Model-data-driven seismic inversion method based on small sample data

As sandstone layers in thin interbedded section are difficult to identify,conventional model-driven seismic inversion and data-driven seismic prediction methods have low precision in predicting them.To solve this problem,a model-data-driven seismic AVO(amplitude variation with offset)inversion method based on a space-variant objective function has been worked out.In this method,zero delay cross-correlation function and F norm are used to establish objective function.Based on inverse distance weighting theory,change of the objective function is controlled according to the location of the target CDP(common depth point),to change the constraint weights of training samples,initial low-frequency models,and seismic data on the inversion.Hence,the proposed method can get high resolution and high-accuracy velocity and density from inversion of small sample data,and is suitable for identifying thin interbedded sand bodies.Tests with thin interbedded geological models show that the proposed method has high inversion accuracy and resolution for small sample data,and can identify sandstone and mudstone layers of about one-30th of the dominant wavelength thick.Tests on the field data of Lishui sag show that the inversion results of the proposed method have small relative error with well-log data,and can identify thin interbedded sandstone layers of about one-15th of the dominant wavelength thick with small sample data.

A New Economy Forecasting Method Based on Data Barycentre Forecasting Method

A new and useful method of technology economics, parameter estimation method, was presented in light of the stability of gravity center of object in this paper. This method could deal with the fitting and forecasting of economy volume and could greatly decrease the errors of the fitting and forecasting results. Moreover, the strict hypothetical conditions in least squares method were not necessary in the method presented in this paper, which overcame the shortcomings of least squares method and expanded the application of data barycentre method. Application to the steel consumption volume forecasting was presented in this paper. It was shown that the result of fitting and forecasting was satisfactory. From the comparison between data barycentre forecasting method and least squares method, we could conclude that the fitting and forecasting results using data barycentre method were more stable than those of using least squares regression forecasting method, and the computation of data barycentre forecasting method was simpler than that of least squares method. As a result, the data barycentre method was convenient to use in technical economy.

Structural Reliability Analysis Based on Support Vector Machine and Dual Neural Network Direct Integration Method

Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.

Bayesian Method Reliability of Flight Simulator

This paper introduces the basic viewpoints and characteristics of Bayesian statistics. Which provides a theoretical basis for solving the problem of small sample of flight simulator using Bayesian method. A series of formulas were derived to establish the Bayesian reliability modeling and evaluation model for flight simulation equipment. The two key problems of Bayesian method were pointed out as follows： obtaining the prior distribution of WeibuU parameter, calculating the parameter a posterior distribution and parameter estimation without analytic solution, and proposing the corresponding solution scheme.

A comprehensive performance evaluation framework of complex products based on a fuzzy AHP and DS theory

With the development of computer technique,performance evaluation of complex products is playing an increasingly critical role in ensuring product quality and improving development process.An extensible comprehensive performance evaluation framework with the integration of effective group decision-making algorithms could be a supporting tool to achieve an efficient evaluation process and reduce comprehensive evaluation dif-ficulty.This paper aims to provide a evaluation framework with friendly interactive operation and extensive expansibility,which adopts a multi-expert evaluation approach based on fuzzy,analytical hierarchy process(FAHP)and Dempstere–Shafer(DS)theory(FADS)in order to consider experts’relative importance degree.In addition,an extensible evaluation process and related auxiliary functions are implemented in the framework,including the establishment of an assessment index system,integration and calls of multiple types of testing data preprocessing methods and index assessment methods suitable for small sample data,graphical result display and data analysis,etc.Finally,performance evaluation cases of two models of airborne radar anti-jamming are presented to verify the feasibility and expansibility of our assessment framework.The group decision-making method shows its effectiveness compared with the experimental evaluation results by the FAHP researched method.