Imaging karatungk Cu-Ni mine in Xinjiang, Western China with a passive seismic array

Peixiao Du, Jing Wu, Yang Li, Jian Wang, Chunming Han, Mark Douglas Lindsay, Huaiyu Yuan, Liang Zhao, Wenjiao Xiao

Research output: Contribution to journalArticlepeer-review

11 Citations (Web of Science)


Karatungk Mine is the second-largest Cu-Ni sulfide mine in China. However, the detailed structure beneath the mine remains unclear. Using continuous waveforms recorded by a dense temporary seismic array, here we apply ambient noise tomography to study the shallow crustal structure of Karatungk Mine down to ~1.3 km depth. We obtain surface-wave dispersions at 0.1–1.5 s by calculating cross-correlation functions, which are inverted for 3D shear-wave structure at the top-most (0–1.3 km) crust by a joint inversion of group and phase dispersions. Our results show that low-velocity zones beneath Y1 ore-hosting intrusion (hereafter called Y1) at 0–0.5 km depth and northwest of the Y2 ore-hosting intrusion (hereafter called Y2) at 0–0.6 km depth are consistent with highly mineralized areas. A relatively high-velocity zone is connected with a weakly mineralized area located to the southeast of Y2 and Y3 (hereafter called Y3) ore-hosting intrusions. Two high-velocity zones, distributed at 0.7–1.3 km depth in the northernmost and southernmost parts of the study area respectively, are interpreted to be igneous rocks related to early magma intrusion. Furthermore, the low-velocity zone at 0.7–1.3 km depth in the middle of the study area may be related to: a possible channel related to initial magma transport; mine strata or a potentially mineralized area. This study demonstrates a new application of dense-array ambient noise tomography to a mining area that may guide future studies of mineralized regions.

Original languageEnglish
Article number601
Pages (from-to)1-16
Number of pages16
Issue number7
Publication statusPublished - Jul 2020


Dive into the research topics of 'Imaging karatungk Cu-Ni mine in Xinjiang, Western China with a passive seismic array'. Together they form a unique fingerprint.

Cite this