Identification of Precursory Anomalies before Moderate-strong Earthquakes in Junction of Shanxi-Hebei-Inner Mongolia Area

Li Hong,J Sun,M Nawaz,X Liu

doi:10.1051/e3sconf/202020601011

Abstract

In this paper, we take the Junction of Shanxi-Hebei-Inner Mongolia area as study region using earthquake corresponding relevancy spectrum method (ECRS method) to identify comprehensive precursory anomalies before moderate-strong earthquake. On base of single-parameter relevancy spectrum database with target earthquake magnitude as Ms4.7 and initial earthquake magnitude as Ms1, we carry on multi-parameter analysis and find that result with time interval of 9 months and anomaly threshold with 0.40 times standard deviation has better prediction efficiency. Its anomaly corresponding rate and earthquake corresponding rate are 6/10 and 9/9 respectively.

Highlights

Introduction and Method BriefThe Junction of Shanxi-Hebei-Inner Mongolia is a historical earthquake-prone area located at the intersection of Zhangjiakou-Bohai tectonic belt and Fenwei earthquake belt
A lot of research indicate that different observed records and many seismic parameters deriving from earthquake catalogues may show different kinds of precursory anomaly before strong earthquakes
Such as precursory recognition method of comprehensive information entropy [1], dynamic subordinate function method based on fuzzy mathematics[2], multipoint group slope and synthetic information flow method[3,4], normalized rate method[5]

Summary

Introduction and Method Brief

The Junction of Shanxi-Hebei-Inner Mongolia is a historical earthquake-prone area located at the intersection of Zhangjiakou-Bohai tectonic belt and Fenwei earthquake belt. Many scholars have done lots of studies to explore different methods to extract and identify precursory anomaly before strong earthquakes based on mathematical statistics. Earthquake corresponding relevancy spectrum method (ECRS) was proposed by Wang Haitao et al to identify comprehensive precursory anomaly before earthquakes and has been applied in many studies areas [6,7,8,9,10,11,12,13,14]. We can convert the time sequence of different seismic parameters j ( j 1, 2,..., k ) into corresponding relevancy time sequence Pij ( i 1, 2,..., n ) according to the affiliation value range point-by-point. We can obtain the sliding average correlation of different seismic parameters of time interval t ( t =3,6,12,18) (unit: month) by formula (10). Sliding extreme average correlation parameters can be obtained as follow, 1, 2,..., k (12) of multi-

Data Preparation

Calculation results

Conclusions and discussions