基于局部线性嵌入的属性降维技术及应用研究

利用分偏移距及分方位地震属性进行断裂、河道等复杂地质异常体检测存在属性信息挖掘不足、综合成果展示单一等问题。分析了经典非降维算法局部线性嵌入（LLE）的基本原理和技术特点, 开展了面向叠前地震属性的LLE降维技术研究并给出了相应的技术流程。通过对道集进行部分叠加和属性计算, 形成一个高维空间的部分叠加属性数据体, 利用LLE数据降维算法将高维空间属性数据体进行降维表示, 得到了三维空间的叠前属性降维数据体。与传统的线性降维算法主成分分析（PCA）相比, 典型数据测试证明了LLE非线性降维技术具有降维效果好、数据保真度高的特点。实际工区数据的应用结果表明: 基于LLE非线性降维技术可以实现对叠前地震属性的降维和融合, 通过充分挖掘偏移距属性和方位属性的有效信息, 实现了断裂-裂缝、隐蔽河道等的准确识别和表征。

Offset and azimuth attributes have obvious advantages in detecting complex geological anomalies such as faults and channels. To improve information mining and result presentation, we carry out the study of Locally Linear Embedding(LLE)-based dimensionality reduction and technical process for prestack seismic attributes based on the fundamentals and technical features of the classic non-linear dimensionality reduction algorithm, LLE. Using the LLE algorithm, prestack seismic attributes in a high-dimension space extracted from partial stacks will be mapped into a dimension-reduction attribute volume in 3D space. As per a case study in Bakken field, the LLE non-linear technique is superior to Principal Component Analysis (PCA), a linear algorithm, in dimensionality reduction and feature preservation. Another practical application shows that the LLE technique can realize dimensionality reduction and fusion of prestack seismic attributes for accurate identification and characterization of geological anomalies.