As natural disasters are induced by geodynamic activities or abnormal changes in the environment, geological hazards tend to wreak havoc on the environment and human society. Recently, the dramatic increase in the volume of various types of Earth observation ‘big data’ from multiple sources, and the rapid development of deep learning as a state-of-the-art data analysis tool, have enabled novel advances in geological hazard analysis, with the ultimate aim to mitigate the devastation associated with these hazards. Motivated by numerous applications, this paper presents an overview of the advances in the utilization of deep learning for geological hazard analysis. First, six commonly available Earth observation data sources are described, e.g., unmanned aerial vehicles, satellite platforms, and in-situ monitoring systems. Second, the deep learning background and six typical deep learning models are introduced, such as convolutional neural networks and recurrent neural networks. Third, focusing on six typical geological hazards, i.e., landslides, debris flows, rockfalls, avalanches, earthquakes, and volcanoes, the deep learning applications for geological hazard analysis are reviewed, and common application paradigms are summarized. Finally, the challenges and opportunities for the application of deep learning models for geological hazard analysis are highlighted, with the aim to inspire further related research.

由于自然灾害是由地球动力学活动或环境异常变化引起的，地质灾害往往对环境和人类社会造成严重破坏。最近，来自多源的各类地球观测“大数据”的数量急剧增加，深度学习作为最先进的数据分析工具的快速发展，使地质灾害分析取得了新的进展，最终目的是减轻与这些危害相关的破坏。受众多应用的启发，本文概述了利用深度学习进行地质灾害分析的进展。首先，描述了六个常用的地球观测数据源，例如无人机、卫星平台和原位监测系统。第二，介绍了深度学习背景和六种典型的深度学习模型，如卷积神经网络和循环神经网络。第三，针对滑坡、泥石流、落石、雪崩、地震、火山等六种典型地质灾害，回顾了深度学习在地质灾害分析中的应用，总结了常用的应用范式。最后，强调了深度学习模型在地质灾害分析中应用的挑战和机遇，以期激发进一步的相关研究。回顾了深度学习在地质灾害分析中的应用，总结了常见的应用范式。最后，强调了深度学习模型在地质灾害分析中应用的挑战和机遇，以期激发进一步的相关研究。回顾了深度学习在地质灾害分析中的应用，总结了常见的应用范式。最后，强调了深度学习模型在地质灾害分析中应用的挑战和机遇，以期激发进一步的相关研究。