Strength-Pareto Based Multi-objective Adaptive Guided Differential Evolution Algorithm for De-clustering Seismic Catalogs

Abstract Seismicity de-clustering involves categorizing seismic events in a catalog into mainshocks, aftershocks, foreshocks, and background events. Achieving accurate de-clustering is essential for interpreting seismic activity patterns and assessing geological risks. This study addresses the challenge of effectively separating these events in earthquake-prone regions through the development of a modified Multi-objective Adaptive Guided Differential Evolution (mMOAGDE) algorithm. The mMOAGDE algorithm introduces innovative features such as adaptive control parameters and three distinct mutation phases to improve diversity and exploration in the solution space. By utilizing Strength Pareto instead of crowding distance and incorporating an archive control mechanism to maintain a constant size of non-dominated solutions, the algorithm achieves a balance between exploration and exploitation. Furthermore, a binary version of mMOAGDE integrates logical adaptive guided exploration to enhance the separation of seismic events. The algorithm evaluates two objective functions, Global Moran’s Index (GMI) and Allan Factor (AF), in spatial and temporal domains to de-cluster seismic catalogs. Applied to 30 years of earthquake data from regions including Southern California, Indonesia, Iran, and Japan, the mMOAGDE is benchmarked against established algorithms such as ANSGA-III, CMOPSO, MOEA/D-UR, and FLEA. The results demonstrate that the de-clustered catalogs yield GMI values within the desired range (-1 to 1) and maximize AF values. Validation through cumulative plots, $$\lambda$$ -plots, Allan factor plots, and inter-event time versus inter-event distance plots confirms the algorithm’s effectiveness in distinguishing aftershocks from background seismicity. This research highlights the potential of the mMOAGDE algorithm as a powerful tool for seismicity de-clustering, offering significant insights into earthquake dynamics and enhancing seismic risk assessment methodologies.

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Publication Details

Journal
International Journal of Computational Intelligence Systems
Published
2025-12-24
DOI
https://doi.org/10.1007/s44196-025-01091-0
Primary Topic
Seismology and Earthquake Studies
Type
article
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article

Strength-Pareto Based Multi-objective Adaptive Guided Differential Evolution Algorithm for De-clustering Seismic Catalogs

Saswati Nanda, Ashish Sharma, Annuruddh Yadav
International Journal of Computational Intelligence Systems
Seismology and Earthquake Studies
article

Strength-Pareto Based Multi-objective Adaptive Guided Differential Evolution Algorithm for De-clustering Seismic Catalogs

Saswati Nanda, Ashish Sharma, Annuruddh Yadav
article en

Abstract

Abstract Seismicity de-clustering involves categorizing seismic events in a catalog into mainshocks, aftershocks, foreshocks, and background events. Achieving accurate de-clustering is essential for interpreting seismic activity patterns and assessing geological risks. This study addresses the challenge of effectively separating these events in earthquake-prone regions through the development of a modified Multi-objective Adaptive Guided Differential Evolution (mMOAGDE) algorithm. The mMOAGDE algorithm introduces innovative features such as adaptive control parameters and three distinct mutation phases to improve diversity and exploration in the solution space. By utilizing Strength Pareto instead of crowding distance and incorporating an archive control mechanism to maintain a constant size of non-dominated solutions, the algorithm achieves a balance between exploration and exploitation. Furthermore, a binary version of mMOAGDE integrates logical adaptive guided exploration to enhance the separation of seismic events. The algorithm evaluates two objective functions, Global Moran’s Index (GMI) and Allan Factor (AF), in spatial and temporal domains to de-cluster seismic catalogs. Applied to 30 years of earthquake data from regions including Southern California, Indonesia, Iran, and Japan, the mMOAGDE is benchmarked against established algorithms such as ANSGA-III, CMOPSO, MOEA/D-UR, and FLEA. The results demonstrate that the de-clustered catalogs yield GMI values within the desired range (-1 to 1) and maximize AF values. Validation through cumulative plots, $$\lambda$$ -plots, Allan factor plots, and inter-event time versus inter-event distance plots confirms the algorithm’s effectiveness in distinguishing aftershocks from background seismicity. This research highlights the potential of the mMOAGDE algorithm as a powerful tool for seismicity de-clustering, offering significant insights into earthquake dynamics and enhancing seismic risk assessment methodologies.

International Journal of Computational Intelligence SystemsVol. 19(1)
Manipal Academy of Higher Education (IN), Malaviya National Institute of Technology Jaipur (IN)
Openalex Percentile: Top 18%
Seismology and Earthquake Studies
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