Abstract
The development and distribution of geologically complicated fault structure have the characteristics of uncertainty, randomness, ambiguity, and variability. Therefore, the prediction of complicated fault structures is a typical nonlinear problem. Neither fuzzy logic method nor artificial neural network alone can solve this problem well because the fuzzy method is generally not easy to realize adaptive learning function, and the neural network method is not suitable for describing sedimentary microfacies or geophysical facies. Therefore, taking the marginal subsags in the Jiyang Depression, Eastern China, as a study case, this paper uses the method of combining artificial neural network and fuzzy logic to study geologically complicated fault structure prediction model. This paper expounds on the research status and significance of geologically complicated fault structure prediction model, elaborates the development background, current status, and future challenges of artificial neural networks and fuzzy logic, introduces the method and principle of fuzzy neural network structure and fuzzy logic analysis algorithm, conducts prediction model design and implementation based on fuzzy neural network, proposes the learning algorithm of fuzzy neural network, analyzes the programming realization of fuzzy neural network, constructs complicated fault structure prediction model based on the artificial neural network and fuzzy logic, performs the fuzzy logic system selection of complicated fault structure prediction model, carries out the artificial neural network structure design of complicated fault structure prediction model, compares the prediction effects of the geologically complicated fault structure model based on artificial neural networks and fuzzy logic, and finally discusses the system design and optimization of the prediction model for geologically complicated fault structures. The study results show that the fuzzy neural network fully integrates the advantages of artificial neural network and fuzzy logic system; based on the clear physical background of fuzzy logic system, it effectively integrates powerful knowledge expression ability and fuzzy reasoning ability into the network knowledge structure of neural network, which greatly improves the prediction accuracy of geologically complicated fault structure.
Highlights
Fuzzy Neural Network StructureAfter inputting the learning samples, one of the main results of fuzzy neural network learning is that the geologically complicated fault structure prediction model summarizes the membership functions of each input variable from the training samples
Introduction e development and distribution of geologically complicated fault structure have the characteristics of uncertainty, randomness, ambiguity, and variability. erefore, the prediction of complicated fracture structure is a typical nonlinear problem. ere is a highly nonlinear and complicated relationship between it and its influencing factors, which is difficult to describe with simple mechanics and mathematical models. is ambiguity and complexity can cause differences in understanding of the nature of complicated fault structures and bring great difficulties to engineering problems, such as oil and gas geological exploration and slope stability analysis [1]
Fuzzy neural network composes various factors that affect the evaluation of the complexity of the fault structure into a common set; for the quantitative evaluation of the fault structure, the value of the fault fractal dimension is one of the important indicators. e essence of the fuzzy artificial neural network based on the information diffusion method is to transform the contradictory samples into noncontradictory samples by spreading the information of the factors
Summary
After inputting the learning samples, one of the main results of fuzzy neural network learning is that the geologically complicated fault structure prediction model summarizes the membership functions of each input variable from the training samples. According to the aforementioned network structure and predicted input and output variables for complicated fault structures, as well as the number of fuzzy divisions of each input component, the parameters that the grid needs to learn are mainly the functional network connection weight f and center value g and width h of the membership function of each node in feature grid. E prediction model of complicated fault structure is first based on the method of fuzzy inference, and a fuzzy logic rule model is preliminarily determined according to the curve or data recorded in the experiment of the production control system [9]. Because the output of the artificial neural network is the result of the second-level fuzzy approximation of the predictive quantity rather than a single predictive quantity, the possibility of contradictory samples is greatly reduced
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