It is significant to determine the refined Moho topography for understanding the tectonic structure of the crust and upper mantle. A novel method to invert the Moho topography from the on-orbit gravity gradients is proposed in the present study. The Moho topography of Tibet is estimated by our method, which is verified by previous studies. The research results show that: (1) the deepest Moho of Tibet, approximately 70 km, is located at the western Kunlun area, where it corresponds well to that of previous publications; (2) clear Moho folds can be observed from the inverted Moho topography, whose direction presents a clockwise pattern and is in good agreement with that of Global Positioning System; (3) compared with the CRUST 1.0, our inverted Moho model has a better spatial resolution and reveals more details for tectonic structure; (4) the poor density model of the crust in Tibet may be the main reason for the differences between the obtained gravity Moho model and seismic Moho model; (5) by comparing our inverted Moho with those from previous publications, our method is correct and effective. This work provides a new method for the study of Moho topography and the interior structure of the Earth.

No abstract

A new satellite-only gravity field model entitled HUST-GOGRA2018s is developed by the combination of GRACE and GOCE data in this study. The modified dynamic approach is applied for GRACE data processing, while the space-wise least square method with a cascade filter is utilized for GOCE data processing. The GRACE-only model HUST-Grace2016s and GOCE-only model HUST-GOCE2018s are then computed, respectively. Our new developed GRACE-only model HUST-Grace2016s performs better than AIUB-GRACE03S, GGM05S, Tongji-GRACE01S at higher degrees, and the quality of our GOCE-only model HUST-GOCE2018s is also better than that of GO_CONS_GCF_2_TIM_R2 and GO_CONS_GCF_2_SPW_ R2. The combination is subsequently implemented by the superposition of GRACE and GOCE full normal equations. During the combination, the optimal weight is determined by the least-squares combined adjustment method with parametric covariance approach (LS-PCA) and the spectral combination method, respectively. The comparison result demonstrates that LS-PCA is more proper for the combination. As a result, the final HUST-GOGRA2018s model is developed. Validated by external gravity field models, the results demonstrate that the HUST-GOGRA2018s is dominated by GRACE data for the spherical harmonic coefficients lower than degree 60 and GOCE data for the spherical harmonic coefficients higher than degree 150, and its performance is better than that of GOCO01S.

The lockdowns imposed worldwide to curb the coronavirus diseases (COVID-19) spread has positive effects on the environment. However, it is unclear what fraction is caused by weather and what is related to lockdown. Here we used Global Navigation Satellite System (GNSS) height anomaly time series to quantify the spatiotemporal characteristics of lockdown-induced noise anomalies at 231 selected sites over mainland China. The results indicated that apparent declines (0.52 mm) in noise in 6 days (24-29 January 2020) after the lockdown in Wuhan resulted in drastically reduced human activities, which accounts for 71% of the total noise decrease. The lockdown effects persisted for 8 weeks and reached the maximum in the third week (6-12 February 2020) with reduced GNSS noise anomalies occurring at 81% of the GNSS sites. With the control of pandemic, increased noise anomalies occurred at more than 60% of the sites during the 9th week, which correlated well with the easing of lockdown in many cities in China. We concluded that this study provides new insights to quantifying the effects of human activities on geodetic measurements during the COVID-19 lockdown.