Rocky Roden, Carrie Laudon and Jie Qi of Geophysical Insights share how applying machine learning to seismic fault delineation produces optimum fault interpretation.
Over the last few years, there has been an increase in the application of machine learning, a type of artificial intelligence, in the interpretation of seismic data.
The two primary machine learning applications include supervised and unsupervised learning approaches. Supervised learning takes a known set of descriptors or labels to known responses and trains a model, which is applied to new data to get reasonable results. In unsupervised learning, there is no prior knowledge of the data, and training adapts to the data identifying natural patterns and clusters.
This article specifically addresses the application of machine learning to seismic fault delineation.
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