Prediction And Analysis Of Key Areas Of Earthquake Based On Neural Network

Earthquakes are catastrophic natural disasters for human society.China is located at the junction of the Asia-Europe plate,the Pacific plate and the Indian plate.It has been subjected to collision and compression between the plates for a long time.Therefore,the number of seismic fault zones in China is high and the activity is high.It is one of the countries most affected by the earthquake in the world.Many large earthquakes in history have caused tremendous social damage and casualties to our country.Therefore,the research and solution of earthquake prediction problems are extremely important for the stability and stability of human society.This article analyzes the characteristics and problems of earthquake big data,and fully considers the high nonlinearity of earthquake breeding and production.At the same time,inspired by the rapid development of artificial intelligence,this article attempts to apply NEAT algorithm for the first time in the field of earthquake prediction,and proposes a NEAT-based algorithm.The short-term earthquake prediction model is compared with the prediction model constructed by BP neural network,which is used in the field of earthquake prediction,and gives corresponding reference opinions.It has breakthroughs in prediction ideas and prediction accuracy,thus Earthquake prediction researchers provide certain ideas and methodological references to facilitate further analysis and prediction.The main research work and results of this article are as follows:(1)Classification of seismic data based on fault zones.Because the activity of the fault zone is closely related to the occurrence and generation of earthquakes,this article introduces the impact of the fault zone on the earthquake into the predictive analysis,and uses the influence range of the fault zone to classify the seismic data,taking into account the different fault zones.Because the different geological structures have different effects on earthquakes,the specific prediction network is customized for different fault zones,so that the effect is better.(2)Dimensionality reduction of seismic parameters based on factor analysis.Seismic activity parameters are a set of parameters that can characterize the degree of seismic hazard.The nature of earthquake prediction is actually the prediction of the degree of seismic hazard.Therefore,this article chooses seismic activity parameters as the main research object of earthquake prediction model,and in order to solve seismic activity.The meaning of the intersection between the sexual parameters,the text using factor analysis method to extract the common factors of seismic activity parameters,to avoid the influence of multi-collinearity on the accuracy of the model.(3)Design and implementation of earthquake short-term prediction model based on BP neural network.Because of its powerful self-learning ability,BP neural network is very good at dealing with complex nonlinear problems and is widely used in the field of earthquake prediction.This article firstly designs and constructs a short-term earthquake prediction model using a BP neural network with a network structure of 6*15*1.(4)Design and implementation of earthquake short-term prediction model based on NEAT algorithm.In recent years,artificial intelligence technology has developed rapidly,and NEAT algorithm has been widely used in this field.Inspired by this,this article attempts to introduce the NEAT algorithm into the field of earthquake prediction and construct the earthquake short-term prediction model.Firstly,the neural network is initialized to the minimum network structure with only the input layer connected to the output layer.The NEAT algorithm is used to gradually evolve the best performing neural network through selection,crossover and mutation.This genetic process can not only update the network parameters,but also update the network parameters.At the same time,the network topology structure is continuously evolved,and the optimal solution is globally searched.The short-term earthquake prediction model based on NEAT algorithm is successfully constructed.Finally,the prediction results based on NEAT algorithm are compared with those based on BP neural network.It is found that the prediction accuracy of the model based on NEAT algorithm is 40% higher than that based on BP neural network,and the average magnitude error is reduced by 0.7.Therefore,NEAT algorithm and its short-term earthquake prediction model are also applicable to the field of earthquake prediction,and have certain reference and application value for the research and solution of earthquake prediction problems.