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Forecasting earthquake recurrence with long-term fault memory and variable stress release: insights from the Main Himalayan Thrust.

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Title: Forecasting earthquake recurrence with long-term fault memory and variable stress release: insights from the Main Himalayan Thrust.
Authors: Khan, Muhammad Yousaf1 (AUTHOR) yousaf_0334@yahoo.com, Cotton, Fabrice2 (AUTHOR), Schorlemmer, Danijel2 (AUTHOR), Iqbal, Talat1 (AUTHOR)
Source: Stochastic Environmental Research & Risk Assessment. Jul2026, Vol. 40 Issue 7, p1-16. 16p.
Subjects: Paleoseismology, Geologic faults, Earthquake hazard analysis, Stress relaxation (Mechanics), Earthquake prediction
Abstract: Paleoseismic studies show large variability in earthquake recurrence intervals along active faults, often manifesting as clusters of events separated by long quiescent periods. Traditional probabilistic models usually attribute this variability to randomness, overlooking the effects of residual strain and partial stress release observed in historical earthquake records. To address this limitation, we apply the recently developed Generalized Long-Term Fault Memory model to the paleoseismic record of the Main Himalayan Thrust fault. In this study, we extend the Generalized Long-Term Fault Memory framework by incorporating variable stress release values instead of assuming a constant release, as in previous formulations, allowing for a more flexible representation of evolving stress and strain conditions on the fault. This modification allows the model to better represent the evolving stress and strain state of the fault. Unlike conventional models such as Poisson, lognormal, Brownian passage time, and Weibull models, which often predict constant or declining earthquake probabilities after the mean recurrence interval, the Generalized Long-Term Fault Memory model explicitly accounts for residual strain and shows that earthquake probability increases with additional accumulated strain. Application of the model to the Main Himalayan Thrust paleoseismic data demonstrates that the Generalized Long-Term Fault Memory model provides improved forecasts for the 1833 earthquake, while renewal models perform comparatively better for the 2015 event. Complementary simulation studies support the consistency of these results in a statistical sense across ensemble realizations. The Generalized Long-Term Fault Memory model estimates a recurrence interval of approximately 66 years along the Main Himalayan Thrust fault. Overall, the combined use of the Generalized Long-Term Fault Memory model and renewal models offers a flexible and physically inspired framework for earthquake forecasting and seismic hazard assessment. [ABSTRACT FROM AUTHOR]
Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature 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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An: 194612091
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  Data: Paleoseismic studies show large variability in earthquake recurrence intervals along active faults, often manifesting as clusters of events separated by long quiescent periods. Traditional probabilistic models usually attribute this variability to randomness, overlooking the effects of residual strain and partial stress release observed in historical earthquake records. To address this limitation, we apply the recently developed Generalized Long-Term Fault Memory model to the paleoseismic record of the Main Himalayan Thrust fault. In this study, we extend the Generalized Long-Term Fault Memory framework by incorporating variable stress release values instead of assuming a constant release, as in previous formulations, allowing for a more flexible representation of evolving stress and strain conditions on the fault. This modification allows the model to better represent the evolving stress and strain state of the fault. Unlike conventional models such as Poisson, lognormal, Brownian passage time, and Weibull models, which often predict constant or declining earthquake probabilities after the mean recurrence interval, the Generalized Long-Term Fault Memory model explicitly accounts for residual strain and shows that earthquake probability increases with additional accumulated strain. Application of the model to the Main Himalayan Thrust paleoseismic data demonstrates that the Generalized Long-Term Fault Memory model provides improved forecasts for the 1833 earthquake, while renewal models perform comparatively better for the 2015 event. Complementary simulation studies support the consistency of these results in a statistical sense across ensemble realizations. The Generalized Long-Term Fault Memory model estimates a recurrence interval of approximately 66 years along the Main Himalayan Thrust fault. Overall, the combined use of the Generalized Long-Term Fault Memory model and renewal models offers a flexible and physically inspired framework for earthquake forecasting and seismic hazard assessment. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Stochastic Environmental Research & Risk Assessment is the property of Springer Nature 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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            NameFull: Khan, Muhammad Yousaf
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              M: 07
              Text: Jul2026
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              Y: 2026
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