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您的检索式：作者名="Lianglei Guo"
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| 1 | High-resolution crustal velocity imaging using ambient noise recordings from a high-density seismic array:An example from the Shangrao section of the Xinjiang basin,China显示文摘A profile of shallow crustal velocity structure(1–2 km) may greatly enhance interpretation of the sedimentary environment and shallow tectonic deformation.Recent advances in surface wave tomography, using ambient noise data recorded with high-density seismic arrays, have improved the understanding of regional crustal structure. As the interest in detailed shallow crustal structure imaging has increased, dense seismic array methods have become increasingly efficient. This study used a high-density seismic array deployed in the Xinjiang basin in southeastern China, to record seismic data, which was then processed with the ambient noise tomography method. The high-density seismic array contained 203 short-period seismometers, spaced at short intervals(~ 400 m). The array collected continuous records of ambient noise for 32 days. Data preprocessing,cross correlation calculation, and Rayleigh surface wave phase-velocity dispersion curve extraction, yielded more than 16,000 Rayleigh surface wave phase-velocity dispersion curves, which were then analyzed using the direct-inversion method. Checkerboard tests indicate that the shear wave velocity is recovered in the study area, at depths of 0–1.4 km,with a lateral image resolution of ~ 400 m. Model test results show that the seismic array effectively images a 50 m thick slab at a depth of 0–300 m, a 150 m thick anomalous body at a depth of 300–600 m, and a 400 m thick anomalous body at a depth of 0.6–1.4 km. The shear wave velocity profile reveals features very similar to those detected by a deep seismic reflection profile across the study area. This demonstrates that analysis of shallow crustal velocity structure provides high-resolution imaging of crustal features.Thus, ambient noise tomography with a high-density seismic array may play an important role in imaging shallow crustal structure. | Gaochun Wang Xiaobo Tian Lianglei Guo Jiayong Yan Qingtian Lyu | 2018 | Earthquake Science2018，31，5： | 9 |
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