A Hierarchical Clustering Method of SOM Based on DTW Distance for Variable-Length Seismic Waveform

Abstract

In seismic facies analysis, waveform clustering of self-organizing map (SOM) usually classifies waveforms truncated by horizon with a fixed time window. However, the fixed time window is not an appropriate method for the situation in which the thickness of the target layer varies in the horizontal direction. In order to adapt SOM to this situation and simultaneously preserve the advantages of SOM, we replace the vanilla SOM measure with dynamic time warp (DTW) distance. Compared with Euclidean distance, DTW distance requires more computational amount about path searching and weight parameter updating in SOM. Therefore, considering the redundancy of seismic data, we introduce a hierarchical clustering strategy, which uses cluster-based stratified sampling and hierarchical mapping to reduce the computational cost of training and prediction. In the visualization section, by combining with hue, saturation, value (HSV) coloring and hierarchical mapping, the method can quickly display the lateral distribution of stratigraphic. The proposed method is validated by a mound shape model in a simulation test. Finally, the method was successfully applied to the field data from a reef-bank reservoir. The experimental and application results show that, when the top and bottom interfaces of the destination layer can be clearly identified, the proposed method can effectively cluster the waveforms with variable length, and the computational cost is acceptable.