Development of an automatic tracking model for seismic stratigraphic correlation based on information entropy theory

Abstract

Accurate stratigraphic information is the basis of seismic data interpretation and reserve prediction. Based on this, this paper firstly establishes a grey model based on information entropy, uses particle swarm optimization technology of neural network to analyze and track the influence of each factor on the degree of seismic hazards, and gets the weight of each factor on the degree of occurrence of geologic hazards through the model by using the matlab software, and secondly, uses the theory of information entropy to determine the evaluation system and its grading standard, and finally evaluates the comprehensive evaluation of fuzzy through the grey clustering model. bp neural network. The development and research of automatic tracking model of seismic stratigraphic correlation based on information entropy theory can greatly improve the efficiency and accuracy of seismic data interpretation, which is of practical value and significance for seismic data interpretation.