Different training sample selection strategies in unsupervised seismic facies analysis

Abstract

Pattern recognition based multiattribute seismic facies analysis enables seismic interpreters to effectively extract and analyze information buried in several seismic attributes. However, most pattern recognition methods rely heavily on training data, which means the algorithms detect features that best represent the training data. In this study, using selforganizing map as an example of pattern recognition techniques, we discuss the influence (and sometimes, bias) associated with different training data selection strategies. We further demonstrate that using the same attributes, different training samples may lead to different interpretation of seismic facies.