冲击地压电磁辐射前兆信息的时间序列数据挖掘及群体识别体系研究
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摘要
电磁辐射技术已经在国内多个煤矿用于冲击地压灾害的预测。如何有效地识别冲击地压电磁辐射监测数据中的前兆信息,研究冲击地压电磁辐射前兆特征,实现冲击地压准确预报,是电磁辐射预测技术今后的发展方向。
本文实验室研究了冲击煤岩的电磁辐射特征,现场测试和分析了工作面应力变化和不同构造的电磁辐射响应规律;运用时间序列数据挖掘方法,对冲击地压电磁辐射监测数据进行处理和挖掘,对冲击地压电磁辐射前兆信息进行了定量识别,建立了冲击地压电磁辐射前兆信息群体识别体系。
(1)实验研究了冲击煤岩在全应力应变峰前、峰后阶段以及煤岩摩擦的电磁辐射特征。煤岩在全应力应变峰前阶段的电磁辐射信号随应力增加而增强,在峰后阶段随应力降低呈先上升后下降的趋势;煤岩摩擦面切应力的增加和加载速率的增加,使得电磁辐射信号增强。
(2)现场测试和分析了工作面应力变化和不同构造的电磁辐射响应规律。电磁辐射的测试结果能够反映工作面应力分布及变化的规律;断层和褶曲等地质构造周围的电磁辐射信号有异常反映。
(3)运用传统时间序列分析方法对冲击地压电磁辐射前兆数据进行了分析,并采用变量R聚类的数据挖掘方法,度量了冲击地压电磁辐射前兆子序列的相似性。研究表明,有冲击危险的电磁辐射数据是一个非白噪声平稳序列,蕴含有可提取的前兆信息,可以用ARMA时间序列模型来拟合及预测;依据相似性度量结果能够对监测范围进行危险性区域划分,确定冲击地压的重点防治区域。
(4)建立了以均值和方差为参数的电磁辐射强度异常判别及预警准则,提出了个体和群体电磁辐射异常的判别指标,对冲击地压电磁辐射前兆信息进行了定量识别。结果表明,电磁辐射强度异常判别及预警准则能够评价电磁辐射异常的危险程度,个体和群体电磁辐射异常判别指标能够对前兆信息进行有效识别。
(5)基于冲击地压电磁辐射前兆信息的定量识别,建立了冲击地压电磁辐射前兆群体识别体系,并进行了验证。结果表明,该体系的三个识别层次(包括:个体异常识别、子群体异常识别和群体异常识别)能够对冲击地压电磁辐射前兆信息进行有效的提取和识别。
本文的创新性主要体现在冲击地压电磁辐射前兆的时间序列数据挖掘和前兆信息群体识别研究两个方面。
本文对冲击地压电磁辐射前兆信息的识别进行了比较系统的研究,对冲击地压的预测具有重要的理论和实用价值。
Electromagnetic radiation technology has been used to forecast rock burst in some coalmines in our country. It is the development trend of forecasting rock burst by EMR technology in the future that how to recognize the precursor information of EMR monitoring data for rock burst effectively, study the EMR precursor characteristics of rock burst, and forecast rock burst accurately.
In the paper, we have studied the EMR characteristics of coal or rock samples in the experiments, with field tests analyzed the EMR responding laws of different tectonics and the stress change on the working face, mined and analyzed the EMR monitoring data of rock burst by time series data mining (TSDA) technology, recognized the EMR precursor information of rock burst quantitatively, and established the group recognition system of the EMR precursor information of rock burst.
EMR characteristics of coal or rock samples during the pre-peak phase and the post-peak phase of complete stress-strain experiments and in the friction experiments were studied. EMR signals which are produced by the deformation of coal or rock samples increase with the increase of stress during the pre-peak phase of the complete strain-stress process, and firstly increase and then decrease with the reduction of stress during the post-peak phase. In the friction experiments, the intensity of EMR signals are made to be enhanced with the increase of shear stress between the friction surfaces and the increase of loading rate .
EMR responding laws of different tectonics and stress change on the working face are tested in the field and analyzed. EMR testing results can reflect the laws of the stress distribution and change on the working face. EMR signals produced by coal or rock around the tectonics such as faults and folds are usually abnormal.
EMR precursor data of rock burst is analyzed by the traditional time series analysis method, and the similarity of EMR precursor sequence is measured by the data mining method of variable cluster. Research results show that EMR data of rock burst is a non-white noise and stationary sequence in which there is the precursor information to be extracted. ARMA model can be used to fit and forecast this sequence. According to the similarity measurement, the monitoring region of rock burst is divided into several areas with different risk, and the area with more risk is confirmed as the important area for predicting and treating rock burst.
We have established the discrimination and warning criterion of EMR intensity abnormity including two statistical parameters of mean and variance, put forward the discrimination indexes of EMR individual and group abnormity, and recognized the EMR precursor information of rock burst quantitatively. The results show that this discrimination and warning criterion can estimate the dangerous degree of EMR abnormity, and these discrimination indexes can recognize the EMR precursor effectively.
Based on the quantitative recognition of the EMR precursor information of rock burst, the group recognition system of EMR precursor is established and demonstrated. The demonstration results show that the three recognition levels of this system, including individual abnormity recognition, sub-group abnormity recognition and group abnormity, all can extract and recognize the EMR precursor information of rock burst effectively.
The innovations of the paper are the time series data mining of EMR precursor of rock burst, and the group recognition of the precursor information.
In the paper, the recognition of EMR precursor information of rock burst has been studied systematically, having an important theoretical and practical significance of forecasting rock burst.
本文实验室研究了冲击煤岩的电磁辐射特征,现场测试和分析了工作面应力变化和不同构造的电磁辐射响应规律;运用时间序列数据挖掘方法,对冲击地压电磁辐射监测数据进行处理和挖掘,对冲击地压电磁辐射前兆信息进行了定量识别,建立了冲击地压电磁辐射前兆信息群体识别体系。
(1)实验研究了冲击煤岩在全应力应变峰前、峰后阶段以及煤岩摩擦的电磁辐射特征。煤岩在全应力应变峰前阶段的电磁辐射信号随应力增加而增强,在峰后阶段随应力降低呈先上升后下降的趋势;煤岩摩擦面切应力的增加和加载速率的增加,使得电磁辐射信号增强。
(2)现场测试和分析了工作面应力变化和不同构造的电磁辐射响应规律。电磁辐射的测试结果能够反映工作面应力分布及变化的规律;断层和褶曲等地质构造周围的电磁辐射信号有异常反映。
(3)运用传统时间序列分析方法对冲击地压电磁辐射前兆数据进行了分析,并采用变量R聚类的数据挖掘方法,度量了冲击地压电磁辐射前兆子序列的相似性。研究表明,有冲击危险的电磁辐射数据是一个非白噪声平稳序列,蕴含有可提取的前兆信息,可以用ARMA时间序列模型来拟合及预测;依据相似性度量结果能够对监测范围进行危险性区域划分,确定冲击地压的重点防治区域。
(4)建立了以均值和方差为参数的电磁辐射强度异常判别及预警准则,提出了个体和群体电磁辐射异常的判别指标,对冲击地压电磁辐射前兆信息进行了定量识别。结果表明,电磁辐射强度异常判别及预警准则能够评价电磁辐射异常的危险程度,个体和群体电磁辐射异常判别指标能够对前兆信息进行有效识别。
(5)基于冲击地压电磁辐射前兆信息的定量识别,建立了冲击地压电磁辐射前兆群体识别体系,并进行了验证。结果表明,该体系的三个识别层次(包括:个体异常识别、子群体异常识别和群体异常识别)能够对冲击地压电磁辐射前兆信息进行有效的提取和识别。
本文的创新性主要体现在冲击地压电磁辐射前兆的时间序列数据挖掘和前兆信息群体识别研究两个方面。
本文对冲击地压电磁辐射前兆信息的识别进行了比较系统的研究,对冲击地压的预测具有重要的理论和实用价值。
Electromagnetic radiation technology has been used to forecast rock burst in some coalmines in our country. It is the development trend of forecasting rock burst by EMR technology in the future that how to recognize the precursor information of EMR monitoring data for rock burst effectively, study the EMR precursor characteristics of rock burst, and forecast rock burst accurately.
In the paper, we have studied the EMR characteristics of coal or rock samples in the experiments, with field tests analyzed the EMR responding laws of different tectonics and the stress change on the working face, mined and analyzed the EMR monitoring data of rock burst by time series data mining (TSDA) technology, recognized the EMR precursor information of rock burst quantitatively, and established the group recognition system of the EMR precursor information of rock burst.
EMR characteristics of coal or rock samples during the pre-peak phase and the post-peak phase of complete stress-strain experiments and in the friction experiments were studied. EMR signals which are produced by the deformation of coal or rock samples increase with the increase of stress during the pre-peak phase of the complete strain-stress process, and firstly increase and then decrease with the reduction of stress during the post-peak phase. In the friction experiments, the intensity of EMR signals are made to be enhanced with the increase of shear stress between the friction surfaces and the increase of loading rate .
EMR responding laws of different tectonics and stress change on the working face are tested in the field and analyzed. EMR testing results can reflect the laws of the stress distribution and change on the working face. EMR signals produced by coal or rock around the tectonics such as faults and folds are usually abnormal.
EMR precursor data of rock burst is analyzed by the traditional time series analysis method, and the similarity of EMR precursor sequence is measured by the data mining method of variable cluster. Research results show that EMR data of rock burst is a non-white noise and stationary sequence in which there is the precursor information to be extracted. ARMA model can be used to fit and forecast this sequence. According to the similarity measurement, the monitoring region of rock burst is divided into several areas with different risk, and the area with more risk is confirmed as the important area for predicting and treating rock burst.
We have established the discrimination and warning criterion of EMR intensity abnormity including two statistical parameters of mean and variance, put forward the discrimination indexes of EMR individual and group abnormity, and recognized the EMR precursor information of rock burst quantitatively. The results show that this discrimination and warning criterion can estimate the dangerous degree of EMR abnormity, and these discrimination indexes can recognize the EMR precursor effectively.
Based on the quantitative recognition of the EMR precursor information of rock burst, the group recognition system of EMR precursor is established and demonstrated. The demonstration results show that the three recognition levels of this system, including individual abnormity recognition, sub-group abnormity recognition and group abnormity, all can extract and recognize the EMR precursor information of rock burst effectively.
The innovations of the paper are the time series data mining of EMR precursor of rock burst, and the group recognition of the precursor information.
In the paper, the recognition of EMR precursor information of rock burst has been studied systematically, having an important theoretical and practical significance of forecasting rock burst.
引文
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