Rapid assessment of the September 5, 2022 ​M 6.8 Luding earthquake in Sichuan, China

Kang, Dengjie; Chen, Wenkai; Zhao, Huaiqun; Wang, Dun

doi:10.1016/j.eqrea.2023.100214

Cited by 11 publications

(2 citation statements)

References 14 publications

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“…Chen et al (2022a) proposed a rapid assessment method that can generate intensity assessment maps within 30 min. The spatial location of the rupture trajectories obtained from the inversion of rupture processes for small-magnitude earthquakes may be less satisfactory (Honda et al, 2011;Yao et al, 2019); however, for these earthquakes, the seismic intensities assessed using aftershock data may be more accurate (Kang et al, 2023). The AL-SM99-fitted curves of the spatial distribution of aftershocks can be used as a cross-reference for the correction of the above inversion results, increasing the speed of the operation using both methods.…”

Section: Time Efficiencymentioning

confidence: 99%

Rapid estimation of seismic intensities by analyzing early aftershock sequences using the robust locally weighted regression program (LOWESS)

Zhao,

Chen,

Zhang

et al. 2023

Nat. Hazards Earth Syst. Sci.

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Abstract. 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 Mw 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 Mw ≥ 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.

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Section: Time Efficiencymentioning

confidence: 99%

Rapid estimation of seismic intensities by analyzing early aftershock sequences using the robust locally weighted regression program (LOWESS)

Zhao,

Chen,

Zhang

et al. 2023

Nat. Hazards Earth Syst. Sci.

Self Cite

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“…Many scholars have established the relationship between the duration of strong ground motion acceleration, velocity, and displacement, as well as the MMI (Trifunac and Brady, 1975;Atkinson and Sonley, 2000;Worden et al, 2012). For the soft data, the formula released by Si in 1999 is used as the GMPE (Si and Midorikawa, 1999); it has been verified in many earthquake cases, and the results have been proven to be reliable and applicable (Chen et al, 2022b;Zhao et al, 2022;Kang et al, 2023;An et al, 2023;Peng et al, 2023;Chen et al, 2023;Zhou et al, 2024;Jia et al, 2024). The ground motion value mainly used in this article is the PGV.…”

Section: Introductionmentioning

confidence: 99%

Bayesian maximum entropy interpolation analysis for rapid assessment of seismic intensity using station and ground motion prediction equations

Kang,

Chen,

Jia

2024

Front. Earth Sci.

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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.

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Stochastic finite-fault modeling of Ms 6.8 Luding earthquake in 2022 and seismic hazard scenario of Moxi fault, southwestern China

Wang

Xie

Shen

et al. 2023

Bull Earthquake Eng

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Rapid assessment of the September 5, 2022 M 6.8 Luding earthquake in Sichuan, China

Cited by 11 publications

References 14 publications

Rapid estimation of seismic intensities by analyzing early aftershock sequences using the robust locally weighted regression program (LOWESS)

Rapid estimation of seismic intensities by analyzing early aftershock sequences using the robust locally weighted regression program (LOWESS)

Bayesian maximum entropy interpolation analysis for rapid assessment of seismic intensity using station and ground motion prediction equations

Stochastic finite-fault modeling of Ms 6.8 Luding earthquake in 2022 and seismic hazard scenario of Moxi fault, southwestern China

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Rapid assessment of the September 5, 2022 ​M 6.8 Luding earthquake in Sichuan, China

Cited by 11 publications

References 14 publications

Rapid estimation of seismic intensities by analyzing early aftershock sequences using the robust locally weighted regression program (LOWESS)

Rapid estimation of seismic intensities by analyzing early aftershock sequences using the robust locally weighted regression program (LOWESS)

Bayesian maximum entropy interpolation analysis for rapid assessment of seismic intensity using station and ground motion prediction equations

Stochastic finite-fault modeling of Ms 6.8 Luding earthquake in 2022 and seismic hazard scenario of Moxi fault, southwestern China

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Rapid assessment of the September 5, 2022 M 6.8 Luding earthquake in Sichuan, China