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Locating Seismo-Conductivity Anomaly before the 2017 MW 6.5 Jiuzhaigou Earthquake in China Using Far Magnetic Stations

Authors :
Zhiqiang Mao
Chieh-Hung Chen
Suqin Zhang
Aisa Yisimayili
Huaizhong Yu
Chen Yu
Jann-Yenq Liu
Source :
Remote Sensing, Vol 12, Iss 11, p 1777 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

Changes in the underlying conductivity around hypocenters are generally considered one of the promising mechanisms of seismo-electromagnetic anomaly generation. Parkinson vectors are indicators of high-conductivity materials and were utilized to remotely monitor conductivity changes during the MW 6.5 Jiuzhaigou earthquake (103.82°E, 33.20°N) on 8 August 2017. Three-component geomagnetic data recorded in 2017 at nine magnetic stations with epicenter distances of 63–770 km were utilized to compute the azimuths of the Parkinson vectors based on the magnetic transfer function. The monitoring and background distributions at each station were constructed by using the azimuths within a 15-day moving window and over the entire study period, respectively. The background distribution was subtracted from the monitoring distribution to mitigate the effects of underlying inhomogeneous electric conductivity structures. The differences obtained at nine stations were superimposed and the intersection of a seismo-conductivity anomaly was located about 70 km away from the epicenter about 17 days before the earthquake. The anomaly disappeared about 7 days before and remained insignificant after the earthquake. Analytical results suggested that the underlying conductivity close to the hypocenter changed before the Jiuzhaigou earthquake. These changes can be detected simultaneously by using multiple magnetometers located far from the epicenter. The disappearance of the seismo-conductivity anomaly after the earthquake sheds light on a promising candidate of the pre-earthquake anomalous phenomena.

Details

Language :
English
ISSN :
20724292
Volume :
12
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.2b39529cea9542f5a5e774d38aec1d36
Document Type :
article
Full Text :
https://doi.org/10.3390/rs12111777