Ambient-Noise Tomography of the Baiyun Gold Deposit in Liaoning, China
SEISMOLOGICAL RESEARCH LETTERS(2020)
摘要
Ambient-noise tomography (ANT) has become an effective method for determining the fine velocity structure of the shallow crust. However, studies on metal mines using this method are rarely reported. To investigate the tectonic background and prospecting of the deep mine in the Baiyun gold deposit (BYGD) of eastern Liaoning Province, China, we use ANT to determine a 3D S-wave velocity structure model of the BYGD. A total of 21 broadband seismic stations were installed in an area of 15 x 14 km, centered at the BYGD. Continuous observations for approximately three months were made. After single-station preprocessing, cross correlation of ambient noise, and phase-weighted stacking, the empirical Green's function for the Rayleigh waves between stations was recovered. Next, group-velocity dispersion with 0.8-3 s periods was measured. A direct inversion method of surface-wave dispersion based on raytracing was then adopted to determine a 3D S-wave velocity structure of the BYGD from the ground surface to a depth of 1.8 km. The results show that the distribution of S-wave velocities in the study area well reflected the geological characteristics of the surface. The velocities were significantly low within the "ore field" and the regional ore-controlling Jianshanzi fault. Combining this with the fact that a large number of magmatic veins were visible inside both structures, it was deduced that both structures had experienced large-scale magmatic intrusion activities, thus confirming that BYGD was a magmatic hydrothermal deposit. The significantly low S-wave velocities beneath the gold deposit extended to a depth of 1.8 km. This might imply the occurrence of blind ore bodies at that depth. The fine velocity structure of the BYGD reconstructed by this study provided a direction for subsequent prospecting of deep regions and demonstrated that ANT has good potential in metal mine exploration.
更多查看译文
关键词
baiyun gold deposit,china,ambient-noise
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要