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Study of the electromechanical coupling process before the 2020 M(s) 6.4 Yutian, China earthquake

The observation of electromechanical coupling might be used as an important tool to detect pre-seismic changes associated with the preparation of earthquakes. This paper attempts to study the electromechanical coupling process before the large earthquakes by using the load/unload response ratio (LUR...

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Detalles Bibliográficos
Autores principales: Yu, Chen, Wang, Shuyan, Yu, Huaizhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586967/
https://www.ncbi.nlm.nih.gov/pubmed/36271246
http://dx.doi.org/10.1038/s41598-022-22532-2
Descripción
Sumario:The observation of electromechanical coupling might be used as an important tool to detect pre-seismic changes associated with the preparation of earthquakes. This paper attempts to study the electromechanical coupling process before the large earthquakes by using the load/unload response ratio (LURR) approach in which the geo-electric data and Benioff strain of small earthquakes were adopted as the data input. The variation of Coulomb failure stress induced by earth tides on the fault surface of the mainshock is applied to differentiate the loading and unloading stages. Using this technique, we test the geo-electric data recorded at the Hotan observatory near the epicenter of 2020 Yutian M(s) 6.4 earthquake. Results show that the LURR time sequence fluctuated around 1.0 for many years and reached significant high peaks at the beginning of 2020. More importantly, this evolution correlates well with the LURR time series calculated by using the Benioff strain of small earthquakes within the circular region of 300 km radius centered at the epicenter. The underlying physics of the changes should be caused by the fluid infiltration derived from pre-seismic rock dilatancy. The corresponding volume variations in the crust could be found in the geophysical observation time series in the same neighborhoods.