面向小样本场景的基于多源域迁移学习的边坡位移预测

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中文摘要：位移的准确预测对滑坡预警至关重要.然而边坡形变与影响边坡稳定性的触发因素之间复杂耦合关系使得很难对边坡位移进行准确预测.此外,在工程实践中,由于设备检修及恶劣条件导致获得的位移监测数据较少,监测数据的不足也限制了预测模型的性能.为解决此问题,本文提出了一种基于多源域迁移学习框架的边坡位移预测模型,利用多个源域的有效信息以更好地完成目标域的预测工作.首先,采用基于最小样本熵的改进变分模态分解(VMD)方法将累积位移分解为趋势项、周期项和随机项.其中趋势项采用自回归(AR)模型预测,周期项采用长短期记忆网络(LSTM)预测.由于随机项受不确定因素影响,采用Wasserstein-GAN与多源域迁移学习相结合的方法对其进行预测以提高预测精度.以某实际矿山边坡为例,在目标域监测样本不足的情况下对本文所提预测模型的预测精度进行验证,并与三种传统预测模型的预测结果进行对比.结果表明,本文所提预测模型性能较好,可为缺乏有效样本时的边坡位移预测提供一些参考.

外文标题：Slope displacement prediction based on multisource domain transfer learning for insufficient sample data

外文摘要：Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.

外文关键词：

slope displacementmultisource domain transfer learning(MDTL)variational mode decomposition(VMD)generative adversarial network(GAN)Wasserstein-GAN(WGAN)

作者：

郑海青、胡琳旎、孙晓云、张雨、金申熠

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作者单位：

石家庄铁道大学电气与电子工程学院,河北石家庄,050043

关键词：

边坡位移多源域迁移学习变分模态分解(VMD) 生成对抗网络 Wasserstein GAN(WGAN)

基金：

National Natural Science Foundation of ChinaDepartment of Education of Hebei Province of ChinaNatural Science Foundation of Hebei Province of ChinaS&T Program of Hebei

项目编号：

51674169ZD2019140F201921024322375413D

出版年：

2024

DOI：

10.1007/s11770-022-1003-x

应用地球物理(英文版)

中国地球物理学会

应用地球物理(英文版)

影响因子：1.01

ISSN：1672-7975

年,卷(期)：2024.21(3)