矿井涌水量混沌特征与预测

杨永国; 陈玉华

矿井涌水量混沌特征与预测

中国矿业大学资源与地球科学学院，江苏徐州 221116

基金项目:

国家自然科学基金项目 40730422

国家自然科学基金项目 40472146

地质过程与矿产资源国家重点实验室开放基金 GPMR2007-11

中国矿业大学科技基金 OF061003

详细信息

作者简介:
杨永国（1962—），男，博士，教授，博士生导师，主要从事数学地质、GIS应用研究.E-mail：ygyang88@cumt.edu.cn

中图分类号: TD742
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出版历程
- 收稿日期: 2008-12-10
- 刊出日期: 2009-03-25

Chaotic Characteristics and Prediction for Water Inrush in Mine

School of Resources and Earth Sciences, China University of Mining and Technology, Xuzhou 221116, China

摘要

摘要:
由于受到水文地质、矿井开采等因素的影响, 矿井地下水系统的演化不仅具有确定性也具有随机性, 采用单一的确定性方法或随机性方法都难以揭示矿井地下水系统演化的两面性.混沌理论将确定性分析方法和随机性分析方法两者实现了统一.矿井涌水量时间序列是地下水系统中各要素相互作用的结果, 它包含着该动力系统的信息.基于刘桥二矿水文地质背景分析, 采用混沌时间序列分析方法对该矿的矿井涌水量时间序列进行建模、分析, 得出了其Lyapunov指数为0.1427, 表明刘桥二矿涌水量具有混沌特征.利用建立的模型, 选择2004年4月至2005年2月间的矿井涌水量时间序列进行验证, 结果表明, 利用混沌时间序列分析方法预测矿井涌水量是可行的且具有较高的精度.
- 刘桥二矿 /
- 混沌时间序列 /
- 矿井涌水量 /
- 预测
Abstract:
It is difficult to discover the certainty and randomness of the law of the evolution of the underground water system by only using deterministic method or stochastic method, because the evolution of the system is not only deterministic but also stochastic, due to the effect of such factors as hydrologic geology and mine exploitation.Chaos theory combines both deterministic analysis method and stochastic analysis method.The time series of water inrush in mine are the results of the interaction between the factors of the underground water system in mine and contain the information of this dynamical system.Based on the background analysis of hydrologic geology of the second coal mine in Liuqiao, we obtained the Lyapunov index (0.1427) of the time series of water inrush by modeling and analyzing of the chaotic time series.The Lyapunov index shows that the water inrush of the second coal mine in Liuqiao is of chaotic characteristics.By using the established model, the time series of water inrush in mine from April 2004 to February 2005 was verified, and the results indicate that the chaotic time series analysis method is feasible and highly effective in predicting the water inrush in mine.
- the second coal mine in Liuqiao /
- chaotic time series /
- water inrush in mine /
- prediction

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图 1 矿井涌水量变化曲线

Fig. 1. Curve of water inrush in mine

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图 2 矿井涌水量的自相关函数

Fig. 2. Autocorrelation function of water inrush in mine

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图 3 基于Cao方法计算的关联维数

Fig. 3. Correlation dimension based on Cao method

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表 1 矿井涌水量实测值和预测值比较

Table 1. Comparison of observed data with forecasted results

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