本文選題：核Fisher判別　切入點(diǎn)：地球化學(xué)異常　出處：《成都理工大學(xué)》2015年碩士論文

【摘要】：在地球化學(xué)礦產(chǎn)勘查研究中,進(jìn)行地球化學(xué)異常評價(jià)重要環(huán)節(jié)是識別地球化學(xué)異常是非礦致異�；蚴堑V致異常,進(jìn)而圈定合理的找礦遠(yuǎn)景區(qū)。對地球化學(xué)異常區(qū)域的有效圈定不但有利于人們尋找礦區(qū),而且可以縮減物力,節(jié)省財(cái)力,減少人力資源。因此本論文研究的重點(diǎn)是對地化地球化學(xué)異常進(jìn)行識別。在數(shù)據(jù)處理過程中由于地球化學(xué)的大部分元素含量指標(biāo)通常表現(xiàn)為互相影響和相互關(guān)聯(lián)的形式,在對地球化學(xué)異常進(jìn)行評價(jià)的時(shí)候,不能僅僅考慮少數(shù)元素含量指標(biāo),應(yīng)盡可能多的考慮元素綜合指標(biāo),這有助于提高異常識別的準(zhǔn)確性。因此,地球化學(xué)元素綜合異常或組合異常是判斷地球化學(xué)礦致異常的有效方法。Fisher判別分析是一種有效的分類方法,它通過選擇最佳的投影向量將數(shù)據(jù)進(jìn)行投影變換,從而達(dá)到將數(shù)據(jù)進(jìn)行分類的目的。這一思想可以為礦致異常的圈定提供數(shù)據(jù)支撐。然而,由于地質(zhì)系統(tǒng)的復(fù)雜性,導(dǎo)致了地球化學(xué)元素信息表現(xiàn)出模糊性與非線性。在Fisher判別分析基礎(chǔ)上定義的線性判別函數(shù)就不能很好的表示這種關(guān)系。因此,引入核函數(shù)更適合對地球化學(xué)數(shù)據(jù)進(jìn)行判別。核方法指的是將線性不可分的輸入空間數(shù)據(jù)非線性映射到高維特征空間,這樣不可分的空間數(shù)據(jù)變?yōu)榭煞謹(jǐn)?shù)據(jù),從而可以采取線性的方法在特征空間上進(jìn)行數(shù)據(jù)的特征提取。因此,核Fisher方法是比Fisher判別分析方法更有效的非線性分類方法。本文研究工作是利用基于核函數(shù)的Fisher判別分析對地球化學(xué)異常進(jìn)行識別�；诤撕瘮�(shù)的Fisher判別分析對異常進(jìn)行分類判別的原理是指借助“核技巧”將輸入數(shù)據(jù)空間隱式地變換到一個(gè)非線性的特征空間,這樣在變換的空間上利用線性的Fisher判別分析對數(shù)據(jù)進(jìn)行異常識別。通過對鄂東南地區(qū)的1:20萬水系地球化學(xué)數(shù)據(jù)的研究,表明基于核函數(shù)的Fisher判別分析在對地球化學(xué)異常識別方面效果顯著。
[Abstract]:In the research of geochemical mineral exploration, the important link of geochemical anomaly evaluation is to identify the geochemical anomaly as non-mineral-induced anomaly or ore-induced anomaly, and then to delineate a reasonable prospecting area.The effective delineation of geochemical anomaly area is not only helpful for people to search for mining area, but also can reduce material resources, save financial resources and reduce human resources.Therefore, the focus of this paper is to identify geochemical anomalies.In the process of data processing, because most of the element content indexes of geochemistry usually show the form of mutual influence and correlation, when evaluating geochemical anomalies, we can not only consider a few element content indexes.As many elements as possible should be considered, which is helpful to improve the accuracy of anomaly recognition.Fisher discriminant analysis (Fisher discriminant analysis) is an effective classification method. It can transform the data by selecting the best projection vector.In order to achieve the purpose of data classification.This idea can provide data support for the delineation of ore-induced anomalies.However, because of the complexity of geological system, geochemical element information shows fuzziness and nonlinearity.The linear discriminant function defined on the basis of Fisher discriminant analysis can not express this relationship well.Therefore, the introduction of kernel function is more suitable to judge geochemical data.Kernel method refers to the nonlinear mapping of input space data from linear inseparability to high dimensional feature space, so that the inseparable spatial data can be transformed into separable data, so that the feature extraction of data can be carried out in the feature space by linear method.Therefore, kernel Fisher method is more effective than Fisher discriminant analysis in nonlinear classification.In this paper, Fisher discriminant analysis based on kernel function is used to identify geochemical anomalies.The principle of classifying anomalies by Fisher discriminant analysis based on kernel function is to implicitly transform the input data space into a nonlinear feature space by means of "kernel technique".In this way, the linear Fisher discriminant analysis is used to identify the anomaly of the data in the space of transformation.Based on the geochemical data of 1: 200 000 drainage system in southeast Hubei, it is shown that the Fisher discriminant analysis based on kernel function is effective in identifying geochemical anomalies.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：P632

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