Machine Learning

Overall, machine learning has introduced us to a whole different world that has taken geoscientists by surprise. However, the real question is how much can we trust the machine? How accurate can it be? And the most intriguing question of all – can machine learning replace the interpreter? We will analyze three machine learning processes to assess the pros and cons of utilization of convolutional neural networks for fault prediction versus interpretations made by the user in a highly complex polygonal fault section of a 3-D seismic reflection dataset.

“Machine learning” has become a common phrase in geophysics. These methods, based on complex algorithms and statistics, allow geoscientists to speed up and improve their interpretations. However, as interpreters, we can feel intimidated and concerned about how much of our expertise can be replaced by machine learning algorithms. To better understand the limitations, we assess the importance of human validation and participation in one machine learning process, highlighting the upsides and downsides of a machine-derived process versus a geoscientist-guided selection of attributes. As Earth scientists, we explored a suite of seismic attributes and selected those that were meaningful for interpreting a deepwater channel system and compared our results with the attributes derived from principal component analysis.