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It is well known that earthquake brings about gigantic direct and indirect economic losses, such as building and lifeline system damages, every year at the global scales. Thus, efficient and accurately estimate the economic loss after earthquake is one of the important factors for the post-earthquake reconstruction and the recovery activity of ordinary life. To estimate the earthquake-induced direct economic losses (DEL), a regression empirical method based on the earthquake-induced direct building damage economic losses (DBDEL) is proposed in this study. Firstly, a linear model between DBDEL of the entire earthquake impact areas and the DBDEL of areas with earthquake intensity greater than VII are acquired based six historical earthquakes occurred at the mainland China. Then, another linear model between the DEL and the entire DBDEL are obtained based on 16 historical earthquakes. The DBDEL and DEL of the historical earthquake records are acquired from field investment. To evaluate the accuracy of the proposed method, the DEL of 2013 Lushan M 7.0 earthquake estimated. Using the field invested and high-resolution remote sensing image estimated DBDEL at the intensity greater than VII areas, the corresponding DELs are estimated based on the proposed method, respectively, with the relative error of 3.2% and 57.0%. In addition, an empirical DEL method based on earthquake magnitude and intensity is also used to estimate the Lushan earthquake DEL, with the relative error of 90.0%. The result indicates that the proposed method can properly estimate DEL based on the field invested or remote sensed DBDEL.

To identify earthquake-damage areas after an earthquake occurs, a method based on Landsat-8 operational land imager (OLI) surface reflectance (SR) products is proposed. The SR pixels with the influences of clouds, cloud shadows, heavy aerosols, or instrument saturation are eliminated. Then, the mean and standard deviation (STD) images of SR products for two years before an earthquake are acquired; the mean images of the first three post-earthquake images are also obtained. The differences between post- and pre-earthquake mean SR images are taken to reflect the effects of the earthquake on SR. As we focus mainly on the impact of earthquakes on populated areas such as cities or villages, the pixels with Visible Infrared Imaging Radiometer Suite (VIIRS) day and night band annual average nighttime light products greater than 0 or Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data labeled ‘urban and built-up’ are taken as the study area for this work. Next, the areas with post- and pre-earthquake SR mean image differences greater than 0.1 and greater than 2 times the local STD are taken to represent earthquake-damaged areas. Finally, this method is applied to the 2015 Nepal moment magnitude (Mw) 7.8 earthquake. Compared with the actual damage data provided by the Nepal Earthquake 2015: Disaster Recovery and Reconstruction Information Platform (NDRRIP), the results show that the coefficient of determination between NDRRIP fully damaged buildings and Landsat-8 OLI SR data estimated damage pixels is 0.80 for the 69 Nepal districts not heavily influenced by snows.

After the earthquake, the correct and efficient earthquake emergency response modes of individuals are crucial for reducing casualties. Based on the keyword database of the earthquake emergency response behaviors of individuals, this research used the machine learning method to construct a decision tree of the earthquake emergency response modes. Through the decision tree, this study extracted decision rules that can predict the emergency response decisions of individuals in future earthquake and also determined the relative importance of six different factors (Seismic intensity > Social context >Location>Action>Age>Gender) in determining response behaviors. The set of rules was quite effective when evaluated using the test individuals with an overall accuracy of 85.23%. And then, combined with the cultural background in China, we had extracted and analyzed the main response modes of individuals at seismic intensities I-V, VI, VII, VIII-IX stricken regions by earthquake. The decision rules and main response modes of individuals were scientific bases for the targeted earthquake emergency safety education and the government emergency rescue.

The county town of Beichuan county, China, experienced catastrophic destruction due to landslides induce by the 2008 Wenchuan earthquake. In consideration of the special location of the county town, this paper selected the landslides induced in the town as representative of large-scale near-rupture landslides, and quantitatively analyzed why the landslide damage was so destructive in the town by using strong motion data obtained from the Wenchuan earthquake in the Longmenshan area. Three methods were employed to estimate the landslide damage using strong motion data. (1) Peak ground accelerations (PGAs) on the hanging wall were used to evaluate the PGAs on the landslide sites in the town. The evaluated average PGAs were all greater than 1 g, indicating that the ground motion intensity was very strong during the earthquake. (2) Acceleration time histories, from another station with similar geological conditions to the town, were used to evaluate the critical acceleration changing range, and the estimated values showed the geological conditions were very susceptible to earthquakes. (3) Acceleration time histories, from two stations on the hanging and foot walls of the rupture, and near the town, were used to calculate the Newmark displacements, and all the evaluated displacements indicated that landslides were very likely. The results show that the slopes, susceptible to earthquakes in the Beichuan county town, were easily triggered under such strong ground-motion intensity and developed into large-scale catastrophic events.

Landslides induced by the 2008 Wenchuan earthquake in the Longmenshan area were relatively well instrumented, which makes it possible to investigate the landslides using ground motion records. Firstly, this paper analyzes the data from Wenchuan earthquake on both regional and local site scale. The analyses show that the Newmark accumulative displacement calculated from the ground motion recorded in a particular geological hazard zone corresponds to the hazard intensity in that zone; the larger the displacement, the more serious the geologic hazard. The calculated result also shows that the displacement is related to the Arias intensity, which represents the total energy released during the earthquake at the observation site. Secondly, this paper constructs an evaluation model of Newmark displacement calculated with Arias intensities to estimate the subsequent slope failure resulting from the earthquake. The calculated results based on the model fit well with the distribution of actual landslides, suggesting that this method is useful for hazard evaluation. Therefore, this type of model can be used for estimating regional-scale distribution of earthquake-induced landslides and their associated hazards immediately after an earthquake.

The MS8.0 Wenchuan earthquake induced severe landslide hazards. For the first time in China, large numbers of strong motion records were obtained during the Wenchuan earthquake, providing the opportunity to study the relationships between ground-motion parameters and the earthquake-induced landslides. Nearly 40 groups of records from the main shock distributed along the Longmenshan fault lines were used to carry out this study. The results appropriate to the Longmenshan area are as follows: ① The threshold of the peak ground acceleration (PGA) is about 0.7 m/s2. When the PGA reaches 2 m/s2, the landslide hazards are very serious; ② The threshold of the peak ground velocity (PGV) is about 0.5 m/s. When the PGV reaches 1.5 m/s, severe landslide hazards will be induced; ③ The threshold for the Arias intensity (Ia) is about 0.2 m/s. When the Ia in one horizontal direction reaches 2 m/s, landslide hazards will be very serious; ④ As for the relevance order of the parameters to earthquake-induced landslides, Ia is the leading parameter, followed by PGV, and finally PGA. The results presented in this paper are consistent with the results from other studies, indicating that the threshold of the ground motion parameters for strong earthquakes is of the same order of magnitude as that of moderate earthquakes. Landslide density of local sites fluctuated with the increase of ground motion intensity if the thresholds were reached. When the upper limits are exceeded, the landslide density remains at a certain level with relatively little variation.