Limited monitoring data of newly-established monitoring slopes in universal landslide monitoring projects and their unavoidable poor representation ability of the deformation patterns have made the traditional single-slope modelling impossible. This paper proposes to classify the multi-slope integrated monitoring dataset based on the lithology of slopes and thus construct pre-trained models to apply to the newly-established monitoring slopes to improve the prediction performance. By integrating the monitoring data, the pre-train models can learn more deformation characteristics from the dataset than from only single-slope data. Moreover, by further classifying the integrated dataset based on the lithology of slopes, constructing different pre-training models, and applying them to newly-established slopes with corresponding lithology, it is feasible to enhance the classified dataset’s ability to represent the deformation patterns of corresponding kind of slopes while still ensuring the volume of dataset of each class is reasonable and, ultimately, to improve the pre-trained models by enhancing the consistency of the pre-training data and target domain data. A case study based on actual monitoring data shows that the pre-training models based on lithology-classified dataset perform overall significantly better on newly-established monitoring slopes with corresponding lithology than single-slope models or pre-training models based on other integrated dataset and may provide effective support for displacement prediction of newly-established monitoring slopes.

landslide, short-time displacement prediction, lithology-classified integrated dataset, pre-training model, newly-established monitoring slope, universal landslide monitoring

针对普适型滑坡监测工作中新建监测坡面有效数据量少, 代表性不足, 难以开展高精度单坡建模的问题, 建立基于岩性分类的综合数据集, 开展模型预训练, 从而提升建模效果。通过综合数据集, 模型可以挖掘和利用多坡面监测数据中更丰富的变形特征。依据基础岩性对综合数据集进行分类, 构建不同的预训练模型, 并应用于对应岩性的新建坡面, 能够在保证数据集数量较为充足的同时, 增强分类数据集对不同类别坡体变形规律的表征能力, 通过提升预训练数据和目标域数据分布的一致性, 进一步提高建模效果。实例验证结果表明, 基于岩性分类综合数据集的预训练模型, 在对应岩性新建坡面上, 建模效果总体上显著优于单坡面模型和基于其他综合数据集的预训练模型, 可以为新建坡面位移预测工作提供有力的支持。