Machine learning in identifying and mapping the surface rupture of the 2021 M(w)7. 4 Madoi earthquake, Qinghai

High-resolution mapping of coseismic surface rupture of large earthquakes is very important for better understanding the behavior and mechanism of earthquake rupture and for quantifying earthquake hazards. High -resolution UAV imagery and topographic data provide a large volume of valuable images of the surface rupture. Manual mapping of fractures on many high-resolution images could be labor-intensive, time-consuming, and thus inefficient. Machine learning provides more possibilities for the rapid processing of such big-data images. In this paper, we demonstrate the potential of Machine learning techniques to rapid, efficient, and complete identification of fractures of the surface rupture zone using high-precision UAV images of the 2021 Madoi M(w)7. 4 earthquake. We applied the canny algorithm (based on Convolutional Neural Networks) to discuss the processing flow and key steps of UAV digital orthophoto in detail, including preparing training data, training, and post-processing. By comparing the interpretations of manual mapping and machine recognition, the proposed method can effectively map surface rupture, providing a tool for studying future large earthquakes. Machine learning has advantages and broad prospects in quantitative studies of earthquake geology, surface processes and geomorphology.