Paper
19 January 2024 Performance verification of Shandong earthquake early warning station network based on spatial analysis
Le Yang, Haitao Yin, Zhijun Feng, Pifeng Ma
Author Affiliations +
Proceedings Volume 12980, Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023); 1298029 (2024) https://doi.org/10.1117/12.3020885
Event: Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023), 2023, Lianyungang, China
Abstract
As one of the key earthquake early warning area, it has very important practical significance to study on spatial distribution and capability of earthquake early warning network in Shandong province. According to the spatial distribution characteristics of 90 base stations, 148 basic stations and 1230 general stations, which are affiliated with Shandong Sub-project of “National Seismic Intensity Rapid Reporting and Early Warning” Project , the capability is analyzed and studied base on historical earthquake events, active fault and seismic peak ground acceleration zonation. The results show that: 1) The blind area of earthquake early warning network in Shandong province is 21.55kmwhichis affected with the instrument delay time of the early warning system. 2) The density of earthquake early warning network near historical earthquake events and high ground motion of seismic peak ground acceleration is higher, which can provide effective earthquake early warning. However, the monitoring intensity of some active faults is insufficient and the blind area is large, which still needs further optimization. 3) The monitoring capability of the speedometer stations is ML0.6. which is ML2.3 of the accelerometer stations and ML3.7 of the intensity meter stations.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Le Yang, Haitao Yin, Zhijun Feng, and Pifeng Ma "Performance verification of Shandong earthquake early warning station network based on spatial analysis", Proc. SPIE 12980, Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023), 1298029 (19 January 2024); https://doi.org/10.1117/12.3020885
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