Seismic Facies Analysis Based on Spectral Clustering with Waveform Characteristic Vector
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摘要: 本文提出了一种基于地震沉积学原理沿层提取地震波形特征向量，并以谱聚类（spectral clustering）分析进行地震相划分的方法。谱聚类能够处理非线性的数据结构和高维数据的聚类问题，但其相似度矩阵的构建和谱分解的计算较为复杂，需要较高的计算资源和时间成本。为了提高谱聚类算法的效率和可扩展性，本文提出了将Mini-batch K-means算法与谱聚类算法结合起来的MKSC算法，在提高谱聚类算法精度的同时大大降低谱聚类空间的复杂度。经过对数值模拟、地球物理模型数据和实际地震资料的处理分析，证明该方法在沉积相划分、沉积相特征识别方面的效果明显，是一种具有良好应用前景的新型沉积特征分析工具。Abstract: Based on the principle of seismic sedimentology, the feature vectors of seismic waveforms are extracted along stratum slices, and spectral clustering analysis is introduced to classify seismic facies. Spectral clustering is an unsupervised machine learning algorithm. Its essence is to simplify the expression of high-dimensional seismic data in the form of feature vectors, which belongs to the process of dimensionality reduction. Considering the traces with specific time windows in the seismic work area as nodes of the graph and the similarity between traces as the weight of the edges, a graph model can be constructed. Spectral clustering must determine the best segmentation method to complete the segmentation of the graph, so that different types of sedimentary characteristics can be distinguished. Physical model and actual data processing and analysis demonstrate that this method is capable of dividing sedimentary facies characteristics and is a new kind of facies analysis tool for reservoir classification, which has good application prospects.
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Key words:
- machine learning /
- spectral clustering /
- waveform clustering /
- seismic facing /
- seismic sedimentology
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图 1 无向图及其邻接矩阵特征向量
Figure 1. Feature vectors of the undirected graph and its adjacency matrix
图 2 相同数据集不同聚类方式分类结果
Figure 2. Classification results of the same dataset by different clustering methods
图 3 物理模型的采集尺寸及参数示意图
Figure 3. Schematic of the acquisition size and parameters of the physical model
图 6 物理模型偏移剖面及基于地震沉积学原理解释的层位
Figure 6. Migration section of the physical model and horizon interpretation based on the seismic sedimentology principle
图 8 物理模型第2至第5层砂、泥岩空间展布及形态
Figure 8. Spatial distribution and morphology of sand and mudstone in the second to fifth layers of the physical model
图 9 为各层的地层切片均方根振幅地震相
Figure 9. Slices of root mean square amplitude seismic phase of stratums
图 10 各层MKSC 聚类地震相分布
Figure 10. MKSC clustering seismic facies distribution of stratums
图 11 目标区基于地震沉积学原理解释的层位
Figure 11. Horizon interpretation based on seismic sedimentological principles in the target area
图 14 分流河道中心部位不同水动力条件下的相变示意图
Figure 14. Schematic diagram of phase transition in the central part of distributary channel under different hydrodynamic conditions
图 16 H3 c段MKSC聚类属性平面图和沉积相平面图
Figure 16. MKSC clustering attribute plan and sedimentary facies plan of H3 c
图 17 H3 b段MKSC聚类属性平面图和沉积相平面图
Figure 17. MKSC clustering attribute plan and sedimentary facies plan of H3 b
表 1 不同聚类分析算法计算效率比较
Table 1. Comparison of the computational efficiency of different clustering analysis algorithms
算法类型 Mini-batch K-means Spectral clustering MKSC 计算时间/s 0.17 0.21 0.17+0.09 
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