Slip rates and seismic moment deficits on main active faults in Tianshan constrained by GNSS network

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