本文選題：礦井突水 + 主成分分析法��； 參考：《中南大學(xué)》2013年碩士論文

【摘要】：礦井突水是威脅礦山安全生產(chǎn)的最大自然災(zāi)害之一,正確識別突水水源可為制定合適的防治水措施提供重要依據(jù),因此,如何快速有效的判別礦井突水水源具有重要意義。目前,大部分水源判別模型沒有考慮到由于水化學(xué)判別指標(biāo)之間信息疊加從而導(dǎo)致水源誤判的影響。為此,本文以某兩個礦山(A、B礦山)的水文地質(zhì)資料和水化學(xué)數(shù)據(jù)研究對象,引用了主成分分析法對水化學(xué)指標(biāo)進(jìn)行信息提煉,并結(jié)合兩種多元判別方法來建立水源判別模型,并與傳統(tǒng)的多元判別模型(Fisher判別法、Bayes判別法、逐步判別法)進(jìn)行比較,找出最優(yōu)的水源判別模型。同時,為了讓礦井突水水源判別工作更加自動化和智能化,通過C++語言開發(fā)了礦井突水水源判別系統(tǒng)。本文研究主要結(jié)果如下： (1)選取A礦山各水層的Ca2+、Mg2+、K++Na+、HCO3-、SO42-Cl-、TDS的7種水化學(xué)指標(biāo)參數(shù)為數(shù)據(jù)樣本,利用Aquachem等水化學(xué)分析軟件繪制出Piper三線圖、常規(guī)離子箱圖、常規(guī)離子與TDS的關(guān)系圖等來分析各水層水化學(xué)特性。結(jié)果表明各水源具有不同的水化學(xué)特性,且太灰水與奧灰水的各離子分布范圍比較接近,兩者水化學(xué)特性較相似,難以通過某一單個離子分布規(guī)律來區(qū)分突水水源。 (2)以A、B礦山的水化學(xué)信息為研究對象,建立Fisher判別模型、Bayes判別模型、逐步判別模型、和與之相應(yīng)的經(jīng)過主成分分析的兩種判別模型。研究結(jié)果表明,經(jīng)過主成分分析處理后的Fisher判別模型、Bayes判別模型A礦山的回判和預(yù)判正確率分別得到提高了6%、18%,完全優(yōu)于傳統(tǒng)的Fisher判別模型、Bayes判別模型。通過主成分分析處理后消除了因?yàn)楦髦笜?biāo)信息的疊加而導(dǎo)致其水源誤判的問題,提高了模型識別突水的精度。 (3)以礦區(qū)各水源的水化學(xué)的差異性為理論基礎(chǔ),以Fisher判別法、Bayes判別法和主成分分析法相結(jié)合的模型為核心,采用C++語言開發(fā)出礦井突水水源判別系統(tǒng)。該系統(tǒng)不僅具有數(shù)據(jù)存儲、查詢功能,還可以快速的判別突水水源,并具有良好的實(shí)用性和可靠性,操作簡便。
[Abstract]:Mine water inrush is one of the biggest natural disasters threatening mine safety production. The correct identification of water inrush source can provide an important basis for making appropriate water prevention measures. Therefore, how to quickly and effectively distinguish mine water inrush source is of great significance.At present, most water source discriminant models do not take into account the influence of water source misjudgment due to the superposition of information among the water chemistry discriminant indexes.Therefore, based on the hydrogeological data and hydrochemical data of two mines, the principal component analysis (PCA) is used to extract the information of the hydrochemical indexes, and two multivariate discriminant methods are used to establish the water source discriminant model.Compared with the traditional multivariate discriminant model, Fisher discriminant method, Bayes discriminant method and stepwise discriminant method, the optimal water source discriminant model is found out.At the same time, in order to make the discrimination of mine water inrush water source more automatic and intelligent, a mine water inrush water source discrimination system is developed by C language.The main results of this paper are as follows:1) 7 kinds of hydrochemical index parameters of Ca2 mg _ 2 K _ 2O H _ CO _ 3-so _ 42-Cl-TDs in each water layer of mine A are selected as data samples, and Piper three-line diagram and conventional ion box diagram are drawn by using Aquachem isohydrochemical analysis software.The relationship between conventional ions and TDS was used to analyze the hydrochemical characteristics of each water layer.The results show that the water sources have different hydrochemical characteristics, and the distribution range of the ions in the ash water is similar to that in the Ordovician water, so it is difficult to distinguish the water inrush water sources by a single ion distribution law.Through principal component analysis, the problem of water source misjudgment caused by the superposition of index information is eliminated, and the accuracy of water inrush recognition is improved.3) based on the differences of water chemistry of water sources in mining areas, and based on the model of combining Fisher discriminant method with principal component analysis method, a discriminant system of mine water inrush water source is developed by C language.The system not only has the functions of data storage and query, but also can quickly distinguish the water source of water inrush, and has good practicability, reliability and simple operation.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TD745

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