基于神经网络的InSAR/GNSS深度融合尾矿库坝体时序监测及预测研究
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吕林伟, 王瑞, 许林涛, 黄诗乔, 黄帅帅, 林敏, 贺一波, 何倩, 晏慧能, 陈尚波
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Time-series monitoring and prediction of tailings dams through neural network-based deep infusion of InSAR and GNSS data
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LYU Linwei, WANG Rui, XU Lintao, HUANG Shiqiao, HUANG Shuaishuai, LIN Min, HE Yibo, HE Qian, YAN Huineng, CHEN Shangbo
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表2 各点InSAR数据与GNSS融合后的数据
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Tab.2 Data after InSAR data and GNSS fusion at each point
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| 期数 | 形变量/mm | 期数 | 形变量/mm | | #1 | #2 | #3 | #4 | #5 | #6 | #1 | #2 | #3 | #4 | #5 | #6 | | 1 | 0.637 1 | 0.615 5 | 0.979 5 | -0.082 2 | 0.234 0 | 0.803 0 | 14 | -4.956 4 | -2.788 8 | -2.134 8 | -2.791 1 | -6.699 8 | -5.676 9 | | 2 | -1.460 7 | -2.950 4 | -3.993 2 | -2.491 5 | -4.643 6 | -6.334 6 | 15 | 0.427 4 | 0.842 5 | 0.981 6 | 0.797 3 | -2.098 1 | -6.149 1 | | 3 | 1.273 6 | 1.239 1 | 0.801 0 | -0.811 3 | -0.381 7 | -0.333 6 | 16 | -2.615 4 | -1.124 9 | -0.962 9 | -2.236 6 | -1.405 7 | -7.558 9 | | 4 | 2.658 6 | 2.072 8 | 1.490 7 | 7.412 5 | 0.520 3 | 2.025 0 | 17 | 5.109 2 | 4.628 5 | -2.439 1 | -1.143 9 | -4.825 7 | -3.887 0 | | 5 | 0.474 9 | 0.049 4 | 0.335 2 | 0.169 2 | -0.244 7 | -0.795 4 | 18 | 3.069 5 | 4.539 3 | 4.668 6 | -3.581 6 | -8.931 5 | -6.462 6 | | 6 | 0.629 5 | 1.733 3 | 2.096 4 | 0.672 0 | 0.412 4 | 0.601 5 | 19 | 8.841 9 | 10.362 4 | 15.089 3 | 13.455 7 | 11.380 1 | 10.399 6 | | 7 | -1.319 0 | -0.113 3 | -0.880 3 | -1.401 8 | -1.241 2 | -0.475 0 | 20 | 5.687 2 | 6.759 1 | 0.908 7 | -0.348 7 | -5.607 8 | -1.784 0 | | 8 | -1.484 2 | -0.624 5 | -0.133 0 | -0.926 9 | -0.990 5 | 0.117 2 | 21 | 6.497 3 | 9.412 2 | 1.143 2 | -2.409 3 | -6.108 6 | -4.407 3 | | 9 | 0.121 5 | 0.494 8 | -0.430 2 | 0.221 7 | -1.595 3 | 0.800 5 | 22 | 5.618 1 | 4.864 5 | 0.958 5 | 4.878 0 | 0.231 5 | -0.646 1 | | 10 | 2.128 4 | 2.686 0 | 1.283 6 | 0.297 1 | 0.091 0 | 0.500 9 | 23 | 6.627 7 | 8.626 6 | 5.340 9 | -2.485 7 | -2.274 1 | -1.678 8 | | 11 | -1.245 6 | 0.287 7 | -1.665 7 | -2.530 0 | -3.448 7 | 1.209 5 | 24 | 6.440 3 | 9.453 2 | 4.616 0 | 1.632 2 | -2.200 9 | 1.804 0 | | 12 | -1.126 7 | -1.372 2 | 0.561 0 | 3.087 9 | -0.439 2 | 0.131 3 | 25 | 5.362 4 | 6.862 3 | 5.398 5 | 2.257 3 | -2.408 6 | -2.099 4 | | 13 | -2.288 3 | 0.465 3 | 2.618 1 | 1.317 1 | -3.416 0 | -3.081 8 | 26 | 3.346 7 | 3.626 3 | 0.785 0 | -2.959 6 | -7.874 3 | -7.624 1 |
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