Research On Analysis And Visualization Of Seismic Data

Nowadays,the global economy is developing rapidly,and the prevention and response capabilities of natural disasters are also increasing.However,in terms of predicting earthquakes,there is difficult for us to master their activity mechanism.How to effectively monitor and predict earthquakes is a top priority in dealing with natural disasters around the world.Based on the domestic and foreign literatures,this paper uses computer technology and neural network to improve seismic factor indicators,which is of great significance for earthquake prediction and early warning work.The following are main tasks :1.The Gutenberg-Richter law is verified by analyzing the magnitude-frequency relationship through the statistics of the earthquake catalogue.All the magnitudes in the earthquake catalogue are distributed on the actual latitude and longitude coordinates,and six high-frequency seismic zones on the seismic belt are summarized and analyzed.2.The modeling of seismic factors such as b-value is obtained based on the least squares method.In view of the difficulty of fast convergence of neural networks,Principal Components Analysis(PCA)is used to analyze multi-dimensional seismic factor weights to reduce the number of inputs to the neural network without losing the major information of the original data.3.Propose the Particle Swarm Optimization Recursive Neural Nerwork network(PSO-RNN)method to realize predicting earthquake.Experimental results and conclusions: First,the comparison of various evaluation indicators shows that the PSO-RNN prediction model is better.Second,compare the R values under different magnitudes and find the best value in the prediction model.Third,using the same prediction model to study the prediction performance of different regions,the smaller the earthquake threshold magnitude is,the better the prediction performance of the region in the PSO-RNN network.Fourth,the prediction and actual position deviation analysis.4.Realize the seismic factor visualization analysis system.The earthquake is effectively alerted by the four functional modules of the system.In this paper,through the seismic factor modeling and training network prediction model,the earthquake prediction indicators improved,and on the basis of theory,a visual analysis system is established to realize real-time monitoring of seismic factors and achieve the purpose of earthquake early warning.