Study on the parameterization response of probabilistic neural networks for seismic facies classification in the Gulf of Mexico

Abstract

Nowadays, there are many unsupervised and supervised machine learning techniques available for performing seismic facies classification. However, those classification methods either demand high computational costs or do not provide an accurate measure of confidence. Probabilistic neural networks (PNNs) overcome these limitations and have demonstrated their superiority among other algorithms. PNNs have been extensively applied for some prediction tasks, but they have not been well studied regarding the prediction of seismic facies volumes using seismic attributes. We have explored the capability of the PNN algorithm when classifying large- and small-scale seismic facies. In addition, we evaluate the impact of user-chosen parameters on the final classification volumes. After performing seven tests, each with a parameter variation, we assess the impact of the parameter change on the resultant classification volumes. We find that the processing task can have a significant impact on the classification volumes, but we also find how the most geologically complex areas are the most challenging for the algorithm. Moreover, we determine that even if the PNN technique is performing and producing considerably accurate results, it is possible to overcome those limitations and significantly improve the final classification volumes by including the geologic insight provided by the geoscientist. We conclude by proposing a new workflow that can guide future geoscientists interested in applying PNNs, to obtain better seismic facies classification volumes by considering some initial steps and advice.