基于迁移学习的青藏高原东北缘地震诱发滑坡易发性评估模型

Assessment model of earthquake-induced landslide susceptibility based on transfer learning in the northeastern margin of the Qinghai-Tibet Plateau

通过研究,旨在提出一种迁移学习方法,以应对机器学习在缺乏历史滑坡点数据的大区域很难取得良好的评估效果的挑战。首先,通过结合10个影响因子利用随机森林算法对2013年芦山7.0级地震极震区进行预训练,得到高精度的预训练模型。随后,采用直推式迁移学习方法进行初始迁移,并利用“半监督”评估方式补充青藏高原东北缘地区的标签数值点。最后,利用归纳式迁移学习进一步训练预训练模型,得到在青藏高原东北缘地区评估更准确的地震诱发滑坡易发性评估图。此外,使用Kullback-Leibler散度计算迁移前后区域影响因子数据的相似性,并对2022年泸定6.8级地震极震区进行评估应用,验证基于迁移学习的青藏高原东北缘地震诱发滑坡易发性评估模型的准确性。研究结果可为该区域地震诱发滑坡灾害预防提供一定的参考。

Machine learning cannot achieve good evaluation results in large areas without historical landslide data;hence,this paper proposes a transfer learning method.First,a pretraining model with high accuracy was obtained by combining ten influencing factors,and the extremely seismic zone of the Lushan M 7.0 earthquake in 2013 was pretrained using the random forest algorithm.Then,an initial transfer was performed using the direct transfer learning method,and the label numerical points in the northeastern margin of the Qinghai-Tibetan Plateau were supplemented using the“semisupervized”evaluation method.Finally,the pretraining model was further trained using inductive transfer learning to obtain an accurate seismic landslide susceptibility assessment map in the northeastern margin of the Qinghai-Tibetan Plateau.In addition,similarity in the influencing factors before and after the transfer was calculated using the Kullback-Leibler divergence.The accuracy of the proposed model based on transfer learning in the northeastern margin of the Qinghai-Tibetan Plateau was verified by applying it to the extremely seismic zone of the Luding M 6.8 earthquake in 2022.This study provides a reference for preventing earthquake-induced landslides in the study region.