2000-2014年中国重特大煤矿事故时空分布分形特征

doi:10.3724/SP.J.1047.2016.01191

摘要/Abstract

摘要：

本文汇总整理了2000-2014年中国发生的472起重特大煤矿事故,获得其空间分布状况;基于分形理论,利用网格法统计不同网格尺度下覆盖重特大煤矿事故的非空网格中的事故数量,计算煤矿事故的全国、三大自然地理区以及干湿分区的分维值,分析其空间分布分形特征;利用R/S方法研究15年来重特大煤矿事故发生的时间分形特征。研究表明,从全国范围来看,重特大煤矿事故具有较好的空间分形特性,但不同区域的重特大煤矿事故的分形特性却具有很大差别。15年来发生重特大事故数量及事故死亡人数逐年下降,由R/S分析得到的Hurst指数表明事故时间序列的分形特征客观存在,并且年发生事故数量及事故死亡人数量减少趋势。

关键词: 重特大煤矿事故, 时空分布, 分形, 分维值, Hurst指数

Abstract:

The frequent occurrence of coal mine accidents has brought great losses to people's lives and threatened the safety of our country and residents. The fractal theory is a newly developed subject in recent years, and it has been turned into a powerful tool to describe the nonlinear problems. In this paper, analyses and discussions on the fractal characteristics of the temporal-spatial distribution and relativity as well as the regularity of the temporal distribution of the national catastrophic coal mine accidents from 2000 to 2014 in China have been calculated using the relevant methods of fractal theory. Firstly, based on previous studies, this paper summarized the coal mine accidents occurred between 2000 and 2014 in China and obtained the time of occurrence, latitude and longitude for each coal mine accident. Secondly, the grid method is used to measure the number of accidents in grids which cover the area of the coal mine accidents at different grid scale levels, including scales for the national wide region, the three dominating physical geographical regions and the arid and humid regions. We firstly numerate the accidents in every grid and calculate the information content for different grid scales. Then, a linear fit for the logarithms of the information content and grid scale is calculated, and as a result the absolute value of the line slope is taken as the fractal dimension. Finally, the Hurst exponent of the time series of the coal mine accidents is obtained by adopting the method of resealed range analysis (R/S Analysis). Results show that the fractal characteristics of the spatial distribution of China's coal mine accidents are objectively existent. But the fractal dimension values of the spatial distribution of the coal mine accidents in different regions are significantly different. Between 2000 and 2014, the number of total accidents and deaths peaked at 2005, and then it decreased gradually indicating that the safety situation of China's coal mine production keeps improving. The Hurst exponent of the time series obtained from the R/S analysis indicates that the fractal characteristics of the time series objectively exist and the decreasing trend is enhanced. Some opinions regarding the applications of fractal theory in coal mine accidents are put forward in the end.

Key words: coal mine accident, temporal-spatial distribution, fractal theory, fractal dimension, Hurst exponent

张英慧, 王伟, 高星, 伍宇明. 2000-2014年中国重特大煤矿事故时空分布分形特征[J]. 地球信息科学学报, 2016, 18(9): 1191-1198.DOI:10.3724/SP.J.1047.2016.01191

ZHANG Yinghui,WANG Wei,GAO Xing,WU Yuming. The Fractal Characteristics of the Temporal-Spatial Distribution of Coal Mine Accidents in China from 2000 to 2014[J]. Journal of Geo-information Science, 2016, 18(9): 1191-1198.DOI:10.3724/SP.J.1047.2016.01191

图/表 13

图1

图2

图3

图4

表1

三大自然地理区重特大煤矿事故空间分布分维值"

	线性回归方程	R	是否通过显著性检验	D
东部季风区	$ln I = - 0.8491 ln r + 8.3172$	0.9821	是	0.8491
西北干旱区	$ln I = - 0.1817 ln r + 3.2823$	0.8854	是	0.1817
青藏高原区	$ln I = - 0.2226 ln r + 1.9167$	0.6837	否	-

表1

图5

图6

图7

表2

干湿分区重特大煤矿事故空间分布分维值"

	线性回归方程	R	是否通过显著性检验	D
半干旱区	$ln I = - 0.5121 ln r + 4.9568$	0.9699	是	0.5121
半湿润区	$ln I = - 0.7782 ln r + 7.4979$	0.9810	是	0.7782
干旱区	$ln I = - 0.1604 ln r + 2.5473$	0.8547	是	0.1604
湿润区	$ln I = - 0.7766 ln r + 7.4893$	0.9811	是	0.7766

表2

表3

表4

图8

图9

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年份	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014
数量	59	44	49	46	36	56	35	31	28	22	14	22	12	13	5
死亡人数	1188	1006	1157	1211	934	1716	732	617	518	509	328	342	216	225	84

时间序列	年份	R_事故次数	S_事故次数	R_死亡人数	S_死亡人数	ln(R/S)_事故次数	ln(R/S)_死亡人数
1	2000	-	-	-	-	-	-
2	2001	7.50	7.50	91	91	0	0
3	2002	8.33	6.24	111	79.50	0.2899	0.3337
4	2003	9.50	5.77	134.5	79.98	0.4993	0.5198
5	2004	12.20	7.47	169.6	109.27	0.4909	0.4396
6	2005	18.33	7.63	514.0	250.58	0.8766	0.7185
7	2006	12.71	8.47	581.1	284.37	0.4067	0.7147
8	2007	23.00	9.42	791.3	316.37	0.8926	0.9167
9	2008	34.00	10.28	1159.3	345.08	1.1957	1.2118
10	2009	46.40	11.56	1459.2	360.07	1.3897	1.3993
11	2010	60.90	13.42	1803.3	388.26	1.5130	1.5357
12	2011	69.00	13.60	2083.0	402.61	1.6240	1.6436
13	2012	80.54	14.65	2377.8	422.62	1.7045	1.7275
14	2013	91.50	15.20	2626.7	433.84	1.7949	1.8008
15	2014	104.73	16.30	2911.9	452.17	1.8602	1.8625