基于SBAS-InSAR和PSO-BP模型的鲁南高铁沿线地表沉降监测与预测OA北大核心CSTPCD
Surface Subsidence Monitoring and Prediction along Lunan High Speed Railway Based on SBAS-InSAR and PSO-BP Model
选取38景Sentinel-1A SAR影像,利用SBAS-InSAR技术获取2019-02～2022-11鲁南高铁曲阜-菏泽段沿线5 km区域的地表沉降结果,分析其分布特征和规律,并利用PSO-BP模型对若干特征点进行沉降预测.结果表明,高铁沿线0.1 km范围内地表年均形变速率为-20-15 mm/a,最大沉降速率为25.46 mm/a,最大抬升速率为17.43 mm/a;PSO-BP模型得到的沉降预测值的RMSE为5.8～12.4 mm,可对地表沉降进行较好的预测.
We select 38 views of Sentinel-IA SAR images,and obtain the surface subsidence results within 5 km along the Qufu-Heze section of the Lunan high speed railway using SBAS-InSAR technol-ogy for the period from February 2019 to November 2022.We analyze the distribution characteristics and patterns,and use the PSO-BP model to predict the subsidence of some feature points.The results show that the annual average deformation rate is between-20 and 15 mm/a,the maximum subsid-ence velocity is 25.46 mm/a,and the maximum lifting velocity is 17.43 mm/a within 0.1 km along the high speed railway;the RMSE of the subsidence prediction value obtained by the PSO-BP model ranges from 5.8 to 12.4 mm,which can predict the surface subsidence well.
何虎振;刘国林;王凤云;陶秋香
山东科技大学测绘与空间信息学院,青岛市前湾港路579号,266590
测绘与仪器
鲁南高铁SBAS-InSARPSO-BP模型地表沉降沉降预测
Lunan high speed railwaySBAS-InSARPSO-BP modelsurface subsidencesubsidence prediction
《大地测量与地球动力学》 2024 (008)
820-826 / 7
山东省自然科学基金(ZR2020MD043,ZR2020MD044);国家自然科学基金(42074009). Natural Science Foundation of Shandong Province,No.ZR2020MD043,ZR2020MD044;National Natural Science Foundation of China,No.42074009.
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