关键词: 首都圈, 极低频电磁台网, 大地电磁, 电性结构

Abstract:

The first control source extremely low frequency(CSELF)electromagnetic observation network through the world, consisting of 30 fixed stations located in the Beijing captical circle region(15 staions)and the sourthern secton of the north-south earthquake belt(15 stations), China, has been established under the support of the wireless electromagnetic method(WEM)project, one of the national science and technology infrastructure construction projects during the 11th Five-year Plan period. As a subsystem of the WEM project, the CSELF network is mainly to study the relationship between elctromagnetic anomalies and mechanisms of earthquake, and further improve our ability to monitor and predict earthquakes by monitoring real-time dynamic changes in both electromagnetic fields and subsurface electric structure. Carrying out the detection of the underground background electric structure in the CSELF network area/station is an important part of this project and of great significance to play its role in the study of earthquake prediction and forecast. In this paper, we elaborate how to acquire the subsurface electric structure of the CSELF network in the Beijing captical circle region and make a simple explanation for the structure. Firstly, a short magnetotelluric(MT)profile, almostly perpendicular to the regional geological strike, was deployed at each station of the CSELF network in the capital circle region during the 2016 and a total of 60 broadband MT sites was collected using ADU -07e systems. Then, all the time series data were processed carefully using the robust method with remote reference technique to MT transfer functions. MT data quality was assessed using the D+algorithm. In general, data at most sites are of high quality as shown by the good consistency in the apparent resistivity and phase curves. Different impedance tensor decomposition methods including the phase tensor analysis, Groom and Bailey(GB)tensor decompositon, and statistical image method based on multi-site, multi-frequency tensor decompositon were used to analyze data dimensionality and directionality. For data inversion, on the one hand, one-dimensional(1-D)subsurface electrical resistivity structures at each station and MT site were derived from 1-D adaptive regularized MT inversion algorithm. On the other hand, we also imaged the 2-D electric structures along the short MT profile by the nonlinear conjugate gradients inversion algorithm at each station. Robustness of all 2-D structures along each short profile were verified by sensitivity tests. Although fixed stations and MT sites are limited and distributed unevenly, the 3-D inversion of 15 stations was also performed to produce a 3-D crustal electrical resistivity model for the entire network using the modular system for 3-D MT inverson: ModEM based on the nonlinear conjugate gradients algorithem. Intergrating 1-D, 2-D and 3-D inversion results, the resistivity structure beneath the CSELF network in captical circle region revealed some significant features: The crustal electrical structures are mainly characterized by high resistivity beneath the Yinshan-Yanshan orogenic belt in the northern margin of North China, the Taihangshan area in the middle, the Jiao-Liao block in the east, while the North China Plain and Shanxi depression areas have relatively lower resistivity in the crust; There are obvious electrical resistivity difference on both sides of the gravity gradient of Taihang Mountains and the Tanlu fault zone, which indicates they could be manifested as an electric structure boundary zone, respectively. Overall, the electric structure characteristics of the entire network area shows high correspondence with the regional geological structure and earthquake activity to some extent. In summary, implementing the detection of underground electrical resistivity structure in the CSELF network of the capital circle region will provide important foundations for the researches on the regional seismogenic environment, the generation mechanism of seismic electromagnetic anomaly signals, and earthquake prediction and forecast.

Key words: capital circle region, CSELF network, magnetotelluric, electric structure