Slip rates and seismic moment deficits on main active faults in Tianshan constrained by GNSS network
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1. GNSS observations In this study, we collected GNSS velocity fields from Zheng et al. (2017) and Li et al. (2022), primarily sourced from the China Crustal Movement Observation Network (CMONOC-I/II) and a series of GNSS instruments deployed near the Kepingtage fold-and-thrust belt (FTB). Utilizing GNSS velocity field fusion method, we eliminated systematic errors caused by the reference frame differences and accidental errors between different velocity fields (Zhan et al. 2015). This process allowed us to obtain a comprehensive horizontal motion field for Tianshan region and its adjacent areas. The core of Zheng et al. (2017) velocity field data comes from the CMONOC project, which includes measurements from 2,576 GNSS observation stations recorded between 1991-2015. Li et al. (2022) GNSS network, consisting of 73 GNSS stations across the Kepingtage FTB, encompasses data from various GNSS instruments collected between 1998 and 2020. The newly incorporated GNSS data (Li et al. 2022) enhance the spatial resolution of crustal deformation measurements along the southern fold-and-thrust belts. Compared to previous datasets (Yang et al. 2008; Wang & Shen 2020a), these observations allow for improved constraint on localized shortening rates and more accurate division of blocks in the foreland thrust belt. 2. Multi-source GNSS velocity field fusion Since the existing GNSS observation data in Tianshan area originate from different studies, we need to create a unified velocity field under the same reference frame. This involves calculating the Euler vector of the rigid block rotation for the entire network based on independent common points in the two velocity fields. Typically, IGS stations with long-term observation records are selected as common points, with at least three required for accurate calculation. Using the least squares fitting algorithm, we minimize and normalize the common point fitting residuals after fitting the two sets of velocity fields. When common points are insufficient, “quasi-common points” can be selected, these are points that are not completely overlapped but are within 1 km of each other and on the same active block, assumed to have the same velocity vector in the same reference frame. This method allows us to integrate GNSS velocity field data from different sources, providing a reliable basis for studying the characteristics of crustal movement in Tianshan area. References Zheng, G., Wang, H., Wright, T.J., Lou, Y., Zhang, R., Zhang, W., Shi, C., et al., 2017. Crustal Deformation in the India-Eurasia Collision Zone From 25 Years of GPS Measurements. J. Geophys. Res. Solid Earth, 122, 9290–9312. doi:10.1002/2017JB014465 Li, J., Yao, Y., Li, R., Yusan, S., Li, G., Freymueller, J.T. & Wang, Q., 2022. Present-Day Strike-Slip Faulting and Thrusting of the Kepingtage Fold-and-Thrust Belt in Southern Tianshan: Constraints From GPS Observations. Geophys. Res. Lett., 49, 1–11. doi:10.1029/2022GL099105 Zhan, W., Wu, Y., Liang, H., Zhu, S., Zhang, F. & Liu, J., 2015. Characteristics of the seismogenic model for the 2015 Nepal Mw7.8 earthquake derived from GPS data. Chinese J. Geophys., 58, 1818–1826. doi:10.6038/cjg20150532 Yang, S.M., Li, J. & Wang, Q., 2008. The deformation pattern and fault rate in the Tianshan Mountains inferred from GPS observations. Sci. China, Ser. D Earth Sci., 51, 1064–1080. doi:10.1007/s11430-008-0090-8 Wang, M. & Shen, Z.K., 2020. Present-Day Crustal Deformation of Continental China Derived From GPS and Its Tectonic Implications. J. Geophys. Res. Solid Earth, 125. doi:10.1029/2019JB018774
1. 全球导航卫星系统（GNSS）观测数据 本研究收集了Zheng等人（2017）与Li等人（2022）的GNSS速度场数据，其主要来源为中国地壳运动观测网络（CMONOC-I/II）以及部署在柯坪塔格褶皱冲断带（FTB）附近的一系列GNSS观测仪器。本研究采用GNSS速度场融合方法，消除了不同速度场间因参考框架差异带来的系统误差与偶然误差（Zhan等，2015），最终得到天山地区及邻区完整的水平运动场。 Zheng等人（2017）的速度场数据核心源自CMONOC项目，涵盖了1991年至2015年间2576个GNSS观测站的观测数据。Li等人（2022）的GNSS观测网络覆盖柯坪塔格FTB区域内的73个GNSS站点，包含1998年至2020年间各类GNSS仪器采集的数据。本次新增的GNSS数据（Li等，2022）提升了南褶皱冲断带沿线地壳形变测量的空间分辨率。相较于此前的数据集（Yang等，2008；Wang & Shen，2020a），本研究的观测数据可更精准地约束局部缩短速率，并更准确地划分前陆冲断带内的地块。 2. 多源GNSS速度场融合 鉴于天山地区现有GNSS观测数据源自不同研究，本研究需构建统一参考框架下的标准化速度场。具体方法为基于两套速度场中的独立公共点，计算整个观测网络的刚性地块旋转欧拉矢量。通常选取具备长期观测记录的国际GNSS服务（IGS）站点作为公共点，至少需要3个公共点方可保证计算精度。本研究采用最小二乘拟合算法，对两套速度场进行拟合后，对公共点的拟合残差进行最小化与归一化处理。当公共点数量不足时，可选取准公共点：即点位并非完全重合，但间距小于1km且位于同一活动地块内的站点，假设其在统一参考框架下具备相同的速度矢量。该方法可实现多源GNSS速度场数据的整合，为研究天山地区地壳运动特征提供可靠依据。 参考文献 Zheng G, Wang H, Wright T J, Lou Y, Zhang R, Zhang W, Shi C, et al. 2017. 基于25年GPS观测的印-欧碰撞带地壳形变. 地球物理研究杂志·固体地球, 122: 9290–9312. doi:10.1002/2017JB014465 Li J, Yao Y, Li R, Yusan S, Li G, Freymueller J T, Wang Q. 2022. 南天山东段柯坪塔格褶皱冲断带现今走滑与逆冲活动：GPS观测约束. 地球物理研究快报, 49: 1–11. doi:10.1029/2022GL099105 Zhan W, Wu Y, Liang H, Zhu S, Zhang F, Liu J. 2015. 基于GPS数据的2015年尼泊尔Mw7.8地震孕震模型特征. 地球物理学报, 58: 1818–1826. doi:10.6038/cjg20150532 Yang S M, Li J, Wang Q. 2008. 基于GPS观测的天山地区形变格局与断层速率. 中国科学D辑：地球科学, 51: 1064–1080. doi:10.1007/s11430-008-0090-8 Wang M, Shen Z K. 2020. 基于GPS观测的中国大陆现今地壳形变及其构造意义. 地球物理研究杂志·固体地球, 125. doi:10.1029/2019JB018774
