Clustering features and seismogenesis of the 2014 M6.6 Jinggu earthquake in Yunnan Province, China
Seismic activities can be seen as the composition of background and clustering earthquakes. It is important to identify seismicity clusters from background events. Based on the Nearest Neighbour Distance algorithm proposed by Zaliapin, we use the Gaussian mixture model (GMM) to fit its spatiotempora...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1177821/full |
| _version_ | 1827922391840325632 |
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| author | Jianchang Zheng |
| author_facet | Jianchang Zheng |
| author_sort | Jianchang Zheng |
| collection | DOAJ |
| description | Seismic activities can be seen as the composition of background and clustering earthquakes. It is important to identify seismicity clusters from background events. Based on the Nearest Neighbour Distance algorithm proposed by Zaliapin, we use the Gaussian mixture model (GMM) to fit its spatiotemporal distribution and use the probability corresponding to clustering seismicity in the GMM model as the clustering ratio. After testing with synthetic catalogues under the ETAS (epidemic-type aftershock sequence) model, We believe the method can discriminate cluster events from randomly occurring background seismicity in a more physical background. We investigate the seismicity and its clustering features before the M6.6 Jinggu earthquake in Yunnan Province, China on 7 October 2014. Our results show the following: 1) The seismogenic process of this strong earthquake has three stages, which are already described by the IPE model (the model is similiar to dilatancy diffusion model, growth of cracks is also involved but diffusion of water in and out of the focal region is not required); 2) The main shock might have been caused by the breaking of a local locked barrier in the hypocentre, and the meta-instability stage was sustained for about 1 year on the fault. From this study, we conclude that the evolution of seismicity clustering features can reflect changes in stress in the crust, and it is closely connected to the seismogenic process of a strong earthquake. |
| first_indexed | 2024-03-13T04:41:03Z |
| format | Article |
| id | doaj.art-140295cdb01e4b84943f07a1972847f7 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-03-13T04:41:03Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-140295cdb01e4b84943f07a1972847f72023-06-19T05:43:49ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-06-011110.3389/feart.2023.11778211177821Clustering features and seismogenesis of the 2014 M6.6 Jinggu earthquake in Yunnan Province, ChinaJianchang ZhengSeismic activities can be seen as the composition of background and clustering earthquakes. It is important to identify seismicity clusters from background events. Based on the Nearest Neighbour Distance algorithm proposed by Zaliapin, we use the Gaussian mixture model (GMM) to fit its spatiotemporal distribution and use the probability corresponding to clustering seismicity in the GMM model as the clustering ratio. After testing with synthetic catalogues under the ETAS (epidemic-type aftershock sequence) model, We believe the method can discriminate cluster events from randomly occurring background seismicity in a more physical background. We investigate the seismicity and its clustering features before the M6.6 Jinggu earthquake in Yunnan Province, China on 7 October 2014. Our results show the following: 1) The seismogenic process of this strong earthquake has three stages, which are already described by the IPE model (the model is similiar to dilatancy diffusion model, growth of cracks is also involved but diffusion of water in and out of the focal region is not required); 2) The main shock might have been caused by the breaking of a local locked barrier in the hypocentre, and the meta-instability stage was sustained for about 1 year on the fault. From this study, we conclude that the evolution of seismicity clustering features can reflect changes in stress in the crust, and it is closely connected to the seismogenic process of a strong earthquake.https://www.frontiersin.org/articles/10.3389/feart.2023.1177821/fullclusteringGaussian mixture modelETAS modelseismogenesisforeshock activities |
| spellingShingle | Jianchang Zheng Clustering features and seismogenesis of the 2014 M6.6 Jinggu earthquake in Yunnan Province, China Frontiers in Earth Science clustering Gaussian mixture model ETAS model seismogenesis foreshock activities |
| title | Clustering features and seismogenesis of the 2014 M6.6 Jinggu earthquake in Yunnan Province, China |
| title_full | Clustering features and seismogenesis of the 2014 M6.6 Jinggu earthquake in Yunnan Province, China |
| title_fullStr | Clustering features and seismogenesis of the 2014 M6.6 Jinggu earthquake in Yunnan Province, China |
| title_full_unstemmed | Clustering features and seismogenesis of the 2014 M6.6 Jinggu earthquake in Yunnan Province, China |
| title_short | Clustering features and seismogenesis of the 2014 M6.6 Jinggu earthquake in Yunnan Province, China |
| title_sort | clustering features and seismogenesis of the 2014 m6 6 jinggu earthquake in yunnan province china |
| topic | clustering Gaussian mixture model ETAS model seismogenesis foreshock activities |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1177821/full |
| work_keys_str_mv | AT jianchangzheng clusteringfeaturesandseismogenesisofthe2014m66jingguearthquakeinyunnanprovincechina |