Zach is a undergraduate geophysics major who joined the SDA in late 2019.  He will be joining the SDA research team by continuing the investigation on how machine learning can be used to further improve our understanding of deepwater clinoforms are formed and developed.  This will be achieved by working with various ML methods to try and extract reliable and interpretable sub-seismic resolution details from the data.  We are hoping to develop a method to improve mapping of smaller scale clinothems and lithological variations to aid insight into the depositional systems, and development of these features.  Initial work will be done on the Giant Foreset Formation in New Zealand, with further studies globally.  This work is continuing on the work of undergraduate projects by Roberto Clairmont and Pete Reilly.


Clinoform single attribute analysis by Pete Reilly. (A) Seismic amplitude. (B) Sweetness. (C) Instantaneous Frequency. (D) Cosine of Instantaneous Phase.