We present a new automated workflow based on machine learning which can significantly reduce the amount of manual interpretation of the top salt boundary. Manual interpretation of top salt on large seismic surveys with complex salt geometry is a time-consuming task. The interpreters typically need to scan through the seismic volume and pick control points line-by-line. It can take more than a month to complete a top salt interpretation. In this new method, a convolutional neural net is designed to detect the top of salt boundaries and the training data are picked as 2D images on a manual top salt interpretation in a specific seismic survey. The trained network is then evaluated both on the seismic data used in the training and on another seismic data not used in the training. In both cases we can produce a top salt interpretation that covers the main parts of the corresponding manual interpretations. The results can be further improved by adding more training data. This new automated workflow has the potential to reduce the interpretation turnaround time of top of salt from approximately a month or more and down to hours.


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