A Principled Benchmark for Seismic Data Segmentation

In recent years, several deep learning techniques have been applied to the problem of seismic facies segmentation. However, there is a lack of an authoritative protocol for evaluating such models, so that the comparison between results becomes compromised. This article proposes a principled benchmark for lithofacies segmentation based on the public seismic volumes from the F3 Netherlands, Penobscot, and Parihaka datasets, along with standard metrics for performance assessment. The utility of the benchmark is illustrated by the evaluation of the U-Net DeconvNet and SegNet encoder-decoder architectures. The goal is to offer a framework which will enable researchers to compare different methods and to develop more effective strategies for the segmentation problem.