Earthquakes will significantly impact the economic development of earthquake-affected areas and the regional post-earthquake economic recovery trajectory. We presented an explorative analysis of disaster resilience and introduced a quantitative framework to model recovery patterns of earthquake disasters. Four resilience metrics, namely instant disturbance, recovery rate, recovery time, and accumulated loss, were calculated to measure resilience as a dynamic process. Furthermore, we applied random forests to explore the potential factors influencing recovery patterns. The selected factors fell into three categories: 1) disaster factors, 2) environmental factors, and 3) socioeconomic factors. The framework’s utility was demonstrated in a retrospective study of the 2008 Wenchuan earthquake, which uncovered the differences in the long-term recovery trajectory of earthquake-affected areas and their influencing factors. The county-level analyses indicated a substantial spatial and temporal variation in the recovery patterns. Earthquake disaster resilience was related to socioeconomic factors and had an association with environmental and disaster factors. Our study not only helped to enrich the understanding of earthquake disaster resilience but also provided a basis for disaster mitigation strategies.

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.

Predicting future seismic trends and occurrence of earthquakes remains a long-standing challenge in seismology. Despite substantial efforts to unravel the physical mechanisms underlying earthquake occurrence, currently, no well-defined physical or statistical model is capable of reliably predicting major earthquakes. However, machine learning methods have demonstrated exceptional proficiency in identifying patterns within large-scale datasets, offering a promising avenue for enhancing earthquake prediction performance. Within the framework of machine learning, this study has developed a feature extraction method based on seismic prediction zoning, improving the effectiveness of machine learning-based earthquake prediction. The research findings indicate that the ensemble learning Stacking method, which is based on seismic prediction zoning, exhibits superior performance and high robustness in predicting the annual maximum earthquake magnitude. Additionally, the long short-term memory (LSTM) method demonstrates commendable performance within specific tectonic zones (e.g., the southwestern Yunnan region), providing valuable guidance for analyzing seismic trends in these regions.