, Yadong; Huang, Pingping; Qi, Yaolong; Xu, Wei; Li, Chunming; Chen, Yuejuan

doi:10.3390/rs15030826

Cited by 8 publications

(8 citation statements)

References 37 publications

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“…During long-term observation, PSs will disappear or rebirth over time because of the time-variation environment and certain uncontrollable factors, such as human activities, as well as the local vibrations caused by the construction and noise of the system, which affect the monitoring results [ 23 ]. Deformation information is estimated by using variables of PSs during inversion.…”

Section: Introductionmentioning

confidence: 99%

A New Permanent Scatterer Selection Method Based on Gaussian Mixture Model for Micro-Deformation Monitoring Radar Images

Tan,

Li,

Hou

et al. 2024

Sensors

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The micro-deformation monitoring radar is usually based on Permanent Scatterer (PS) technology to realize deformation inversion. When the region is continuously monitored for a long time, the radar image amplitude and pixel variance will change significantly with time. Therefore, it is difficult to select phase-stable scatterers by conventional amplitude deviation methods, as they can seriously affect the accuracy of deformation inversion. For different regions studied within the same scenario, using a PS selection method based on the same threshold often increases the size of the deformation error. Therefore, this paper proposes a new PS selection method based on the Gaussian Mixture Model (GMM). Firstly, PS candidates (PSCs) are selected based on the pixels’ amplitude information. Then, the amplitude deviation index of each PSC is calculated, and each pixel’s probability values in different Gaussian distributions are acquired through iterations. Subsequently, the cluster types of pixels with larger probability values are designated as low-amplitude deviation pixels. Finally, the coherence coefficient and phase stability of low-amplitude deviation pixels are calculated. By comparing the probability values of each of the pixels in different Gaussian distributions, the cluster type with the larger probability, such as high-coherence pixels and high-phase stability pixels, is selected and designated as the final PS. Our analysis of the measured data revealed that the proposed method not only increased the number of PSs in the group, but also improved the stability of the number of PSs between groups.

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Section: Introductionmentioning

confidence: 99%

A New Permanent Scatterer Selection Method Based on Gaussian Mixture Model for Micro-Deformation Monitoring Radar Images

Tan,

Li,

Hou

et al. 2024

Sensors

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“…Currently, open-pit slope deformation prediction models mainly include the statistical model, the deterministic model, and the artificial intelligence model [3]. The artificial intelligence model is suitable for the construction of complex nonlinear models, which significantly improves the accuracy of slope prediction [4]. Artificial intelligence models include the random decision forest model [5], the neural network model [6], the support vector machine [7], the extreme learning machine [8], etc.…”

Section: Introductionmentioning

confidence: 99%

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Du,

Song,

et al. 2024

ELEKTRON ELEKTROTECH

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At present, due to the complex and changeable geological conditions, the precise deformation prediction technology of high and steep slope could not achieve an accurate prediction. In particular, the single forecasting model has some problems such as poor stability, low precision, and data fluctuation. In practice, excavating the complex nonlinear relationship between open-pit slope surface deformation monitoring data and various influencing factors and improving the accuracy of the deformation prediction of high and steep slopes is the key to safe open-pit mine production. It proposed to introduce the position factor and the velocity factor into a twin support vector machine (TWSVM). The adaptive subgroup optimisation (APSO) algorithm is selected for parameter optimisation. Through the comparative analysis of TWSVM, genetic algorithm-TWSVM (GA-TWSVM), and the proposed APSO⁃TWSVM, the experimental data show that the mean absolute error (MAE) values of the three models are 13.29 %，8.17 %, and 1.27 %, the RMSE - 47.83 %，6.52 %, and 3.02 %, respectively; the prediction time for APSO⁃TWSVM is improved by 62.5 % compared to GA-TWSVM.

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“…However, there may be a disadvantage in the frequency of the passage of the relevant satellites over a given location. A very effective method is terrestrial InSAR [8], which is commonly placed on a linear base and images the opposite slope in a time series, as reported in [9] and [10]. However, this system cannot always be used; there must be favorable conditions for this, in particular, the entire landslide area to be monitored must be visible from a ground position.…”

Section: Introductionmentioning

confidence: 99%

Methodology and Results of Staged UAS Photogrammetric Rockslide Monitoring in the Alpine Terrain in High Tatras, Slovakia, after the Hydrological Event in 2022

Kovanič

Štroner

Urban

et al. 2023

Land

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There are numerous talus cones that have formed by long-term geological processes and sudden hydrological events in the Small Cold Valley (High Tatras National Park in Slovakia). Frequent hiking trails lead here; therefore, their safeness needs to be monitored due to recent rock avalanches and landslides. A complex methodology for monitoring changes in talus cones was developed to determine the extent, pace, nature, and origin of the morphological changes in the land in this complex high-mountain terrain. Non-contact UAS photogrammetry with SfM-MVS processing was applied as a quick, reliable, and environment-friendly data acquisition method. For proper georeferencing, a network of GCPs and stabilized surveying points were established by terrestrial geodetic surveying. Together with an evaluation of the methodology, the results comparing the actual state of a talus cone in 2018 and 2022 (after the significant hydrological event) are presented. Comparing and analyzing spatial models represented by point clouds, with an accuracy of centimeter level, was obtained. The detected morphological changes reached values in meters. A differential model expresses the distribution of the morphological changes. In conclusion, geodetic and geological knowledge is synthesized to evaluate the phenomena occurring in this territory.

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A Method for Predicting Landslides Based on Micro-Deformation Monitoring Radar Data

Cited by 8 publications

References 37 publications

A New Permanent Scatterer Selection Method Based on Gaussian Mixture Model for Micro-Deformation Monitoring Radar Images

A New Permanent Scatterer Selection Method Based on Gaussian Mixture Model for Micro-Deformation Monitoring Radar Images

Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM

Methodology and Results of Staged UAS Photogrammetric Rockslide Monitoring in the Alpine Terrain in High Tatras, Slovakia, after the Hydrological Event in 2022

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