Polarization Migration of Multi-component Seismic Data for Survey in the Tunnel of Mountain Cities
Polarization Migration of Multi-component Seismic Data for Survey in the Tunnel of Mountain Cities
复制标题
山地城市隧道勘测多分量地震资料偏振偏移
DOI:
10.2113/jeeg24.04.569
复制
发表时间:
2019-12
影响因子:
1
通讯作者:
Heng Zhang
中科院分区:
文献类型:
--
作者:
Bo Wang;Lanying Huang;Shengdong Liu;Fubao Zhou;Biao Jin;Huachao Sun;Heng Zhang
With the development of transportation in the west of China, tunnel construction in mountain cities is becoming very important and widespread. Tunneling safety in the tunnels is usually controlled by faults, and the advanced prediction of faults by seismic detection method has become a research hotspot in the field of engineering geophysics. Unlike seismic exploration on the ground, the sources and receivers are not properly arranged due to the limitation of the tunnel detection space, so as to cause migration artifacts problem in the process of advanced migration imaging. The problem results in inaccurate imaging of the faults. To solve the problem, this paper proposed a new polarization migration method. The method makes use of the polarization characteristic of three-component seismic signals. The principal polarization direction is calculated by Hilbert transform and complex covariance matrix analysis. A weighted function of the principal polarization direction factor is incorporated into the migration calculation. To verify the effectiveness of the polarization migration method, this paper carries out numerical simulations. Test results demonstrate that the artifacts are eliminated by the polarization migration, and occurrence parameters of faults, such as dip and trend are calculated accurately. The field detection case shows that seismic advanced prediction which is based on polarization migration provided parameters of faults in the front of the tunnel face with 100m, and the distance error is less than 2m, and the dip error is less than 3°, which ensures efficient and safe construction of tunneling.
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影响因子:
2
作者:
Liu, Jiangping;Qu, Niannian;Mao, Mao;Zhou, Liming
通讯作者:
Zhou, Liming
DOI:
10.1080/19648189.2016.1170731
发表时间:
2016-04
影响因子:
2.1
作者:
Shucai Li;Jie Song;Jianqing Zhang;Chuanwu Wang;B. Liu;Fangwen Liu;Shengming Ma;L. Nie
通讯作者:
Shucai Li;Jie Song;Jianqing Zhang;Chuanwu Wang;B. Liu;Fangwen Liu;Shengming Ma;L. Nie
影响因子:
0.9
作者:
Bo Wang;Shengdong Liu;Fubao Zhou;Jun Zhang;Fangkun Zheng
通讯作者:
Fangkun Zheng
DOI:
--
发表时间:
2014
期刊:
Chinese journal of rock mechanics and engineering
影响因子:
--
作者:
Li Shuca
通讯作者:
Li Shuca
影响因子:
6.1
作者:
J. Doolittle;B. Jenkinson;D. Hopkins;Michael Ulmer;W. Tuttle
通讯作者:
J. Doolittle;B. Jenkinson;D. Hopkins;Michael Ulmer;W. Tuttle