Spatiotemporal Analysis of the Background Seismicity Identified by Different Declustering Methods in Northern Algeria and Its Vicinity
The main purpose of this paper was to, for the first time, analyse the spatiotemporal features of the background seismicity of Northern Algeria and its vicinity, as identified by different declustering methods (specifically: the Gardner and Knopoff, Gruenthal, Uhrhammer, Reasenberg, Nearest Neighbou...
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MDPI AG
2023-02-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/12/3/237 |
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| author | Amel Benali Abdollah Jalilian Antonella Peresan Elisa Varini Sara Idrissou |
| author_facet | Amel Benali Abdollah Jalilian Antonella Peresan Elisa Varini Sara Idrissou |
| author_sort | Amel Benali |
| collection | DOAJ |
| description | The main purpose of this paper was to, for the first time, analyse the spatiotemporal features of the background seismicity of Northern Algeria and its vicinity, as identified by different declustering methods (specifically: the Gardner and Knopoff, Gruenthal, Uhrhammer, Reasenberg, Nearest Neighbour, and Stochastic Declustering methods). Each declustering method identifies a different declustered catalogue, namely a different subset of the earthquake catalogue that represents the background seismicity, which is usually expected to be a realisation of a homogeneous Poisson process over time, though not necessarily in space. In this study, a statistical analysis was performed to assess whether the background seismicity identified by each declustering method has the spatiotemporal properties typical of such a Poisson process. The main statistical tools of the analysis were the coefficient of variation, the Allan factor, the Markov-modulated Poisson process (also named switched Poisson process with multiple states), the Morisita index, and the L–function. The results obtained for Northern Algeria showed that, in all cases, temporal correlation and spatial clustering were reduced, but not totally eliminated in the declustered catalogues, especially at long time scales. We found that the Stochastic Declustering and Gruenthal methods were the most successful methods in reducing time correlation. For each declustered catalogue, the switched Poisson process with multiple states outperformed the uniform Poisson model, and it was selected as the best model to describe the background seismicity in time. Moreover, for all declustered catalogues, the spatially inhomogeneous Poisson process did not fit properly the spatial distribution of earthquake epicentres. Hence, the assumption of stationary and homogeneous Poisson process, widely used in seismic hazard assessment, was not met by the investigated catalogue, independently from the adopted declustering method. Accounting for the spatiotemporal features of the background seismicity identified in this study is, therefore, a key element towards effective seismic hazard assessment and earthquake forecasting in Algeria and the surrounding area. |
| first_indexed | 2024-03-11T06:56:49Z |
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| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-11T06:56:49Z |
| publishDate | 2023-02-01 |
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| record_format | Article |
| series | Axioms |
| spelling | doaj.art-c7c181b7c59d4f42ab9577a88595d5fc2023-11-17T09:34:41ZengMDPI AGAxioms2075-16802023-02-0112323710.3390/axioms12030237Spatiotemporal Analysis of the Background Seismicity Identified by Different Declustering Methods in Northern Algeria and Its VicinityAmel Benali0Abdollah Jalilian1Antonella Peresan2Elisa Varini3Sara Idrissou4Division Aléas et Risques Géologiques, Centre de Recherche en Astronomie, Astrophysique et Géophysique, Route de l’Observatoire, Bouzareah, Algiers 16340, AlgeriaDepartment of Statistics, Razi University, Bagh-e-Abrisham, Kermanshah 67144-1511, IranSeismological Research Centre, National Institute of Oceanography and Applied Geophysics–OGS, Via Treviso 55, 33100 Udine, ItalyInstitute of Applied Mathematics and Information Technologies Enrico Magenes, National Research Council, Via Corti 12, 20133 Milan, ItalyDépartement de Génie Civil, Faculté de Technologie, Université Abderrahmane Mira, Route de Targa Ouzemmour, Béjaia 06000, AlgeriaThe main purpose of this paper was to, for the first time, analyse the spatiotemporal features of the background seismicity of Northern Algeria and its vicinity, as identified by different declustering methods (specifically: the Gardner and Knopoff, Gruenthal, Uhrhammer, Reasenberg, Nearest Neighbour, and Stochastic Declustering methods). Each declustering method identifies a different declustered catalogue, namely a different subset of the earthquake catalogue that represents the background seismicity, which is usually expected to be a realisation of a homogeneous Poisson process over time, though not necessarily in space. In this study, a statistical analysis was performed to assess whether the background seismicity identified by each declustering method has the spatiotemporal properties typical of such a Poisson process. The main statistical tools of the analysis were the coefficient of variation, the Allan factor, the Markov-modulated Poisson process (also named switched Poisson process with multiple states), the Morisita index, and the L–function. The results obtained for Northern Algeria showed that, in all cases, temporal correlation and spatial clustering were reduced, but not totally eliminated in the declustered catalogues, especially at long time scales. We found that the Stochastic Declustering and Gruenthal methods were the most successful methods in reducing time correlation. For each declustered catalogue, the switched Poisson process with multiple states outperformed the uniform Poisson model, and it was selected as the best model to describe the background seismicity in time. Moreover, for all declustered catalogues, the spatially inhomogeneous Poisson process did not fit properly the spatial distribution of earthquake epicentres. Hence, the assumption of stationary and homogeneous Poisson process, widely used in seismic hazard assessment, was not met by the investigated catalogue, independently from the adopted declustering method. Accounting for the spatiotemporal features of the background seismicity identified in this study is, therefore, a key element towards effective seismic hazard assessment and earthquake forecasting in Algeria and the surrounding area.https://www.mdpi.com/2075-1680/12/3/237statistical seismologydeclusteringMarkov-modulated Poisson processAllan factorMorisita indexL–function |
| spellingShingle | Amel Benali Abdollah Jalilian Antonella Peresan Elisa Varini Sara Idrissou Spatiotemporal Analysis of the Background Seismicity Identified by Different Declustering Methods in Northern Algeria and Its Vicinity Axioms statistical seismology declustering Markov-modulated Poisson process Allan factor Morisita index L–function |
| title | Spatiotemporal Analysis of the Background Seismicity Identified by Different Declustering Methods in Northern Algeria and Its Vicinity |
| title_full | Spatiotemporal Analysis of the Background Seismicity Identified by Different Declustering Methods in Northern Algeria and Its Vicinity |
| title_fullStr | Spatiotemporal Analysis of the Background Seismicity Identified by Different Declustering Methods in Northern Algeria and Its Vicinity |
| title_full_unstemmed | Spatiotemporal Analysis of the Background Seismicity Identified by Different Declustering Methods in Northern Algeria and Its Vicinity |
| title_short | Spatiotemporal Analysis of the Background Seismicity Identified by Different Declustering Methods in Northern Algeria and Its Vicinity |
| title_sort | spatiotemporal analysis of the background seismicity identified by different declustering methods in northern algeria and its vicinity |
| topic | statistical seismology declustering Markov-modulated Poisson process Allan factor Morisita index L–function |
| url | https://www.mdpi.com/2075-1680/12/3/237 |
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