It is of great significance for emergency rescue to rapidly assess damage of buildings after an earthquake. Some previous methods are time-consuming, data are difficult to obtain, or there is lack of regional damage assessment. We proposed a novel way to rapidly assess building damage by comprehensively utilizing earth observation-derived data and field investigation to alleviate the above problems. These data are related to hazard-causing factors, hazard-formative environment, and hazard-affected body. Specifically, predicted ground motion parameters are used to reflect hazard-causing factors, e.g., peak ground velocity (PGV), peak ground acceleration (PGA), and pseudo-spectral acceleration (PSA). The hazard-formative environment is denoted by the underground 30 m shear wave velocity. Vulnerability of buildings is reflected by their structure type, age, and height. We take the April 2015 Nepal earthquake as a case study, and building damage data interpreted from satellite images are used to validate the effectiveness of the proposed method. Based on the gradient boosting machine, this paper rapidly assesses building damage from two different spatial levels, i.e., pixel and microzone, and obtains the potentially affected position and regional damage rate. Compared with the method of fragility function, the machine learning method provide a better estimation of the building damage rate. Compared with the assessment method based on remote sensing image, the method in this paper is very efficient since spatial distribution of hazard-causing factors, e.g., PGA, can be quickly predicted shortly after an earthquake. The comparison of experiment with and without vulnerability data of buildings shows that data on the vulnerability of buildings are very important to improve the assessment accuracy of building damage.

Changes in the stress state of faults and their surroundings is a highly plausible mechanism explaining earthquake interaction. These stress changes can impact the seismicity rate and the size distribution of earthquakes. However, the effect of large earthquakes on the earthquake size distribution along the Longmenshan fault has not been quantified. We evaluated the levels of the b value for the stable state before and after the large earthquakes on 12 May 2008 (Wenchuan, M_S 8.0) and 20 April 2013 (Lushan, M_S 7.0) along the Longmenshan fault. We found that after the mainshocks, the size distribution of the subsequent earthquakes shifted toward relatively larger events in the Wenchuan aftershock zone (b value decreased from 1.21 to 0.84), and generally remained invariable in the Lushan aftershock zone (b value remained at 0.76). The time required for the b value to return to stable states after both mainshocks was entirely consistent with the time needed by the aftershock depth images to stop visibly changing. The result of the temporal variation of b values shows decreasing trends for the b value before both large earthquakes. Our results are available for assessing the potential seismic risk of the Longmenshan fault as a reference.

Greening dryland ecosystems greatly benefits from significant CO₂ fertilization. This greening trend across global drylands, however, has also been severely constrained by enhancing atmospheric and soil water (SW) deficits. Thus far, the relative offsets in the contributions between the atmospheric vapor pressure deficit (VPD), SW at varying depths, and CO₂ fertilization to vegetation dynamics, as well as the differences in the impacts of decreasing SW at different soil depths on dryland ecosystems over long periods, remain poorly recorded. Here, this study comprehensively explored the relative offsets in the contributions to vegetation dynamics between high VPD, low SW, and rising CO₂ concentration across global drylands during 1982–2018 using process-based models and satellite-observed Leaf Area Index (LAI), Gross Primary Productivity (GPP), and solar-induced chlorophyll fluorescence (SIF). Results revealed that decreasing-SW-induced reductions of LAI in dryland ecosystems were larger than those caused by rising VPD. Furthermore, dryland vegetation was more severely constrained by decreasing SW on the subsurface (7–28 cm) among various soil layers. Notable offsets were found in the contributions between enhanced water constraints and CO₂ fertilization, with the former offsetting approximately 38.49% of the beneficial effects of the latter on vegetation changes in global drylands. Process-based models supported the satellite-observed finding that increasing water constraints failed to overwhelmingly offset significant CO₂ fertilization on dryland ecosystems. This work emphasizes the differences in the impact of SW at different soil depths on vegetation dynamics across global drylands as well as highlights the far-reaching importance of significant CO₂ fertilization to greening dryland ecosystems despite increasing atmospheric and SW constraints.

This paper selects fault source models of typical earthquakes across the globe and uses a volume extending 100 km horizontally from each mainshock rupture plane and 50 km vertically as the primary area of earthquake influence for calculation and analysis. A deep neural network is constructed to model the relationship between elastic stress tensor components and aftershock state at multiple timescales, and the model is evaluated. Finally, based on the aftershock hysteresis model, the aftershock hysteresis effect of the Wenchuan earthquake in 2008 and Tohoku earthquake in 2011 is analyzed, and the aftershock hysteresis effect at different depths is compared and analyzed. The correlation between the aftershock hysteresis effect and the Omori formula is also discussed and analyzed. The constructed aftershock hysteresis model has a good fit to the data and can predict the aftershock pattern at multiple timescales after a large earthquake. Compared with the traditional aftershock spatial analysis method, the model is more effective and fully considers the distribution of actual faults, instead of treating the earthquake as a point source. The expansion rate of the aftershock pattern is negatively correlated with time, and the aftershock patterns at all timescales are roughly similar and anisotropic.

The rapid assessment of building damage within a region after an earthquake is crucial for post-earthquake relief efforts. The current building damage assessment methods primarily employ remote sensing or structural equation modeling, which suffer from poor timeliness, are largely focused on individual buildings, and face difficulties in obtaining structural data. Furthermore, building assessment cases are often applicable only to a single earthquake, exhibiting poor generalization performance when the study area changes. This paper addresses the above issues by selecting historical earthquake cases from different geographical regions. The data includes hazard-causing factors, hazard-affected body factors, and hazard-formative environment factors captured by multi-sensors, as well as damage proxy map (DPM) data. In this study, we developed a technical approach to improve the generalization performance of building earthquake damage assessment using the light gradient boosting machine (LightGBM) and sequential least squares quadratic programming (SLSQP) methods. Among them, the LightGBM method is used to construct the evaluation model, while the SLSQP method is used to seek the optimal combination of single-earthquake-case models when constructing a multi-earthquake-case model. The analysis shows that the constructed multi-earthquake-case model is superior to the baseline model. Compared with the baseline model, the constructed multi-earthquake-case model has an mean absolute error (MAE) reduced by 0.015–0.037, an root mean squared error (RMSE) reduced by 0.021–0.056, and an coefficient of determination (R²) increased by 0.096–0.296. Furthermore, the availability of historical earthquake cases as prior data can improve the effectiveness of post-earthquake building damage assessments and is suitable for damage assessments lacking building structural data.