Geofluids

Volume 2019, Article ID 5485731, 9 pages

https://doi.org/10.1155/2019/5485731

## Research on Water-Filled Source Identification Technology of Coal Seam Floor Based on Multiple Index Factors

^{1}Institute of Resources & Environment, Henan Polytechnic University, Jiaozuo 454000, China^{2}Collaborative Innovation Center of Coalbed Methane and Shale Gas for Central Plains Economic Region, Jiaozuo, Henan Province 454000, China^{3}Institute of Resources & Environment, North China University of Water Resources and Electric Power, Zhengzhou, Henan 450000, China^{4}Energy and Chemical Industry Group of China Pingmei Shenma, Pingdingshan 467000, China^{5}Shandong Weifang Zongheng Building Materials Company Limited, Weifang, Shandong 262404, China

Correspondence should be addressed to Guang Yang; moc.qq@293067916 and Qi Wang; nc.ude.uwcn@iqgnaw

Received 26 October 2018; Revised 3 January 2019; Accepted 14 February 2019; Published 31 March 2019

Academic Editor: Carlos R. De Souza Filho

Copyright © 2019 Xinyi Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

According to the No. 13 Mine of Pingdingshan Coal Co. Ltd., the fault dimension of the study area is calculated based on fractal theory. The four impact factors such as water inflow, water pressure, water inrush coefficient, and fault fractal dimension of 21 water boreholes are used as evaluation indices, and a mathematical model for identifying the water-filled source of the coal seam floor is established by coupling an entropy weight method and fuzzy variable set theory. The model is used to identify the water-filled source of 7 boreholes, which provide a reliable reference for the identification of the water-filled source. According to the calculation result of the entropy weight method, the water volume per unit of the borehole and the groundwater pressure have a significant impact on the water source identification, which accounts for 89.93% of the weight value. In the fuzzy variable set model, when the distance parameter is 1 and the optimization criterion parameter is 2, the accuracy of the water sample identification of the water source category to be identified is 85.71%, which is at a high recognition level. The more typical the impact factors selected and the more samples, the accuracy of water-filled source identification is much higher.

#### 1. Introduction

The Pingdingshan coalfield is located in a North China typical coalfield area. At present, the No. 2-1 coal seams of the Permian Shanxi formation are studied. The coal distribution has 5–7 layers of Carboniferous thin limestone aquifer and a thick layer of Cambrian limestone aquifer. Due to the influence of geological structures and coal mining disturbance, faults and fissures are developed in thin layer of limestone aquifers and aquicludes, which lead to a close hydraulic connection between the thin layer of limestones and the thick layer of limestone aquifers, so the coal seam mining process is threatened by the high-pressure aquifer of the bottom limestone. In order to reduce the influence of water inrush on safe underground production, the key to controlling mine water disasters is to identify the water source quickly and accurately. At present, the main methods for identifying water-filled sources include geological analysis, hydrodynamic analysis, hydrochemical analysis, water temperature analysis, and geophysical prospecting [1].

Liu et al. considered the influence of water temperature and water level combined with a QLT mathematical model to identify the water-filled source in the Panxie mining area [2]. Gui and Lu analyzed the main water inrush and the hydraulic connection between aquifers in the Wanbei mining area by using the radioactive isotope tritium as the discriminant index [3]. Yuan and Gui established the ground temperature equation according to the geothermal characteristics when judging the water source of the Renlou coal mine and achieved good results based on the temperature analysis method [4]. Chen established a three-dimensional (3D) geological model based on the geological structure of the Sunan mining area and identified the water-filled source by simulating the groundwater flow [5]. Wang et al. used six conventional ions as the discriminating factor for the Jiaozuo mining area. Based on the distance discriminant analysis and grey system correlation degree method, the discriminant model of water-filled source identification was established, and the application test was carried out in the Xin’an mining area under similar hydrogeological conditions [6]. Wang et al. analyzed the 6 conventional ions in the groundwater of Pingdingshan coalfield and refined the hydrogeological unit. Based on this, the water source discrimination model was established [7]. Gao carried out hydrogeological pumping and water injection tests in Qiganlou Iron Mine, analyzed its hydrogeological parameters, and predicted the water-filled source of the mine [8]. Wang et al. quantitatively analyzed the structural faults in the Luan mining area and, combined with the improved analytic hierarchy process (AHP), evaluated the dangerous degree of water inrush in the Luan mining area [9]. With the continuous development and improvement of science and technology, a large amount of high-precision equipment has been applied to water source identification. Based on geographic information system technology and drilling depth water temperature fitting results, Ma et al. constructed a model to identify water-filled sources [10]. Zhang used laser-induced fluorescence technology as a means for identifying the water-filled source based on changes in water fluorescence caused by different parameters, such as water temperature and the flow rate of the water source [11]. These achievements provide a reference for future generations to study the water-filled source of mines.

Previous studies have used modern mathematical methods to establish identification models and identify water-filled sources using typical water chemical components, temperature, water level, etc. These methods are simple and fast. However, due to the difficulty of water sample collecting and the external interference of water sample test results, the established identification model is inconsistent with the reality. When geological structure and hydrogeological parameters are used as indicators, they are not quantitatively analyzed. They have space limitations in applicability and cannot be used as a general method to solve problems in other places, which in turn makes it difficult to accurately judge individual water-filled sources.

In this paper, the Carboniferous thin layer and the Cambrian thick limestone aquifer in the coal seam floor of Pingmei No. 13 Mine are used as the object, and the fractal theory is used to quantitatively evaluate the fault complexity of the mining area. Taking groundwater pressure, water inrush coefficient, water inflow per borehole, and fractal dimension of faults as index factors, the weight of each index is calculated based on the entropy weight method, and then the fuzzy variable set theory is used to obtain the comprehensive relative membership degree of the water sample to be discriminated. Finally, the water samples are classified according to the principle of maximum membership degree to identify the water-filled source. The key factors of water inrush are fully considered when the model is built, and the external disturbance is removed to the greatest extent, so as to accurately identify the water-filled source of the coal seam floor.

#### 2. Mathematical Method

##### 2.1. Entropy Weight Method

Determining the weight of each index is the key to calculating the relative membership degree of each water sample. The accuracy of the identification results is directly related to the selection of appropriate methods. The methods for determining weights include AHP, fuzzy inverse equation method, and entropy weight method [12]. Among them, the weight value of the entropy weight method is objective and reliable and can quantify the effective information of each index and provide a new idea for the comprehensive evaluation of each discriminant index. Therefore, this paper uses the entropy weight method to calculate the weight of each index. The calculation steps are as follows [13].

There are kinds of water sources to judge, and the identification indices are . Based on the average value of each identification index , the evaluation matrix is set up. where the entropy value is as follows: When , then .

In the formula,

The weight value is as follows:

##### 2.2. Fuzzy Variable Set Theory

Fuzzy variable set theory is established based on fuzzy set theory. Using the relative membership function, the theory constructs a relative difference function with relativity and dynamic variability, and the concept and model of fuzzy variable set are depicted. This theory expands the static concept and definition of the fuzzy set membership function, overcomes the defect of the static and unique membership function of the fuzzy set, and is widely used in the field of hydrological resources and other engineering examples [14–17].

###### 2.2.1. Determination of Relative Membership Degree

Starting with a fuzzy concept (phenomenon or thing) on the domain, let be any element on and satisfy . Let denote the relative membership degree of the attraction property of the fuzzy concept as , and the relative membership degree of the repellent property is . If , it means that the two properties reach dynamic equilibrium; if , it means that equals and attracts the dominant property; if , the opposite is true.

is the relative difference degree of to on the continuous number axis, in the formula .

This is the relative difference function of to.

In addition, the definition of the redundant set of fuzzy variable sets is as follows:

Therefore, the relative membership degree is as follows:

###### 2.2.2. Construction of Relative Differential Function Model

Let be the interval range of on the number axis (Figure 1), i.e., the attraction domain of the fuzzy set ; is an interval within a finite range of ; and are the interval ranges of on the axis , the repulsive domain of the fuzzy set .