Machine Learning and Seismic Structural Attributes Hybrid Approach to Map Complex Fault System

This paper present a hybrid Machine Learning and seismic structural attributes approach to extract detailed major and minor discontinuities from seismic data from a complex fault system that present a challenge using conventional interpretation techniques. Fault interpretation is image segmentation problem and we thus adopted U-net encoder-decoder architecture as a first step in this hybrid workflow, it is well suited for seismic discontinuities. Image segmentation can not only figure out whether a particular feature such as faults exist but can also create a mask showing where in the volume those features exist.