本文選題：支持向量機 + 測井巖性識別　； 參考：《東北石油大學》2015年碩士論文

【摘要】：在油氣勘探的過程中,隨著深度的不斷加深,在地形較復雜的地方,使用傳統(tǒng)的方法很難快速準確的分辨出油氣位置,但處理此問題可使用巖性識別方法。巖性識別過程是一個非常典型的、具有高維特性的、很難解決的非線性的模式識別過程,而支持向量機(Support Vector Machine,簡稱SVM)正是解決此問題的最優(yōu)方法之一,其能夠很好地處理小樣本、靈活的轉換非線性、強悍的應對高維模式識別等問題。本文首先介紹了蟻群算法的理論和支持向量機的理論;其次,由于基于支持向量機參數的選取直接影響到支持向量機分類性能的好壞,因此,介紹了幾種支持向量機參數尋優(yōu)的方法,如網格搜索法、雙線性搜索法、窮舉法、遺傳算法、粒子群算法等,重點介紹了將交叉驗證法與蟻群算法的有效結合的新蟻群算法,將新蟻群算法優(yōu)化支持向量機同蟻群算法優(yōu)化支持向量機進行了比較,得知前者不但縮短了優(yōu)化SVM的時間,也提高了分類準確率;最后,針對大規(guī)模數據分類問題,傳統(tǒng)的支持向量機表現出很多的不足,因此,本文對傳統(tǒng)SVM進行了改進-最近鄰支持向量機,使用巖性測井數據訓練改進后的支持向量機和傳統(tǒng)的支持向量機,將實驗結果進行比較,其結果表明:針對大規(guī)模數據分類,最近鄰支持向量機表現出一定的優(yōu)勢。
[Abstract]:In the process of oil and gas exploration, along with the deepening of depth, it is difficult to distinguish the oil and gas position quickly and accurately by using the traditional method in the places where the topography is more complicated, but the lithologic identification method can be used to deal with this problem. The lithologic recognition process is a very typical, high dimensional and difficult nonlinear pattern recognition process. Support Vector Machine (SVM) is one of the best methods to solve this problem. It can deal with small samples, convert nonlinearity flexibly, and deal with high dimensional pattern recognition. This paper first introduces the theory of ant colony algorithm and support vector machine. Secondly, because the selection of parameters based on support vector machine directly affects the classification performance of support vector machine, This paper introduces several methods for parameter optimization of support vector machines, such as grid search, bilinear search, exhaustive, genetic algorithm, particle swarm optimization, etc. Comparing the new ant colony optimization support vector machine with the ant colony optimization support vector machine, we know that the former not only shortens the time of optimizing SVM, but also improves the classification accuracy. Finally, aiming at the large-scale data classification problem, The traditional support vector machine (SVM) shows a lot of shortcomings. Therefore, this paper improves the traditional SVM (nearest neighbor support vector machine), uses lithologic logging data to train the improved support vector machine (SVM) and the traditional support vector machine (SVM). The experimental results show that the nearest neighbor support vector machine has some advantages for large-scale data classification.
【學位授予單位】：東北石油大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P631.81;TP18

【參考文獻】

相關期刊論文 前1條

1 宋延杰;張劍風;閆偉林;何英偉;王德平;;基于支持向量機的復雜巖性測井識別方法[J];大慶石油學院學報;2007年05期

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本文編號：1780324