發(fā)布時間：2018-08-30 12:32

【摘要】：化探數(shù)據(jù)處理是勘查地球化學(xué)的一項重要內(nèi)容,不同的數(shù)據(jù)處理方法直接影響著化探找礦的效果及效率�；綌�(shù)據(jù)處理是應(yīng)用數(shù)學(xué)方法和計算機技術(shù),從化探原始數(shù)據(jù)中發(fā)現(xiàn)和提取有效信息,揭示化學(xué)元素與各種地質(zhì)現(xiàn)象的內(nèi)在聯(lián)系,為地球化學(xué)找礦提供依據(jù)。如何科學(xué)有效地提取化探異常信息,并從大量異常中進行快速準(zhǔn)確地篩選評價,以確定進一步找礦的靶區(qū),則是決定化探找礦工作關(guān)鍵。地球化學(xué)元素含量值并不局限于正態(tài)分布或者對數(shù)正態(tài)分布,具有不連續(xù)性、突變性、非均勻性、多樣性和隨機性等特征,即非線性特征。在進行化探數(shù)據(jù)處理時,對于非線性的特征就要采用非線性的算法。本文以青海省大柴旦鎮(zhèn)柴達(dá)木山南坡一帶地區(qū)為例,采用傳統(tǒng)統(tǒng)計方法、KNN算法、聚類分析、主成分分析、ISOMAP算法對研究區(qū)1:10000土壤地球化學(xué)測量數(shù)據(jù)進行分析處理,在了解研究區(qū)地質(zhì)背景的基礎(chǔ)上,圈定成礦遠(yuǎn)景區(qū),為該區(qū)下一步地質(zhì)勘探工作提供了工作靶區(qū)。從地球化學(xué)元素含量異常的評價與研究出發(fā),利用了現(xiàn)代的數(shù)學(xué)方法和非線性分析的方法,挖掘化探數(shù)據(jù)中蘊含的成礦異常信息。通過與傳統(tǒng)分析方法的對比表明,KNN分類算法對化探數(shù)據(jù)中的元素含量異常有很好的識別作用。利用主成分分析將Cu、Au、Zn、As、Sb、Pb六種元素分成了兩組,并在此基礎(chǔ)上圈定了兩組元素的組合異常。利用ISOMAP同樣將六種元素分為兩組,圈定此時兩組元素的組合異常。通過對比得到,用ISOMAP算法圈定的組合元素異常比主成分分析圈定的異常區(qū)域分布集中且形狀規(guī)則。
[Abstract]:Geochemical data processing is an important part of exploration geochemistry. Different data processing methods directly affect the effect and efficiency of geochemical prospecting. Geochemical data processing is the application of mathematical methods and computer technology to discover and extract effective information from the original data of geochemical exploration, to reveal the inherent relationship between chemical elements and various geological phenomena, and to provide the basis for geochemical prospecting. How to extract geochemical anomaly information scientifically and effectively, and how to select and evaluate quickly and accurately from a large number of anomalies in order to determine the target area for further prospecting is the key to determine the geochemical prospecting work. The content of geochemical elements is not limited to normal distribution or logarithmic normal distribution. It has the characteristics of discontinuity, mutation, heterogeneity, diversity and randomness, that is, nonlinear characteristics. In the process of geochemical data processing, nonlinear algorithm should be used for nonlinear characteristics. Taking the south slope area of Qaidam Mountain in Dachaidan Town, Qinghai Province as an example, this paper uses the traditional statistical method, such as KNN algorithm, clustering analysis, principal component analysis (PCA) and ISOMAP algorithm, to analyze and process the geochemical data of 1: 10000 soil in the study area. On the basis of understanding the geological background of the study area, the metallogenic area is delineated, which provides a working target area for the further geological exploration in this area. Based on the evaluation and study of geochemical element anomaly, the information of metallogenic anomaly contained in geochemical exploration data is excavated by using modern mathematical method and nonlinear analysis method. The comparison with the traditional analysis method shows that the KNN classification algorithm has a good effect on identifying the anomaly of element content in geochemical data. The six elements of Cu,Au,Zn,As,Sb,Pb are divided into two groups by principal component analysis, and the combined anomalies of the two groups of elements are delineated on this basis. The six elements are also divided into two groups by ISOMAP, and the combined anomalies of the two groups are delineated. By comparison, it is found that the combined element anomalies delineated by ISOMAP algorithm are more concentrated and regular in shape than those delineated by principal component analysis (PCA).
【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

【參考文獻】

相關(guān)期刊論文 前2條

1 吳敏金;分形信息論及其應(yīng)用[J];華東師范大學(xué)學(xué)報(自然科學(xué)版);1996年01期

2 周靖;劉晉勝;;一種采用類相關(guān)度優(yōu)化距離的KNN算法[J];微計算機應(yīng)用;2010年11期

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本文編號：2213077