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. |
|---|---|
| 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] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: egs DbLabel: Engineering Source An: 188282597 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Interval Determination Strategy for Bayesian Inversion of Seismic Source Parameters Under Uncertain Interval Conditions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Leyang%22">Wang, Leyang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xi%2C+Can%22">Xi, Can</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> xican@ecut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xu%2C+Guangyu%22">Xu, Guangyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Zhanglin%22">Sun, Zhanglin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Fei%22">Wu, Fei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Sep2025, Vol. 17 Issue 18, p3151. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Markov+chain+Monte+Carlo%22">Markov chain Monte Carlo</searchLink><br /><searchLink fieldCode="DE" term="%22Interval+analysis%22">Interval analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Earthquake+hazard+analysis%22">Earthquake hazard analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Constrained+optimization%22">Constrained optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+analysis%22">Mathematical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Geophysical+observations%22">Geophysical observations</searchLink><br /><searchLink fieldCode="DE" term="%22Seismology%22">Seismology</searchLink><br /><searchLink fieldCode="DE" term="%22Bayesian+field+theory%22">Bayesian field theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs17183151 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 3151 Subjects: – SubjectFull: Markov chain Monte Carlo Type: general – SubjectFull: Interval analysis Type: general – SubjectFull: Earthquake hazard analysis Type: general – SubjectFull: Constrained optimization Type: general – SubjectFull: Mathematical analysis Type: general – SubjectFull: Geophysical observations Type: general – SubjectFull: Seismology Type: general – SubjectFull: Bayesian field theory Type: general Titles: – TitleFull: Interval Determination Strategy for Bayesian Inversion of Seismic Source Parameters Under Uncertain Interval Conditions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Leyang – PersonEntity: Name: NameFull: Xi, Can – PersonEntity: Name: NameFull: Xu, Guangyu – PersonEntity: Name: NameFull: Sun, Zhanglin – PersonEntity: Name: NameFull: Wu, Fei IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 17 – Type: issue Value: 18 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
