Seismic facies analysis using generative topographic mapping

Summary Seismic facies analysis is commonly carried out by classifying seismic waveforms based on their shapes in an interval of interest. It is also carried out by using different seismic attributes, reducing the dimensionality of the input data volumes using Kohonen’s self-organizing maps (SOM), and organizing it into clusters on a 2D map. Such methods are computationally fast and inexpensive. However, they have shortcomings in that there is no definite criteria for selection of a search radius and the learning rate, as these are parameters dependent on the input data. In addition, there is no cost function that is defined and optimized and so usually the method is deficient in providing a measure of confidence that could be assigned to the results. Generative topographic mapping (GTM) has been shown to address the shortcomings of the SOM method and suggested as an alternative to it. We demonstrate the application of GTM to a dataset from central Alberta, Canada and show that its performance is more encouraging than the simplistic waveform classification or the SOM multiattribute approach.