Ground deformation observation is wildly concerned in the field of earthquake engineering. This paper proposes a high‐precision displacement measurement technology based on both computer vision and numerical simulation. During the earthquake, the vision‐based testing system collects visual data of the target installed on the location to be observed. The visual data streams can be quantified to the dynamic relative displacement value automatically, by employing mathematical vision algorithms and then by taking the relative displacement as an intermediate quantity, which is brought into the numerical model for iteration. When the test result is close to the simulated one, the absolute ground displacement data could be obtained approximately. A series of experiments have been carried out to suggest that the proposed method presents an innovative and low‐cost solution to ground measurement in high accuracy. The method not only realizes the real‐time ground deformation observation; moreover, it also provides a wider range of reliable data support to understand deformation mechanism, investigate seismic source information, and recognize the ground motion characteristics.

In order to predict the intensity of earthquake damage in advance and improve the effectiveness of earthquake emergency measures, this paper proposes a deep learning model for real‐time prediction of the trend of ground motion intensity. The input sample is the real‐time monitoring recordings of the current received ground motion acceleration. According to the different sampling frequencies, the neural network is constructed by several subnetworks, and the output of each subnetwork is combined into one. After the training and verification of the model, the results show that the model has an accuracy rate of 75% on the testing set, which is effective on real‐time prediction of the ground motion intensity. Moreover, the correlation between the Arias intensity and structural damage is stronger than the correlation between peak acceleration and structural damage, so the model is useful for determining real‐time response measures on earthquake disaster prevention and mitigation compared with the current more common antiseismic measures based on predictive PGA.

After a primary seismic event, subsequent aftershocks frequently induce progressive damage to slopes. Evaluating the response of slopes to mainshock-aftershock sequence (MAS) from a probabilistic perspective is crucial for disaster prevention and mitigation. Current research primarily focuses on single mainshock events, with limited consideration of the aftershock effects. Our study addresses the MAS events. Firstly, the random input field of the MAS is constructed through the collaboration of theoretical models and real data. Then, considering the peak characteristics, cumulative characteristics and spectral characteristics of the MAS, the numerical simulation method is used to explore the control parameters for the soil slope response. Ultimately, leveraging the probability density evolution method (PDEM), we introduce a reliability assessment framework for soil slope behavior under MAS loading. Compared with the existing approaches, the new method fully accounts for aftershock characteristics and assesses the degree of slope permanent displacement response with greater precision. These results offer substantial practical utility for engineering applications and serve as guidance for slope stability assessment and disaster mitigation strategies.

The coefficient of variation α is a critical parameter in the Brownian Passage Time (BPT) model, used to quantify the variability of large earthquake recurrence intervals. In this paper, a new estimation method is proposed for α based on paleoseismic sequences across multiple faults within a given tectonic region. By integrating Monte Carlo simulations with a Bayesian framework, the method assesses the probability distribution of α without assuming that the sample average recurrence interval equals the true mean μ, thereby avoiding epistemic bias. To validate the method, 1,000,000 simulations were conducted in two study areas of differing spatial scales. In the Western Qilian Mountains-Hexi Corridor, the posterior mean of α is 0.36 (without dating uncertainty) and 0.34 (with uncertainty). Expanding the analysis to 29 faults across western China, the estimated α increases to 0.39 (without dating uncertainty) and 0.36 (with uncertainty), with substantially reduced uncertainty bounds. The results reveal that increasing the number of paleoseismic sequences significantly reduces the uncertainty in estimating α, while considering dating uncertainty has only a minor impact. The methodology provides a robust framework for deriving region-specific recurrence variability parameters and proves particularly valuable for tectonically active regions where individual fault records are sparse but collectively form comprehensive datasets across multiple fault systems.

After a primary seismic event, subsequent aftershocks frequently induce progressive damage to slopes. Evaluating the response of slopes to mainshock-aftershock sequence (MAS) from a probabilistic perspective is crucial for disaster prevention and mitigation. Current research primarily focuses on single mainshock events, with limited consideration of the aftershock effects. Our study addresses the MAS events. Firstly, the random input field of the MAS is constructed through the collaboration of theoretical models and real data. Then, considering the peak characteristics, cumulative characteristics and spectral characteristics of the MAS, the numerical simulation method is used to explore the control parameters for the soil slope response. Ultimately, leveraging the probability density evolution method (PDEM), we introduce a reliability assessment framework for soil slope behavior under MAS loading. Compared with the existing approaches, the new method fully accounts for aftershock characteristics and assesses the degree of slope permanent displacement response with greater precision. These results offer substantial practical utility for engineering applications and serve as guidance for slope stability assessment and disaster mitigation strategies.