基于分峰思想的煤岩显微组分识别与统计分析

doi:10.16180/j.cnki.issn1007-7820.2023.04.002

摘要/Abstract

摘要：

针对现有方法识别煤岩显微组分准确率低的问题,文中提出了一种基于分峰思想的煤岩显微组分识别与统计分析方法。文中从单颗粒角度确定各煤种的镜质组峰值偏移范围,并提出自适应寻峰算法选取煤岩颗粒的有效峰值点。在煤岩显微组分识别阶段设计多策略的分峰峰位识别算法将煤岩颗粒分类为需要分峰聚类的活惰结合颗粒和无需分峰的纯镜质组颗粒、惰质组颗粒和壳质组颗粒,确定需要分峰聚类煤岩颗粒的分峰峰位,然后基于分峰规则和统计学方法进行高斯拟合,分别确定壳质组阈值、镜质组阈值和惰质组阈值,完成各煤岩颗粒的聚类分割。实验结果表明,文中方法能够有效识别单个煤岩颗粒并实现显微组分含量的定量统计,准确率达到 96.85%,熵值最小低至 0.615 3,与传统方法相比准确性更高,具有较好的现实应用意义。

关键词: 煤岩显微组分, 分峰思想, 统计分析方法, 自适应寻峰算法, 煤岩颗粒, 多策略, 分峰规则, 高斯拟合, 聚类

Abstract:

In view of the low accuracy of the existing methods to identify coal macerals, a method for identifying and statistical analysis of coal macerals based on the idea of peak splitting is proposed in this study. The peak offset range of vitrinite of each coal type is determined from the point of view of individual particle, and an adaptive peak finding algorithm is proposed to select the effective peak point of coal and rock particles. In the coal macerals identification stage, the multi-strategy peak position identification algorithm is designed to classify the coal and rock particles into active-inert particles requiring peak clustering and pure vitrinite particles, inertinite particles and exinite particles without peak clustering, and the peak positions of coal and rock particles requiring peak clustering are selected. Then, Gaussian fitting is carried out based on peak splitting rules and statistical methods to determine the threshold values of exinite, vitrinite, and inertinite respectively, and complete the clustering and segmentation of coal and rock particles. The experimental results show that the proposed method can effectively identify single coal particles and realize quantitative statistics of maceral content, with an accuracy of 96.85% and the minimum entropy of 0.615 3. Compared with the traditional method, the proposed method has higher accuracy and has better practical application significance.

Key words: coal macerals, the idea of peak splitting, statistical analysis method, adaptive peak finding algorithm, coal and rock particles, multi-strategy, peak splitting rules, Gaussian fitting, clustering

中图分类号:

TQ531

陈纯,舒慧生,阚秀,孙维周. 基于分峰思想的煤岩显微组分识别与统计分析[J]. 电子科技, 2023, 36(4): 9-20.

CHEN Chun,SHU Huisheng,KAN Xiu,SUN Weizhou. Identification and Statistical Analysis of Coal Macerals Based on the Idea of Peak Splitting[J]. Electronic Science and Technology, 2023, 36(4): 9-20.

图/表 23

图1

表1

图2

图3

图4

图5

表2

表3

图6

表4

图7

图8

图9

图10

图11

算法1 基于多策略的分峰峰位识别算法

输入:各煤岩颗粒γ的反射率分布标准差std,通过自适应寻峰算法选取的所有有效峰(x_pj,y_pj),j=1,2,…,v。v为峰的个数。令镜质组峰偏移范围为[α, β],且峰位值为η。其中,E表示壳质组颗粒,V表示镜质组颗粒,I表示惰质组颗粒,B表示活惰结合颗粒。
输出:煤岩颗粒γ的所属类别,需要分峰的分峰峰位点(

x p b,

y_pb)。
if std≤0.2 and ν=1:
γ ∈ V
end if
if (std≤0.2 and ν>1) or std>0.2:
if $ j_k ∈ [1, ν],k=1,2,…,u,

x p j k

∈ [α, β]:
if ν=1:
γ ∈ V
else:
let x_pc=

x p j k 0

when

y p j k 0

=max{

y p j k

,k ∈ [1, u]}
if " k ∈ [1, u],k1k₀, max{

y p j k

,k∈ [1, u], k1k₀}<

y p j k 0

/2 :

γ∈ B and x_pb =x_pc
end if
if $ k_l ∈ [1, u],k_l1k₀, l∈ [1, w], min{

y p j k l

, k_l∈ [1, u], k_l1k₀,
l∈ [1, w]}≥

y p j k 0

/2 :
if abs(

x p j k 0

)- η=min{abs(

x p j k l

)- η, k_l∈ [1, u],k_l1k₀,
l∈ [1, w]} :
then γ ∈ B and x_pb =

x p j k 0

end if
end if
else:
if " j ∈ [1, v],

x p j

< α:
γ∈ E
elif " j ∈ [1, v],

x p j

> β:
γ ∈ I
else:
γ∈ B and x_pb =min{

x p j k

, j_k[1, v], k ∈ [1, u]}
end if
end if
end if
输出煤岩颗粒γ的所属类别,需要分峰的分峰峰位点(x_pb, y_pb)。

图12

图13

表5

图14

表6

图15

图16

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煤岩批次	煤岩编号	标定灰度	标定反射率/%	反射率关系式
1	C₁	87	0.591	g=6 061x+52.00
	C₂	108	0.905
	C₃	156	1.721
	C₄	156	1.721
2	C₅	94	0.591	g=5 966x+59.61
	C₆	115	0.905
	C₇	162	1.721
3	C₈	89	0.591	g=5 966x+54.69
	C₉	110	0.905
	C₁₀	157	1.721

编号	颗粒类别	形态特征	占比
1	纯镜质组颗粒	表面平滑,std低	较多
2	纯壳质组颗粒	表面平滑,std低	很少
3	纯惰质组颗粒	表面突兀,std高	较多
4	壳质组-镜质组-惰质组颗粒	表面较突兀,std较高	多
5	镜质组-惰质组颗粒	表面较突兀,std较高	多
6	壳质组-镜质组颗粒	表面平滑,std低	较多
7	壳质组-惰质组颗粒	表面较突兀,std较高	少

定义	说明
n	总数据点个数
w₁	窗长
μ	在一个窗长w₁内,满足峰值条件的最大半宽与窗长半宽的比例

定义	说明
n	总数据点个数
w₂	窗长
θ	在一个窗长w₂内,满足拐点定义的条件下,左右端点相对拐点的导数平均变化率

镜质组峰位	正态分布最大标准差
≤ 1.0%	0.10
> 1.0%	0.12