基于量子蒙特卡罗的地震多属性聚类方法

The seismic multi-attribute clustering method based on quantum Monte Carlo method

多属性聚类分析是从众多地震属性中提取地下地质特征的重要途径。为了更好地提高属性聚类分析的有效性,本文将寻优能力更强的量子蒙特卡罗方法引入到聚类分析中,它能自动根据数据结构的特点,动态调整数据固有的类别数;通过在聚类分析过程中引入相关性分析,估算各个属性的权重大小,提出根据权重大小进行加权的方法,突出对地质特征敏感的地震属性的作用;并提出变尺度的方法,挖掘属性之间共有的、宏观的特征之外的细节部分,减少属性交叉信息的影响。实例应用结果表明,本文提出的方法能很好地挖掘数据的内在特征,提高储层预测的准确性。

Multi-attributes clustering is an important way for drawing underground geologic features from a large number of seismic attributes.For improving the effectiveness of the attributes clustering,this paper introduces quantum Monte Carlo method with stronger optimization ability into the clustering to adjust the inherent category number of data dynamically according to the characteristics of the data structure.By increasing the correlation analysis in the process of clustering for estimating each attribute weight,seismic attributes which are sensitive to geological features can be highlighted.The proposed variable scale method can show details together with macro common attribute characteristics to reduce the influence of attribute cross information.Examples show that the method proposed in this paper could be very good at mining the inherent characteristics of data to improve reservoir prediction accuracy.