Analysis and prediction of landslide subsidence characteristics of Dangchuan based on Sentinel-1A data

Hui Zhang; Xinghai Dang; Liqi Jia; Jianyun Zhao; Xincheng Fan; Ming Lu

doi:10.1117/12.2639299

15 July 2022 Analysis and prediction of landslide subsidence characteristics of Dangchuan based on Sentinel-1A data

Hui Zhang, Xinghai Dang, Liqi Jia, Jianyun Zhao, Xincheng Fan, Ming Lu

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 122581H (2022) https://doi.org/10.1117/12.2639299
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

In order to study the spatial distribution characteristics and causes of Heifangtai landslide in Gansu Province, the sentinel- 1A images from September 2017 to November 2020 were used as the data source to extract surface subsidence information in the study area using SBAS technology, and the high coherence point D1 of the landslide in Dangchuan village was selected, the subsidence was analyzed by combining irrigation, rainfall and temperature data. And the BP neural network was used to predict the point. The results showed that: (1) the area identified by SBAS technology was mainly spread in Xinyuan village, Fangtai village, Zhuwang village, Chenjia village and around the tableland. (2) In February and March, due to the large temperature difference, the landslide of Dangchuan started to settle as the temperature increased and caused the permafrost to melt; The amount of irrigation and rainfall increases from June, when the loess tableland starts to sink and landslides occur frequently; After October, the landslide in Dangchuan Village produced a frozen stagnant water effect, and there was a tendency for the subsidence to increase. (3) The prediction result of BP neural network shows that the subsidence rate of D1 point will surpass 60 mm in 2022, which is important for the early identification and prevention of the area.

Citation Download Citation

Hui Zhang, Xinghai Dang, Liqi Jia, Jianyun Zhao, Xincheng Fan, and Ming Lu "Analysis and prediction of landslide subsidence characteristics of Dangchuan based on Sentinel-1A data", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 122581H (15 July 2022); https://doi.org/10.1117/12.2639299

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KEYWORDS

Landslide (networking)

Neural networks

Signal processing

Interferometry

Data processing

Coherence (optics)

Image filtering

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