Accurate and rapid assessment of seismic intensity after a destructive earthquake is essential for efficient early emergency response. We proposed an improved method, AL-SM99, to assess seismic intensity by analyzing aftershock sequences that occur within 2 h of mainshocks. The implementation effect and application conditions of this method were illustrated using 27 earthquakes with M_w 6.5–8.3 that occurred globally between 2000 and 2023. When the fault system in the seismic region is clear and simple, the robust LOWESS-fitted (locally weighted regression program) curves could be used to estimate the location and length of the fault rupture. LOWESS results can indicate the overall rupture trend and make reliable rupture-scale judgments even when the fault system is complex. When M_w ≥ 7.0 and the number of aftershocks exceeds 40, the AL-SM99 intensity evaluation results may be more reliable. Using aftershock catalogues obtained by conventional means allows for a stable assessment of seismic intensities within 1.5 h of the mainshock. When the number of aftershocks is sufficiently large, the intensity assessment time can be greatly reduced. With early accessible aftershocks, we can quickly determine the rupture fault planes and have a better estimate of the seismic intensities. The results of the intensity assessment provide a useful guide for determining the extent of the hardest-hit areas. By expanding the data sources for seismic intensity assessment, the early accessible data are utilized adequately. This study provides a valuable reference point for investigating the relationship between early aftershock events and fault rupture.

The evaluation of the crowdedness of urban emergency shelters is highly important for the evacuation and advance planning of emergency shelters. Given the urgency of evacuation following a disaster, this study assesses the buildings in a residential area, and attempts to improve the existing gravity model of emergency shelter evaluation through the introduction of population-scale influence factor, emergency shelter service capacity and travel limit distance. The study considers parts of the Chengguan District of Lanzhou City as an example. Based on the attraction of emergency shelters to the residential area, the residents are allocated to each emergency shelter for quantitative analysis of urban emergency shelter crowdedness under different travel limits distances. The results show that the crowdedness differs greatly depending on the distance to the shelter, and becomes worse due to the small number of emergency shelters in the Southwest region. With the increase of the travel limit distance, the number of emergency shelters that can be reached by the residents increases; consequently, the crowdedness decreases, which shows a trend of increasing from northeast to southwest. The improved gravity model considers the travel distance, population size and service capacity of emergency shelter. The model allows us to evaluate the impact of the travel distance on the crowdedness of emergency shelters when the travel distance is short, and assess the service capacity of emergency shelters when the travel distance is long. The use of this model can reasonably and reliably reflect the crowdedness of emergency shelters and provide a scientific basis for the planning and decision-making of relevant government departments.

Following a major earthquake, disaster information services must deliver accurate damage assessment results during the emergency ‘black box’ phase when data is scarce. Seismic intensity maps contain crucial information for determining the damage in the affected area. For earthquakes with Mw between 5.5 and 7, this study proposes using GIS analysis to mine aftershock events in early aftershock sequences that are closely related to the mainshock fault, and then using these events to generate seismic intensity assessment maps. Regression curves were first obtained using a nonparametric method (rLowess) to analyse the geographical coordinates of early aftershocks. Then, a buffer of 1 or 1.5 km radius was made for the curve, and the aftershocks in the buffer were used to calculate the predicted peak ground velocity (PGV) values over a specific km-grid range. Finally, rapid mapping of seismic intensity was assessed based on the intensity scale. This straightforward and repeatable method employs seismic station data obtained shortly after the mainshock. The assessed seismic intensity accurately reflects the location and extent of the hardest hit areas and can be cross-referenced with geophysical results to accurately assess the damage in the affected areas.

In this paper, we explored the combination of seismic station data and ground motion prediction equations (GMPE) to predict seismic intensity results by using Bayesian Maximum Entropy (BME) method. The results indicate that: 1) In earthquake analysis in Japan, soft data has predicted higher values of intensity in disaster areas. BME corrected this phenomenon, especially near the epicenter. Meanwhile, for earthquakes in the United States, BME corrected the erroneous prediction of rupture direction using soft data. 2) Compared with other spatial interpolation methods, the profile results of Japan earthquake and Turkey earthquake show that BME is more consistent with ShakeMap results than IDW and Kriging. Moreover, IDW has a low intensity anomaly zone. 3) The BME method overcomes the phenomenon that the strength evaluation results do not match the actual failure situation when the moment magnitude is small. It more accurately delineates the scope of the disaster area and enriches the post-earthquake processing of disaster area information and data. BME has a wide range of applicability, and it can still be effectively used for interpolation analysis when there is only soft data or few sites with data available.