首都圈极低频电磁台网区地下电性结构探测

PROBING THE SUBSURFACE ELECTRIC STRUCTURE FOR CSELF NETWORK IN CAPITAL CIRCLE REGION

开展极低频电磁(CSELF)台网区、台站的地下背景电性结构探测对于发挥其在地震预测预报研究中的作用具有重要意义。文中在首都圈CSELF电磁台网的每个台站附近布设了一条短的宽频带大地电磁剖面,共完成了60个测点的数据采集,对数据进行了处理与分析。首先,通过大地电磁一维反演获得每个台站和测点下方的电阻率结构;然后,利用二维反演技术获得每个台站沿剖面的地壳电性结构;最后,对整个台网区的台站数据进行三维反演,获得台网区三维地壳电性结构。结果表明,华北北部阴山-燕山造山带、中部的太行山地区和东部的胶-辽地块的地壳电性结构主要表现为高阻特征,华北平原和山西断陷区域表现为相对低阻;太行山重力梯度带与郯庐断裂带两侧的电性结构差异明显,表现为电性边界带;台网区的电性结构特征与区域地质构造、地震活动性特征具有明显的相关性。首都圈CSELF台网区地下电性结构探测为区域的孕震环境、地震电磁异常信号的产生机理以及地震预测预报研究工作提供了重要的参考资料。

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.