Insights on the Italian Seismic Network from location uncertainties

Probabilistic earthquake locations provide confidence intervals for the hypocentre solutions such as errors encountered in the position, the origin time, and in magnitude. If the relationship of the parameters relative to the local arrangement of the seismic network is considered, such as the node distance, the number of stations, the seismic gap, and the quality of phase readings), the uncertainties can then provide insights on the location capability of the network. In this paper, we collect the earthquake data recorded from the Italian Seismic Network for a time span of 5 years. The data pertain to three different catalogues according to the progressive refinement phases of the location procedure: automatic location, revised location, and published location. By means of spatial analysis, we assess the distribution of the location-related and network-related estimators across the study area. These estimators are subsequently combined to assess the existence of spatial correlations at a local scale. The results indicate that the Italian network is generally able to provide robust locations at the national scale and for smaller earthquakes, and the elongated shape of Italy (and of its network) does not cause systematic bias in the locations. However, we highlight the existence of subregions in which the performance of the network is weaker. At present, a unique 2D, 3-layer velocity model is used for the earthquake location procedure, and this could represent the main limitation for the improvement of the locations. Therefore, the assessment of locally optimized velocity models is the priority for the homogenization and the improvement of the Italian Seismic Network performance.

Redundant Data High Efficiency Compression Based on Distributed Parallel Algorithm

In order to improve the optimal storage capacity of redundant data in serial hybrid network cascade database, a high efficiency compression algorithm for redundant data in serial hybrid network cascade database based on distributed parallel algorithm is proposed. The distributed storage structure model of redundant data of serial mixed network cascade database is constructed, the association feature extraction of redundant data of serial mixed network cascade database is carried out by using distributed hybrid feature mining method, the dimension reduction of redundant data of serial mixed network cascade database is carried out by combining with feature transformation method, the automatic location allocation of redundant data of serial mixed network cascade database is carried out by using high-order spectrum decomposition method, and the high-efficiency energy compression output model of redundant data of serial mixed network cascade database is constructed. The simulation results show that this method has good losslessness for redundant data compression of serial mixed network cascaded database and good fidelity of data output.

The optimization of automated goods dynamic allocation and warehousing model

In the development of modern logistics, the role of automated cargo warehousing is gradually reflected, which is essential for the automatic distribution of goods. This paper briefly introduced the automatic location allocation model and the particle swarm optimization (PSO) algorithm used to optimize the model. At the same time, it introduced the concept of genetic operator and multi-group co-evolution to improve the algorithm, and then the simulation analysis of standard PSO and improved PSO was performed on MATLAB software. The results showed that the improved PSO iterated fewer times and get better solution sets; compared with the manual allocation scheme, the improved PSO calculation reduced more warehousing time, lowered more center of gravity height, and improved shelf stability. In summary, the improved PSO algorithm can effectively optimize the automated goods dynamic allocation and warehousing model.