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Speaker: Mike Perz, TGS

The worldwide explosion in popularity of machine learning (ML) across diverse sectors has profound and positive implications for our own geoscience field, a space which consumes vast amounts of data whose value often fails to be fully leveraged in traditional workflows. In this talk we will explore the question of whether ML-predicted sonic data can be used to improve prestack depth migration (PSDM) velocity model building. 

Speaker Biography: Mike Perz, TGS
Mike Perz holds a BSc in Physics (University of Toronto) and an MSc in Geophysics (University of British Columbia). Mike has over 25 years of industry experience in the seismic technology space. While historically his research interests have centered on land processing, over the last few years he has extended his technical reach to include multidisciplinary topics which are more explicitly tied to the question of how can seismic better deliver value in unconventionals.  Such topics include onshore pore-pressure prediction, the use of 3D seismic to constrain geomechanical modeling, and the use of artificial intelligence to inform PSDM model building.