EPP

There are many active faults in the southeast margin of Tibetan Plateau, where three large active faults zones, the Longmenshan, Xianshuihe and Anninghe, merge to form a "Y" shape. Strong crustal deformation and a complicated fault distribution accompany strong earthquake activity in this zone. In this paper, we investigate a multi-scale gravity anomaly in the southeastern margin of the Tibetan Plateau using the wavelet transform; we find that the pattern of the gravity field is closely related to the fault system in the study area. Analyzing the characteristics of this Bouguer gravity anomaly at different orders indicates that the eastern Himalayan syntaxis has produced a strong eastward push during its northward movement, resulting in a shortening of the crust from west to east and a rapid uplift of the Tibetan Plateau. The Songpan–Garzê and Sichuan–Yunnan blocks have been forced to slip and extrude southward and eastward laterally. The distributions of seven large earthquakes from 1970 to 2018 reflects the relationship between large earthquakes and characteristics of the gravity anomaly. Comparing the tectonic backgrounds of several earthquakes reveals that the large earthquakes occur usually in the high gravity anomaly gradient zone, which corresponds in general to the boundary zones of the blocks. We infer that large earthquakes occur primarily in high Bouguer gravity anomaly zones in the upper crust, while low Bouguer gravity anomalies encompass the lower crust and the uppermost mantle.

One of the most important dynamic processes in the middle and upper atmosphere, gravity waves (GWs) play a key role in determining global atmospheric circulation. Gravity wave potential energy (GW E_p) is an important parameter that characterizes GW intensity, so it is critical to understand its global distribution. In this paper, a deep learning algorithm (DeepLab V3+) is used to estimate the stratospheric GW E_p. The deep learning model inputs are ERA5 reanalysis datasets and GMTED2010 terrain data. GW E_p averaged over 20−30 km from 60°S−60°N, calculated by COSMIC radio occultation (RO) data, is used as the measured value corresponding to the model output. The results show that (1) this method can effectively estimate the zonal trend of GW E_p. However, the errors between the estimated and measured value of E_p are larger in low-latitude regions than in mid-latitude regions, possibly due to the large number of convolution operations used in the deep learning model. Additionally, the measured E_p has errors associated with interpolation to the grid; this tends to be amplified in low-latitude regions because the GW E_p is larger and the RO data are relatively sparse, affecting the training accuracy. (2) The estimated E_p shows seasonal variations, which are stronger in the winter hemisphere and weaker in the summer hemisphere. (3) The effect of quasi-biennial oscillation (QBO) can be clearly observed in the monthly variation of estimated GW E_p, and its QBO amplitude may be less than that of the measured E_p.