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《Earthquake》 2018-01
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Extracting High-frequency Anomaly from Digital Underground Fluid Data:A Case Study of the 2014 Ludian M_S6.5 Earthquake

WANG Xi-long;JIA Xiao-dong;WANG Bo;WANG Yi-xi;WANG Jun;XIANG Yang;JIN Hao;FU Cong;Earthquake Administration of Liaoning Province;China Earthquake Networks Center;Earthquake Administration of Tianjin Municipal City;Earthquake Administration of Anhui Province;Earthquake Administration of Xinjiang Uygur Autonomous Region;Qinghai Bureau of Geological Survey;  
The sampling frequency of data has been obviously improved after the digital transformation for the seismic subsurface fluids observation in China.And there is a great deal of tectonic information in high frequency observational data,creating favorable conditions for us to catch the precursory information in earthquake nucleation and occurrence process.How to develop data processing and anomaly recognition method matching with the digital high frequency observation data for extracting useful abnormal information become a key issue.We introduced the PDF(probability density distribution)method to analyzed fluid observation data of water level and water temperature at 174 stations in the area of the North-South Seismic Zone before the Ludian M_S6.5 earthquake on August 3,2014.The analysis results show that 10 water level and 7 water temperature data had the high frequency anomalies before the earthquake,and the points of abnormal information were concentrated in the tectonic belts of the central Yunnan sub-block in the southwest.The high-frequency anomalies migrated to the epicenter over time.Based on the analysis of crustal structure and tectonic stress near the earthquake epicenter,as well as the dynamic evolution in a larger area in this region,we found that the spatial distribution of abnormal information had a good consistency with the crustal deformation of the Sichuan-Yunnan region.This result not only shows that the PDF can effectively reflect the regional tectonic stress field,but also has certain reliability and applicability to extract high-frequency anomaly information from fluid observation data.
【Fund】: 中国地震局地震科技星火计划(XH16006Y XH15011);中国地震局地震监测预报经常性项目(17C07ZX022 17C072X020)
【CateGory Index】: P315.7
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