Interval Determination Strategy for Bayesian Inversion of Seismic Source Parameters Under Uncertain Interval Conditions.
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| Title: | Interval Determination Strategy for Bayesian Inversion of Seismic Source Parameters Under Uncertain Interval Conditions. |
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| Authors: | Wang, Leyang1,2 (AUTHOR), Xi, Can1,2 (AUTHOR) xican@ecut.edu.cn, Xu, Guangyu1,2 (AUTHOR), Sun, Zhanglin1,2 (AUTHOR), Wu, Fei1,2 (AUTHOR) |
| Source: | Remote Sensing. Sep2025, Vol. 17 Issue 18, p3151. 22p. |
| Subjects: | Markov chain Monte Carlo, Interval analysis, Earthquake hazard analysis, Constrained optimization, Mathematical analysis, Geophysical observations, Seismology, Bayesian field theory |
| Abstract: | Highlights: What are the main findings? Two novel strategies—'CFI' (Converge First, Then Interval) and 'IVI' (Interval Value Iteration)—are proposed to prevent Markov Chain Monte Carlo (MCMC) algorithms from becoming trapped in local optima during the Bayesian inversion of seismic source parameters. The 'IVI' strategy, when paired with an MCMC algorithm using a normally distribut-ed step size, significantly reduces the root-mean-square error (RMSE) of the inversion results. What is the implication of the main finding? Application to the 2022 Mw6.6 Menyuan earthquake demonstrates the method's practicality, yielding fault parameters (depth, strike, dip, rake) closer to the GCMT solution with low fitting residuals. The strategies provide clear guidance for inversion settings: use 'IVI' when parameter ranges are unknown, 'CFI' when an approximate range is known, and standard con-straints only when both the interval and a reliable initial model are fully known. Using a Bayesian framework to invert earthquake source parameters from multi-source geodetic data has become an important research direction. To address the issue of Markov Chain Monte Carlo (MCMC) algorithms getting stuck in local optima during nonlinear inversion of fault geometric parameters, which is often caused by improperly set parameter bounds or large deviations in the initial values, this study proposes two strategies: 'CFI (Converge First, Then Interval)' and 'IVI (Interval Value Iteration)'. Tests with 12 different experimental setups show that both strategies can prevent the chain from getting trapped in local optima. Among them, the 'IVI' strategy, when used with MCMC algorithms where the step size follows a normal distribution, can also significantly reduce the root-mean-square error. To verify its applicability, the 'IVI' strategy was applied to the Bayesian inversion of the 2022 Menyuan Mw6.6 earthquake. The results show that the inverted values for fault depth, strike, dip, and rake angles are closer to the GCMT results, with ascending and descending track fitting residuals of 2.71 cm and 2.64 cm, respectively. The conclusion of this paper is to recommend the 'IVI' strategy when the range of source parameters is unclear. If the approximate range of parameters is known, the 'CFI' strategy can be applied. The original interval constraint method is recommended when the parameter bounds are fully determinable and a reliable initial model of seismic source parameters is obtainable. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Two novel strategies—'CFI' (Converge First, Then Interval) and 'IVI' (Interval Value Iteration)—are proposed to prevent Markov Chain Monte Carlo (MCMC) algorithms from becoming trapped in local optima during the Bayesian inversion of seismic source parameters. The 'IVI' strategy, when paired with an MCMC algorithm using a normally distribut-ed step size, significantly reduces the root-mean-square error (RMSE) of the inversion results. What is the implication of the main finding? Application to the 2022 Mw6.6 Menyuan earthquake demonstrates the method's practicality, yielding fault parameters (depth, strike, dip, rake) closer to the GCMT solution with low fitting residuals. The strategies provide clear guidance for inversion settings: use 'IVI' when parameter ranges are unknown, 'CFI' when an approximate range is known, and standard con-straints only when both the interval and a reliable initial model are fully known. Using a Bayesian framework to invert earthquake source parameters from multi-source geodetic data has become an important research direction. To address the issue of Markov Chain Monte Carlo (MCMC) algorithms getting stuck in local optima during nonlinear inversion of fault geometric parameters, which is often caused by improperly set parameter bounds or large deviations in the initial values, this study proposes two strategies: 'CFI (Converge First, Then Interval)' and 'IVI (Interval Value Iteration)'. Tests with 12 different experimental setups show that both strategies can prevent the chain from getting trapped in local optima. Among them, the 'IVI' strategy, when used with MCMC algorithms where the step size follows a normal distribution, can also significantly reduce the root-mean-square error. To verify its applicability, the 'IVI' strategy was applied to the Bayesian inversion of the 2022 Menyuan Mw6.6 earthquake. The results show that the inverted values for fault depth, strike, dip, and rake angles are closer to the GCMT results, with ascending and descending track fitting residuals of 2.71 cm and 2.64 cm, respectively. The conclusion of this paper is to recommend the 'IVI' strategy when the range of source parameters is unclear. If the approximate range of parameters is known, the 'CFI' strategy can be applied. The original interval constraint method is recommended when the parameter bounds are fully determinable and a reliable initial model of seismic source parameters is obtainable. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17183151 |
