A deep-learning method for latent space analysis of multiple seismic attributes

Abstract

Seismic attributes are a well-established method for highlighting subtle features in seismic data to improve interpretability and suitability for quantitative analysis. Seismic attributes are an enabling technology in such areas as thin-bed analysis, geobody extraction, and seismic geomorphology. Seismic attributes are mathematical functions of the data that are designed to exploit geologic and/or geophysical principles to provide meaningful information about underlying processes. Seismic attributes often suffer from an “abundance of riches” because the high dimensionality of seismic attributes may cause great difficulty in accomplishing even simple tasks. Spectral decomposition, for instance, typically produces tens and sometimes hundreds of attributes. However, when it comes to visualization, for instance, we are limited to visualizing three or at most four attributes simultaneously. We have developed a deep-learning-based approach to latent space analysis. This method is superior to other methods in that it focuses upon capturing essential information rather than just focusing upon probability density functions or clusters. Our method provides a quantitative way to assess the fit of the latent space to the original data. We apply our method to a data set from Canterbury Basin, New Zealand. This data set contains a turbidite system, and it has been the subject of several other papers. We examine the goodness of fit of our model by comparing the input data to what can be reproduced, and we provide an interpretation based upon our method.