【摘要】 岩土作为一种极其复杂的材料,通常会受到外界多种因素的影响而发生变化,这些影响因素既包括断层、裂隙、雨水冲刷和腐蚀等天然环境因素,也包括众多的人为因素,从而导致测井岩性的识别会产生大量的干扰数据。在对大数据量的信息寻优处理的算法中,支持向量机(Support Vector Machine,SVM)是一种受到广泛关注的寻优方法。但是传统的SVM寻优方法存在耗时长的缺陷,因此将传统SVM寻优当中的留一交叉法改为K折交叉法,并利用这种优化的SVM对测井岩性数据进行寻优处理,来进行测井岩性的识别。对比试验结果表明,相对于传统支持向量机的寻优算法,该方法具有识别正确率高、收敛速度快等优点。更多还原

【Abstract】 Rock-soil is a kind of extremely complex material,and it will encounter a lot of influence,these factors include the slip,fracture,rain erosion,corrosion and other natural factors and artificial factors.So many impacts result in the identification of lithology will produce a large number of error data.In the large amounts of data information optimization processing algorithm,support vector machine(Support Vector Machine,SVM)is a widely concerned optimization method.But the traditional SVM method is time-consuming,so we optimized the traditional SVM,made the leave one out cross method change to K fold cross method,and used optimized SVM optimize large data searching.Comparative test results show that the method has the advantages of high recognition accuracy,quick convergence speed,compared with the traditional optimization algorithm of support vector machine.更多还原