Data-processing induced GPS-positioning error

Global Positioning System data processing is affected by many non-tectonic factors, including the common-mode errors （CME） in station-position time series. The characteristics and origins of CME are still not clear, due to uneven distribution of global GPS networks and the lack of reliable data of the position time series. In this work, data from 241 continuous GPS stations were reprocessed in a consistent way and the results were compared with those generated at Jet Propulsion Laboratory （JPL）. Improvements of residual positions were obtained for many low-quality stations, especially those located in Asia and Australia.

Impact assessment of the seasonal hydrological loading on geodetic movement and seismicity in Nepal Himalaya using GRACE and GNSS measurements

The Himalayan terrain is an epitome of ongoing convergence and geodetic deformation where both tectonic and non-tectonic forces prevail.In this study,the Gravity Recovery and Climate Experiment(GRACE)and Global Positioning System(GPS)datasets are used to assess the impact of seasonal loading on deformation with seismicity in Nepal.The recorded GPS data from 21 Global Navigation Satellite System(GNSS)stations during 2017-2020 are processed with respect to ITRF14 and the Indian reference frame,and the Center for Space Research(CSR)mascon RL06 during 2002-2020 is adopted to estimate the terrestrial water storage(TWS)change over the Ganga-Brahmaputra River basin.The results indicate that the hydrological loading effect or TWS change shows high negative,high positive,and moderately positive values in pre-monsoon,co-monsoon,and post-monsoon months,respectively.The detrended GPS data of both horizontal and vertical components correlate with the seasonal TWS change using the Pearson correlation coefficient at each GNSS site.In addition,the correlation coefficient has been interpolated using inverse distance weighting to investigate the regional TWS influence on geodetic displacement.In the north component,the correlation coefficient ranges from-0.6 to 0.6.At the same time,the TWS is positively correlated with geodetic displacement(0.82)in the east component,and the correlation coefficient is negative(-0.69)in the vertical component.The negative correlation signifies an inverse relationship between seasonal TWS variation and geodetic displacements.The strain rate is estimated,which shows higher negative values in pre-monsoon than in post-monsoon.Similarly,the effect of seismicity is 47.90%for pre-monsoon,15.97%for co-monsoon,and 17.56%for post-monsoon.Thus we can infer that the seismicity decreases with the increase of seasonal hydrological loading.Furthermore,the effect of strain is much higher in pre-monsoon than in post-monsoon since the impact of co-monsoon continues to persist on a small scale in the post-monsoon season.

Complex dynamics of repeating and river-blocking landslides in Jiangda during 2018

Two large landslides successively blocked the Jinsha River at the same location in Jiangda Village on October 10 and November 3,2018,respectively.The dynamic processes and possible interactions of the two landslides need to be studied to better understand the physical processes involved,and to provide information on future disaster mitigation.We investigated their force histories and sliding directions by inverting regional broadband seismograms.The scale of the October landslide was approximately three times that of the November event.The October event revealed a particularly strong deceleration force,which may have been caused by a collision between the sliding mass and ground surface.In contrast,the November event had a relatively weaker deceleration force,indicating that it may have been gradually stopped by the landslide dam formed during the October landslide.The sliding directions of the two landslides differed significantly in terms of both horizontal and vertical directions,indicating a change in their sliding surfaces.We conclude that unconsolidated materials at the top of the October landslide continued sliding along a curved slope during the November event.From our seismic models of dynamic processes,both the October landslide and local background may have affected and even changed a subsequent landslide’s mechanism.