基于自记忆模型的煤与瓦斯突出电磁辐射预测研究  

Research on Prediction of Coal and Gas Outburst by Means of EME Based on Self-memorization Model

作　　者：肖红飞[1,2] 彭斌[2] 

机构地区：[1]煤矿安全开采技术湖南省重点实验室,湘潭411201 [2]湖南科技大学能源与安全工程学院,湘潭411201

出　　处：《中国安全科学学报》2009年第10期88-94,共7页China Safety Science Journal

基　　金：国家自然科学基金资助(50604008);教育部留学回国人员科研启动基金;中国博士后基金;中南大学博士后基金

摘　　要：利用实验测定的电磁辐射信号时间序列,用双向差分原理反导出一个非线性常微分方程;以其为微分动力核,运用动力系统数据机理自记忆模式构造自记忆方程并求出自记忆系数;利用该方程预测未来电磁辐射信号的变化,并与现场测定对比分析,用误差分析和距平分析法验证该模型正确性和预测准确率。实例表明:该自记忆模型预测与实测结果是一致的,相对误差均在6.7852%左右,距平符合率为90%;自记忆方法能有效应用于煤与瓦斯突出电磁辐射动态预测中;该模型与电磁辐射预测方法的有机结合能有效地提高预测准确性,从而为煤与瓦斯突出电磁辐射预测技术提供了一种新的研究途径。On the basis of experimental research on prediction of coal or gas outburst by means of electromagnetic radiation （EMR） method which is a kind of non-contacting forecasting methods, according to self-memory theorem in dynamic process, the self-memorization model of coal and gas outburst＇ prediction model by means of electromagnetic emission （EME） method is researched. Firstly, by use of an observed time data series of in-situ EME signals, a nonlinear ordinary differential equation based on the bilateral difference principle is retrieved. Taking the above nonlinear ordinary differential equation as a dynamic kernel, with the self-memorization principle a forecast model can be established, which is called the Databased Mechanistic Self-memory Model （DAMSM）, and the self-memory efficiency is also given. Finally, the above forecasting equation is used to predict the EME data in the future, and the prediction fitting value is compared with the practical data. Some computing cases are given which show that the forecasting aceuraey of self-memorization model is satisfactory, and the organic combination of EME method and self- memorization model can predict the coal and gas outburst efficiently. In this work, a new research idea and method is provided for prediction of coal and gas outburst based on EME prediction method.

关 键 词：煤与瓦斯突出 自记忆模型 数据机理 电磁辐射(EME) 预测 

分 类 号：X936[环境科学与工程—安全科学] TD76[矿业工程—矿井通风与安全]