The lognormal distribution is commonly used to characterize the aleatory variability of ground-motion prediction equations (GMPEs) in probabilistic seismic hazard analysis (PSHA). However, this approach often leads to results without actual physical meaning at low exceedance probabilities. In this paper, we discuss how to calculate PSHA with a low exceedance probability. Peak ground acceleration records from the NGA-West2 database and 15,493 residuals calculated by Campbell-Bozorgnia using the NGA-West2 GMPE were applied to analyze the tail shape of the residuals. The results showed that the generalized Pareto distribution (GPD) captured the characteristics of residuals in the tail better than the lognormal distribution. Further study showed that the shapes of the tails of the distributions of residuals with different magnitudes varied significantly due to the heteroscedasticity of the magnitude; the distribution of residuals with larger magnitudes had a smaller upper limit on the right side. Moreover, the residuals of the three magnitude ranges given in this study were more consistent with the GPD of different parameters at the tail than the lognormal distribution and the GPD fitted by all the residuals, leading to a bounded PSHA hazard curve. Therefore, the lognormal distribution is more representative up to a determined threshold, and the GPD fitted to the residuals of three ranges of magnitude better characterizes the tail for PSHA calculation.
Imaging the lithospheric structure beneath the South China Sea (SCS) not only provides crucial constraints on the tectonic history of southeastern Asia, but also provides an important sample for understanding the evolution of the oceanic lithosphere. However, due to the lack of seismic sampling, the lithospheric structures beneath the SCS are not well resolved. Here, with China National Seismic Network, we observe the “Lid signal” generated by earthquakes below the lithosphere, which propagates through the fast mantle Lid and acts as the first arrival. The Lid signal provides robust constraints on the structure of the lithosphere. Through modeling the Lid signals of events occurred at different depths, we find that the thickness of the lithosphere beneath the northern margin of the SCS is ∼65 km. Across the lithosphere‐asthenosphere boundary, the SH velocity decreases by 6% ∼ 8% within 60 km. Such a large velocity contrast may be due to the presence of melt in the asthenosphere. We also detect another velocity jump at a depth of ∼250 km in both SH and P wave data, which correlates with the depth of the X‐discontinuity. A comparison between the lithospheric structure beneath the SCS basin and other oceanic plates further suggests a hot asthenosphere beneath the SCS, which could be related to the existence of the Hainan Plume nearby.
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