The schematic on the left shows an interpretation applied to an inline within the Poseidon seismic volume. The colored image demonstrates corresponding interpretation made by the automated system some distance from this initial interpretation. There is a high degree of consistency in what can be seen to be similar features.
In the second example, the LANDMASS-1 dataset was used. Example seismic facies were used to train the machine learning algorithm, seen in the tiles on the right, top to bottom; salt, chaotic, fault and horizon. The colored image is the automated interpretation of the F3 seismic data volume. The ability to deliver decision-ready quality image data with minimal expert time provides confidence in the potential of this new technique.
Basic stratigraphic units within seismic volumes are defined by careful analysis by a skilled sequence stratigrapher. These patterns may be defined objectively and are considered to be predictable.1 Partnering with a major independent, Enthought has conducted R&D into the efficacy of using machine learning techniques and custom processing workflows to automatically delineate these patterns throughout a seismic volume.
This initial seismic interpretation is time consuming for an expert, and requires rigorous attention to detail.
Machine learning and AI can alleviate the drudgery of interpreting large seismic volumes and allow more time for experts to focus on quality and value. Our approach is designed to operate where input from the interpreter is minimal. Where possible, we limit the complexity of machine learning systems, and apply standard seismic processing methods familiar to the interpreter, while taking significantly less time.
This approach is shown in the following two examples:
1Seismic Stratigraphy and Global Changes of Sea Level, Part 6: Stratigraphic Interpretation of Seismic Reflection Patterns in Depositional Sequences, R. M. Mitchum et al, AAPG Special Volumes, 1977