January 13, 2022 Reminder “Luncheon” Webinar – Marianne Rauch

How AI derived sonic logs can improve the PSDM model building

Marianne Rauch*, Mike Perz, TGS

Abstract:

Machine learning applications have started to infiltrate the geosciences especially in the fields of log estimations, interpretation of salt bodies and fault definitions, seismic processing such as automated first break picking and velocity analysis and pre-stack inversions for rock property estimations. Our industry is known for big datasets that are very suitable for this task.

We are showing an onshore example of how we utilize sonic logs that have been estimated through a gradient boosted trees machine learning process in our PSDM velocity modeling building and how we are benefitting from these additional data points.

Marianne received her PhD in Physics in 1985 from Uni Graz in Austria. She started her oil career as research assistant at Curtin University in Perth, Australia more than30 years ago and has been active in geophysics ever since then. Marianne lived in many places and worked on-shore and off-shore basins all over the world. Her main specialties are DHI, seismic processing, depth migration, potential fields and researching new technologies, methodologies. She likes to do applied research, mentor and teach and is a seasoned presenter at conventions and workshop. In 2020 she received the Special Commendation Award from the SEG. She has published a good number of articles on several subjects and still is passionate about geoscience.

Marianne is very active in the geoscience community. Currently, she serves as the Chair of the SEG grav/mag group, is the 1^st VP of the GSH and is part of the technical committee of the URTeC 2022. She reviews abstracts and articles for various conventions and publications.

She is the Principal Technical Advisor, TGS, Houston.