Real-time seismic-image interpretation via deconvolutional neural network

Seismic interpretation is now serving as a fundamental tool for depicting subsurface geology and assisting activities in various domains, such as environmental engineering and petroleum exploration. In the past decades, a number of computer-aided tools have been developed for speeding the interpretation process and improving the interpretation accuracy. However, most of the existing interpretation techniques are designed for interpreting a certain seismic feature (e.g., faults and salt domes) in a seismic section or volume at a time; correspondingly, the rest features would be ignored. Full-feature interpretation becomes feasible with the aid of multiple classification techniques. When implemented into the seismic domain, however, the major drawback is the low efficiency particularly for a large dataset, since the classification need to be repeated at every seismic sample. To resolve such limitation, this study proposes implementing the deconvolutional neural network (DCNN) for the purpose of real-time seismic interpretation, so that all the important features in a seismic image can be identified and interpreted both accurately and simultaneously. The performance of the new DCNN tool is verified through application of segmenting the F3 seismic dataset into nine major features, including salt domes, strong reflections, steep dips, etc. Good match is observed between the results and the original seismic signals, indicating not only the capability of the proposed DCNN network in seismic image analysis but also its great potentials for realtime seismic feature interpretation of an entire volume.