PDF(Pubmed)
Abstract：
A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms.
摘要：
深层滑坡可能会从蠕滑运动中释放出许多微震信号，其中包括斜坡表面的岩土滑移和地下的岩土剪切破裂。机器学习可以有效地增强滑坡地震监测中微震信号的分类，并解释滑坡运动的力学过程。在本文中,在深层滑坡内部部署了八套三轴地震传感器，酒仙平,中国,通过1年的连续监测，获得了大量与边坡运动有关的微震信号。所有数据都通过地震事件识别模式，长期平均值和短期平均值的比率。我们选取了11天的数据,手动将4131个数据分为八类，并创建了一个微震事件数据库。本文对经典的机器学习算法和集成学习算法进行了测试。为了评估各算法模型的地震事件分类性能,我们通过精度的维度评估了所提出的算法，精度,并召回每个模型。验证结果表明,经典机器学习算法中性能最好的决策树算法的准确率为88.75%,而集成算法，包括随机森林，梯度提升树，极端梯度提升，和轻型梯度增压机，精度范围从93.5%到94.2%，在精度的综合评价中也取得了更好的结果，召回，F1得分。每个微震事件类别的特定分类测试显示相同的结果。结果表明，与经典的机器学习算法相比，集成学习算法显示出更好的结果。
