# Recognition of Earthquake Surface Ruptures Using Deep Learning
###### Abstract  
Investigating post-earthquake surface ruptures is important for understanding the tectonics of seismogenic faults. The use of unmanned aerial vehicle (UAV) images to identify post-earthquake surface ruptures has the advantages of low cost, fast data acquisition, and high data processing efficiency. With the rapid development of deep learning in recent years, researchers have begun using it for image crack detection. However, due to the complex background and diverse characteristics of the surface ruptures, it remains challenging to quickly train an effective automatic earthquake surface rupture recognition model on a limited number of samples. This study proposes a workflow that applies an image segmentation algorithm based on convolutional neural networks (CNNs) to extract cracks from post-earthquake UAV images. We selected the 16-layer visual geometry group (VGG16) network as the primary network architecture. Then, we improved the VGG16 network and deleted several convolutional layers to reduce computation and memory consumption. Moreover, we added dilated convolution and atrous spatial pyramid pooling (ASPP) to make the network perform well in the surface crack identification of post-earthquake UAV images. We trained the proposed method using the data of the MS 7.4 Maduo earthquake and obtained a model that could automatically identify and draw small and irregular surface ruptures from high-resolution UAV images. deep learning: earthquake surface ruptures; crack detection; image semantic segmentation +
Footnote †: journal: Medical Imaging  
images. The results can meet the needs of the quantitative parameter extraction of coseismic surface ruptures and structure research in an earthquake emergency. Relevant scholars have used high-resolution UAV data to analyze giant coseismic surface rupture zones [5,6]. Their analysis results provide direct evidence for ascertaining the geometric, kinematic, and dynamic characteristics of the seismogenic fault. However, artificial visual interpretation and traditional image processing are the primary methods for finely interpreting high-precision UAV photos of earthquake surface ruptures. Manual visual interpretation is time-consuming and labor-intensive, and its recognition accuracy is highly dependent on the ability of experts. Ideally, traditional image processing methods, such as edge detection [7] and Gabor filtering [8], only have high detection accuracy if the crack has good continuity and high contrast. However, external disturbances can easily affect the results of traditional methods [9]. Unfortunately, the distribution of cracks and noise interference are complex and diverse, which leads to poor results in traditional crack detection algorithms. The crack identification method based on deep learning does not require the pre-definition of crack features or image preprocessing but learns and automatically extracts features through samples to identify and extract cracks. Recently, deep learning has achieved great success in image processing and other fields, and several results have been achieved in combination with deep learning for crack identification (such as road crack detection [10], bridge inspection [11], house crack detection [12], and fault identification [13]). [PERSON] and [PERSON] [14] divided the current crack identification and extraction algorithms into three categories based on deep learning: (1) image classification [15,16], (2) target detection [17], and (3) image segmentation [18]. However, neither the image classification algorithm nor the target detection algorithm can directly extract cracks from the image at the pixel level. The two methods must combine traditional image segmentation techniques, such as edge detection, to complete the pixel-level extraction of cracks. However, compared with traditional digital image processing technologies, these algorithms can remove most background noise in the image for better crack identification results. The image segmentation algorithm can directly extract cracks from the original image.