Maximum correntropy self-organizing map for waveform classification

Abstract

The self-organizing map (SOM) is a well-known unsupervised classification method for seismic facies analysis. The conventional SOM networks and their mutation methods are mostly based on the Euclidean distance measurement, which estimates the global similarity between the input data and weights of neuron. Such measurement is suitable for signals with a Gaussian distribution but may not work well for seismic data for which the dominant structural features (e.g., stratigraphic layers, faults, channels, and salt bodies) typically appear some specially distributed patterns. Therefore, we propose to modify the SOM by introducing a new distance measurement criterion based on the maximum correntropy. The correntropy is mainly used to measure the local similarity between variables, especially for non-Gaussian signals, and therefore is more suitable than the Euclidean distance to adaptively characterize the seismic structural similarities for seismic classification tasks. Validations on two synthetic data sets indicate that the proposed method yields more accurate classification results and is more robust to noise than the traditional SOM algorithm. Comparison experiments on a field data example also demonstrate that the proposed method can delineate the distribution of seismic facies more effectively and stably than the traditional one.