State recognition method of hydrodynamic pressure-driven landslides based on a generative adversarial network
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摘要:
动水驱动型滑坡状态识别能更有效地辅助分析滑坡形变规律, 实现滑坡状态的准确识别对深入展开动水驱动型滑坡状态研究具有重要意义。针对目前动水驱动型滑坡突变状态研究较少, 难以获得相关特征, 从而导致状态识别性能不佳的问题, 提出了一种应用于动水驱动型滑坡状态识别的生成对抗网络学习方法。本方法通过构建滑坡状态监测数据矩阵, 依据少量数据样本设计合理的生成器网络完成对滑坡状态的数据扩增并设计判别器网络实现扩增数据的筛选, 通过对抗生成网络实现对滑坡状态的分类, 达到滑坡状态识别的目的。以三峡库区白水河滑坡为研究对象, 将降雨、库水位、深部位移和地表位移等多源监测数据进行了规范化处理, 设计生成器网络和对抗器网络完成了对滑坡状态数据的扩增, 设计滑坡状态识别生成对抗网络完成了对滑坡状态的分类和识别。结果表明, 生成对抗网络对滑坡状态识别具有较高的准确率。研究结果证实本方法能够对目标区域内的动水驱动型滑坡状态进行准确识别和分类, 可直接应用于工程实际。
Abstract:Identifying the states of hydrodynamic pressure-driven landslides can more effectively assist in the analysis of the landslide deformation law, and accurately identifying the landslide state is of great significance for the in-depth study of the hydrodynamic pressure-driven landslide state. Aiming at the problem that there are few abrupt states of hydrodynamic pressure-driven landslides, it is difficult to obtain relevant features, which leads to poor state recognition performance, and a generative adversarial network learning method for landslide state recognition is proposed.In this method, the landslide state monitoring data matrix is constructed, a reasonable generator network is designed based on a small number of data samples to complete the data amplification of the landslide states, and the discriminator network is designed to realize the screening of the amplified data, and the classifying landslide states being realized through the confrontation generation network to achieve the purpose of landslide status identification. Taking the Baishuihe landslide in the Three Gorges Reservoir area as the research object, multisource monitoring data such as rainfall, reservoir water level, deep displacement, and surface displacement are normalized. The states recognition generative adversarial network completes the classification and identification of the landslide state. The results show that the generative adversarial network has high accuracy for landslide state recognition. The research method in this paper can accurately identify and classify the landslide state in the target area.
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图 2 滑坡状态判别器方法示意图
Figure 2. Schematic diagram of the landslide state discriminator method
图 3 滑坡状态识别生成对抗网络架构
Figure 3. A framework of the generative adversarial algorithm for landslide state recognition
图 5 白水河滑坡ZG93监测点相关监测数据
Figure 5. Related monitoring data of Baishuihe landslide at ZG93 monitoring point
图 6 白水河滑坡状态识别训练准确率
Figure 6. Landslide state recognition training accuracy of the Baishuihe landslide
图 7 白水河滑坡状态识别训练损失值
Figure 7. Landslide state recognition training loss value of the Baishuihe landslide
图 8 白水河滑坡状态识别验证准确率
Figure 8. Landslide state recognition verification accuracy of the Baishuihe landslide
图 9 白水河滑坡状态识别验证损失值
Figure 9. Landslide state recognition verification loss value of the Baishuihe landslide
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