【摘要】：煤與瓦斯突出是威脅煤礦安全生產(chǎn)的最嚴(yán)重自然災(zāi)害之一。目前我國煤與瓦斯突出區(qū)域預(yù)測主要集中在開拓后依靠井下巷道、實(shí)測瓦斯參數(shù)為主的點(diǎn)預(yù)測,現(xiàn)場工程實(shí)踐遇到的開拓前煤與瓦斯突出危險(xiǎn)性預(yù)測難題,一直沒有得到切實(shí)可行的解決方法。支持向量機(jī)作為一種可以很好的解決小樣本、非線性、高維數(shù)等實(shí)際問題的模式識(shí)別方法,已被廣泛應(yīng)用到眾多生產(chǎn)領(lǐng)域預(yù)測預(yù)報(bào)中,為此本文引入支持向量機(jī)建立學(xué)習(xí)模型對煤礦開拓前的突出危險(xiǎn)進(jìn)行預(yù)測,期望提高開拓前煤與瓦斯突出預(yù)測準(zhǔn)確性。山西呂梁某礦9號(hào)煤層尚未揭露,無井下瓦斯參數(shù)實(shí)測條件,對礦井瓦斯賦存規(guī)律進(jìn)行研究,收集礦井地勘時(shí)期所測的瓦斯參數(shù)并進(jìn)行分析。采用鉆孔煤屑解吸法實(shí)測同屬一個(gè)地質(zhì)單元鄰近礦井瓦斯含量,對地勘瓦斯含量進(jìn)行修正;根據(jù)瓦斯含量和瓦斯壓力之間的關(guān)系,利用間接法反算煤層瓦斯壓力。煤與瓦斯突出與其各個(gè)因素之間存在著復(fù)雜的非線性關(guān)系,本文分析支持向量機(jī)預(yù)測煤與瓦斯突出的可行性,引入粒子群優(yōu)化算法(PSO),利用粒子群算法對支持向量機(jī)參數(shù)進(jìn)行優(yōu)化,建立開拓前煤與瓦斯突出PSO-SVM預(yù)測模型。收集同屬一個(gè)地質(zhì)單元的鄰近礦井突出樣本作為訓(xùn)練樣本,呂梁某礦地勘修正后數(shù)據(jù)作為測試樣本,利用MATLAB編寫煤與瓦斯突出預(yù)測的PSO-SVM算法程序?qū)ζ?號(hào)煤層進(jìn)行煤與瓦斯突出預(yù)測,與單項(xiàng)指標(biāo)法和綜合指標(biāo)D與K法預(yù)測結(jié)果一致�；诹Ｗ尤簝�(yōu)化的支持向量機(jī)用在開拓前煤與瓦斯突出危險(xiǎn)預(yù)測中是可行的,為礦井開拓前煤與瓦斯突出預(yù)測提供方向。
[Abstract]:Coal and gas outburst is one of the most serious natural disasters threatening coal mine safety. At present, the regional prediction of coal and gas outburst in China is mainly focused on the underground roadway after development, the point prediction based on the measured gas parameters, and the difficult problem of prediction of coal and gas outburst danger encountered in the field engineering practice before the development of coal and gas outburst. There has been no practical solution. Support vector machine (SVM), as a pattern recognition method which can solve many practical problems such as small sample, nonlinear and high dimension, has been widely used in many production fields. In order to improve the accuracy of coal and gas outburst prediction, this paper introduces support vector machine to establish a learning model to predict the outburst danger before the development of coal mine. Coal seam No. 9 in a certain mine of Lv Liang, Shanxi Province, has not been exposed, and there is no condition of underground gas parameter measurement. The law of gas occurrence in coal mine is studied, and the gas parameters measured in geological prospecting period of mine are collected and analyzed. The gas content in coal mine adjacent to the same geological unit is measured by the method of coal cuttings desorption by borehole, and the gas content in geological prospecting is corrected. According to the relationship between gas content and gas pressure, the indirect method is used to calculate the gas pressure in coal seam. There is a complex nonlinear relationship between coal and gas outburst and its various factors. This paper analyzes the feasibility of predicting coal and gas outburst by support vector machine. The particle swarm optimization (PSO),) algorithm is introduced to optimize the parameters of support vector machine (SVM), and the PSO-SVM prediction model of coal and gas outburst is established. The outburst samples of adjacent mines belonging to the same geological unit were collected as training samples, and the revised geological prospecting data of a certain mine of Lv Liang were used as test samples. The PSO-SVM algorithm program of coal and gas outburst prediction is compiled by MATLAB to predict coal and gas outburst in coal seam No. 9, which is consistent with the prediction results of single index method and synthetic index D and K method. The application of support vector machine based on particle swarm optimization is feasible in predicting the danger of coal and gas outburst before exploitation, which provides a direction for the prediction of coal and gas outburst before mine development.
【學(xué)位授予單位】：華北科技學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TD713.2

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