Comparison of the Dynamic and Static Corner Frequencies in Ground Motion Simulation: Cases Study of Jiuzhaigou Earthquake and Northridge Earthquake

By using the stochastic finite-fault method based on static corner frequency (Model 1) and dynamic corner frequency (Model 2), we calculate the far-field received energy (FRE) and acceleration response spectra (SA) and then compare it with the observed SA. The results show that FRE obtained by the two models depends on the subfault size regardless of high-frequency scaling factor (HSF). Considering the HSF, the results obtained by Model 1 and Model 2 are found to be consistent. Then, similar conclusion was obtained from the Northridge earthquake. Finally, we analyzed the reasons and proposed the areas that need to be improved.

Gravity Observations and Apparent Density Changes before the 2017 Jiuzhaigou Ms7.0 Earthquake and Their Precursory Significance

An Ms7.0 earthquake struck Jiuzhaigou (China) on 8 August 2017. The epicenter was in the eastern margin of the Tibetan Plateau, an area covered by a dense time-varying gravity observation network. Data from seven repeated high-precision hybrid gravity surveys (2014–2017) allowed the microGal-level time-varying gravity signal to be obtained at a resolution better than 75 km using the modified Bayesian gravity adjustment method. The “equivalent source” model inversion method in spherical coordinates was adopted to obtain the near-crust apparent density variations before the earthquake. A major gravity change occurred from the southwest to the northeast of the eastern Tibetan Plateau approximately 2 years before the earthquake, and a substantial gravity gradient zone was consistent with the tectonic trend that gradually appeared within the focal area of the Jiuzhaigou earthquake during 2015–2016. Factors that might cause such regional gravitational changes (e.g., vertical crustal deformation and variations in near-surface water distributions) were studied. The results suggest that gravity effects contributed by these known factors were insufficient to produce gravity changes as big as those observed, which might be related to the process of fluid material redistribution in the crust. Regional change of the gravity field has precursory significance for high-risk earthquake areas and it could be used as a candidate precursor for annual medium-term earthquake prediction.

Spatial Evaluation of Earthquake Disaster Based On Correlation Characteristics And BP Neural Network

Rapid spatial evaluation of disaster after earthquake occurrence is required in the emergency rescue management, due to its significant support for decreasing casualties and property losses. The earthquake-hit population is taken as an example of earthquake disaster to construct the evaluation model using the data from the 2013 Ms7.0 Lushan earthquake. Ten influencing factors are classified into environmental factors and seismic factors. The correlation analysis reveals characteristics that there is a nonlinear relationship between the earthquake-hit population and various factors, and per capita GDP and PGA factor have a stronger correlation with earthquake-hit population. Moreover, the spatial variability of influencing factors would affect the distribution of earthquake-hit population. The earthquake-hit population is evaluated using BP neural network with optimizing training samples based on the spatial characteristics of per capita GDP and PGA factors. Different number of sample points are generated in areas with different value intervals of influencing factors, instead of the random distribution of sample points. The minimum value of RMSE (Root Mean Square Error) from testing set is 18 people/km2, showing good accuracy in the spatial evaluation of earthquake-hit population. Meanwhile, the optimizing samples considering spatial characteristics could improve the convergence speed and generalization capability comparing to random samples. The trained network was generalized to the 2017 Ms7.0 Jiuzhaigou earthquake to verify the prediction accuracy. The evaluation results indicate that BP neural network considering the correlation characteristics of factors has the capability to evaluate the seismic disaster information in space, providing more detailed information for emergency service and rescue operation.

Joint Inversion of Rupture across a Fault Stepover during the 8 August 2017 Mw 6.5 Jiuzhaigou, China, Earthquake

The 8 August 2017 Mw 6.5 Jiuzhaigou earthquake occurred in a tectonically fractured region in southwest China. We investigate the multifault coseismic rupture process by jointly analyzing teleseismic, strong-motion, high-rate Global Positioning System, and Interferometric Synthetic Aperture Radar (InSAR) datasets. We clearly identify two right-stepping fault segments and a compressional stepover based on variations in focal mechanisms constrained by coseismic InSAR deformation data. The average geometric parameters of the northwest and southeast segments are strike = 130°/dip = 57° and strike = 151°/dip = 70°, respectively. The rupture model estimated from a joint inversion of the seismic and geodetic datasets indicates that the rupture initiated on the southeastern segment and jumped to the northwestern segment, resulting in distinctive slip patches on the two segments. A 4-km-long coseismic slip gap was identified around the stepover, consistent with the aftershock locations and mechanisms. The right-stepping segmentation and coseismic rupture across the compressional stepover exhibited by the 2017 Jiuzhaigou earthquake are reminiscent of the multifault rupture pattern during the 1976 Songpan earthquake sequence farther south along the Huya fault system in three successive Ms∼7 events. Although the common features of fault geometry and stepover may control the similarity in event locations and focal mechanisms of the 2017 and 1976 sequences, the significantly wider (~15 km) stepover in the 1976 sequence likely prohibited coseismic rupture jumping and hence reduced seismic hazard.