In this paper, we construct a new 1′ $\times$ 1′ global seafloor topography model, BAT_VGG2021, using the satellite altimetric vertical gravity gradient anomaly model (VGG), SIO curv_30.1.nc, and ship soundings. Approximately 74.66 million single-beam depths and more than 180 GB of multibeam grids were downloaded and adopted from the National Centers for Environmental Information (NCEI), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), and Geosciences Australia (GA). The SIO curv_30.1.nc model was used to predict seafloor topography at 15~160 km wavelengths, and ship soundings were used to calibrate topography to VGG ratios. The accuracy of the new BAT_VGG2021 model was assessed by comparing it with ship soundings and existing models. The results indicate that the standard deviation of differences between the predicted model and ship soundings is about 40~80 m, and ~93% of the differences are within 100 m, similar to that of the SIO topo_20.1.nc model. The new BAT_VGG2021 model shows better accuracy than the DTU18BAT, ETOPO1, and GEBCO_08 models, and has been improved significantly from our last model, BAT_VGG2014.

Predicting future seismic trends and occurrence of earthquakes remains a long-standing challenge in seismology. Despite substantial efforts to unravel the physical mechanisms underlying earthquake occurrence, currently, no well-defined physical or statistical model is capable of reliably predicting major earthquakes. However, machine learning methods have demonstrated exceptional proficiency in identifying patterns within large-scale datasets, offering a promising avenue for enhancing earthquake prediction performance. Within the framework of machine learning, this study has developed a feature extraction method based on seismic prediction zoning, improving the effectiveness of machine learning-based earthquake prediction. The research findings indicate that the ensemble learning Stacking method, which is based on seismic prediction zoning, exhibits superior performance and high robustness in predicting the annual maximum earthquake magnitude. Additionally, the long short-term memory (LSTM) method demonstrates commendable performance within specific tectonic zones (e.g., the southwestern Yunnan region), providing valuable guidance for analyzing seismic trends in these regions.

Several commercial seismometers now support CSTP, the real-time communication protocol used in the China Earthquake Early Warning System, but there is still no simple, flexible, and low-cost solution to archive CSTP streams or integrate them into existing data processing systems. In this study, we design and implement SharpCEEWPServer, a lightweight, out-of-the-box graphical server that integrates client management, real-time data reception, visualization, and archiving, and can, via RingServer, convert CSTP real-time streams into widely supported SeedLink streams. Hardware compatibility is evaluated using four commercial CSTP-capable instruments, a forwarding chain is built to assess forwarding functionality and reliability, and concurrency performance is tested using simulated networks with different station counts. The stability under network impairment scenarios and the performance of the forwarding system were also analyzed. The results show that the server can reliably receive and forward real-time data streams, and that laptop-class hardware is sufficient to withstand the load imposed by an M7.0 earthquake scenario when receiving real-time streams from 1000 three-component seismometers. However, the current version’s latency performance can only meet the needs of non-early warning networks. Overall, the proposed server significantly lowers the deployment and usage threshold for new CSTP-capable instruments and provides an efficient, low-cost integration solution for temporary networks in earthquake emergency response and seismic arrays.