The research on active tectonics and geomorphology involves extensive sub-topics, including the kinematics of crustal movements, the processes underlying the evolution of landforms, and the associated dynamic mechanisms. These sub-topics are intricately connected with the interactions between the Earth's endogenic and exogenic processes. In the contemporary realm of the Earth system science, research in active tectonics and geomorphology has become a hot topic for interdisciplinary study. The advancement in big data research coupled with the progressive developments in deep learning technologies has furnished this field of study with a voluminous array of data sources and the requisite analytical tools for technical analysis. In recent years, the application of big data and deep learning technologies in this research field has yielded a series of outstanding results, fostering new research directions, and ushering the discipline into a new phase. In this paper we synthesize existing research to outline the data sources pertinent to the study of active tectonics and geomorphology, including field geological survey, unmanned aerial vehicle ( UAV ) -based photography, aerial photography, and remote sensing observations. Then, we discuss in-depth examination of the recent innovations progresses in deep learning algorithms, including but not limited to convolutional neural networks( CNNs) , deep Gaussian processes, and autoencoders. This article further elaborates on innovative applications of deep learning in the study of active tectonics and geomorphology. These include the identification of changes in glacier extent, monitoring volcanic activity and deformation, recognizing river systems, precise surveillance of landslide events, as well as observations of lithospheric deformation co-seismic surface ruptures. Based on the summary of prior studies, this paper showcases a distinct application instance. By employing convolutional neural networks( CNNs) within the realm of deep learning image analysis and utilizing UAV-obtained high-resolution images, we undertake the automated detection of structural fractures in granite rocks in Meizhou island, in the southeast of Fujian province, China. In fault damage zones, structural rock fractures are widely developed, and the study of their orientation, system, and secondary characteristics is of great importance for determining their mechanisms of development and the multi-phase tectonic activity events in the region. Under conventional methodologies , the study of structural fractures in rocks is time-consuming and requires considerable manual effort in conducting exhaustive field surveys and detailed interpretation of cartographic representations. However, the application of deep learning can greatly enhance the efficiency of cartographic work. This application case has improved the classic deep learning framework by developing a CNN model specifically designed for the extraction of complex features and multi-scale rock fractures. This model achieved rapid identification of over 9 000 fractures with varied shapes and complex distributions within 55 minutes, attaining an accuracy of 85% and a recall rate of 89%. These findings demonstrate that deep learning significantly enhances operational efficiency in comparison to manual statistical methods for the automated identification of rock structural fractures, while also maintaining exceptional accuracy in fracture detection. Based on the results identified by deep learning, it can be clearly observed that two sets of fractures, oriented NE and NW, develop on the granite outcrops in the study area. According to previous research and the cross-cutting relationships of the fractures, it is known that NE-oriented fractures formed earlier than NW-oriented fractures, corresponding respectively to the Indosinian Movement and the expansion movement of the South China Sea in the tectonic history of South China. Through the automated extraction of deep learning models, the workload of manual mapping can be greatly reduced, yielding results consistent with actual geomorphological phenomena. © 2024 State Seismology Administration. All rights reserved.
