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Visualization Analysis of Seismic Facies Based on Deep Embedded SOM

As a classical visualization tool for seismic facies analysis, the clustering process of self-organizing map (SOM) is generally divided into two stages: feature extraction and clustering. However, when the horizon pickings are ambiguous and waveforms are chaotic, the structural feature extraction cannot correspond well to the topological structure of SOM. To improve the performance of classification, we propose a one-stage method of deep embedded SOM (DESOM) for seismic facies visualization analysis, which means that the extracting feature representation and clustering are completed simultaneously. Moreover, the stability of the DESOM results can be improved by adding sparse constraints, and thus the visualization results can be displayed in more detail. In the experiment, by comparing with the external indexes of clustering and the hierarchical results of prototype categories, the superiorities of the DESOM and sparse DESOM (SDESOM) methods are verified based on a geophysical model. In the field data application, these methods are combined with the Hue, Saturation, Value (HSV) color mapping technology to display the geological structure information of the target horizon. According to the law of the correlation of facies and the internal cluster indexes, it approves that the DESOM method can improve the continuity of channels, and the SDESOM method can obtain more detailed information of seismic facies distribution.