Three-dimensional S-wave velocity structure of the upper crust in the Guangdong-Hong Kong-Macao Greater Bay Area:insights into the basins structure and genesis of hot springs

As one of the four largest bay areas with strong economic activities in the world,the Guangdong-Hong Kong-Macao Greater Bay Area(GHMGBA)is located in the zone of interaction between the South China Block(SCB)and the South China Sea(SCS).Under the influence of complex geologic evolution,basin-range structures,fault systems and hot springs are well developed here.However,the characteristics of geological structures and the genetic mechanism of these geological phenomena are still unclear.Therefore,we performed ambient noise tomography to obtain 3-D upper crust(0-7.5 km)S-wave velocity structures of the GHMGBA by using 40-day continuous waveform data from 130 seismic stations in the GHMGBA.Our results show that sedimentary basins in the GHMGBA are mainly characterized by low-velocity anomalies.S-wave velocities of sediment formation in basins are about 2.8-3.1 km/s.Rapid changes in velocity appear at the edges of the basins,which correspond to the NE-,NEE-,and NW-trending faults,indicating prominent basin-controlling effects of the faults.The Sanshui Basin(SSB),the largest in the GHMGBA,has a developmental depth of about 4 km,and there is a significant difference in velocity gradient between the east and west sides of the basin,indicating that SSB has experienced east-west asymmetric expansion.Moreover,there are prominent low-velocity anomalies at a depth of about 4.5 km beneath the hot springs at the west of the Zhujiang(Pearl)River estuary(ZRE).We infer that the low-velocity anomalies are fluid reservoirs of the hot springs,which lead to the development of the hot springs on the surface.In addition,the distribution of main cities in the GHMGBA shows a spatial correlation with low-velocity areas at shallow depths(<3 km).The population development trend in the GHMGBA in the past 20 years is also mainly concentrated in the structural province of relatively low-velocity.In combination with the GHMGBA basin structures and drainage distribution characteristics,we suggest that the basic geological environment to some extent affects the habitability of the human settlement and thus determines the distribution and development trend of the main urban context.We believe that the 3-D S-wave velocity structure of the upper crust of the GHMGBA obtained in this study,as well as the deep structural characteristics of the basins and hot springs,will provide support to urban construction planning and geological hazards research of the GHMGBA.

Distributed cooperative task planning algorithm for multiple satellites in delayed communication environment

Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delays, which is unable to ensure the integrity and timeliness of the information on decision making for satellites. And the optimization of the planning result is affected. Therefore, the effect of communication delay is considered during the multi-satel ite coordinating process. For this problem, firstly, a distributed cooperative optimization problem for multiple satellites in the delayed communication environment is formulized. Secondly, based on both the analysis of the temporal sequence of tasks in a single satellite and the dynamically decoupled characteristics of the multi-satellite system, the environment information of multi-satellite distributed cooperative optimization is constructed on the basis of the directed acyclic graph（DAG）. Then, both a cooperative optimization decision making framework and a model are built according to the decentralized partial observable Markov decision process（DEC-POMDP）. After that, a satellite coordinating strategy aimed at different conditions of communication delay is mainly analyzed, and a unified processing strategy on communication delay is designed. An approximate cooperative optimization algorithm based on simulated annealing is proposed. Finally, the effectiveness and robustness of the method presented in this paper are verified via the simulation.