Mapping shallow hazards, such as gas hydrates, is a critical stage for any subsurface characterization workflow. This is especially challenging in 2D seismic datasets where the data is significantly more limited. However, we have seen significant success when applying recent deep learning approaches to tedious seismic interpretation tasks. Geoscientists can now leverage these powerful workflows to characterize shallow hazards faster and more accurately, such as within the Pegasus Basin, offshore New Zealand. Julian Chenin, Geophysical Data Scientist at Bluware, walks through this case study and shares the results.
Bluverse Case Study: Mapping 2D Gas Hydrates Using Interactive Deep Learning
