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Welcome to the 2021 GSH Spring Symposium! This is the Geophysical Society of Houston’s premier technical event and this year the symposium theme will be on the application of the latest data science tools to solving geophysical problems.

Due to the current COVID-19 situation, we have elected to hold this year’s symposium as an online only event. This unfortunately means we can’t all meet in person and that will not be able to hold our traditional honoree roast event, but we hope to do both of these at a special fall event this year.  However, we still hope we can make our spring symposium as informative and interactive as previous years and we are preparing a great line up of speakers. 

Symposium Proceedings Booklet CLICK HERE

The symposium live presentations were held over two days, April 27 and 28, 2021, and included a keynote address, multiple speakers each day and two exciting lunch time events. The first will be our traditional SEG Student Challenge Bowl competition. This will be held online and will feature the traditional battle between student geophysicists to be crowned the winner of the GSH Challenge Bowl and to earn a place at the Challenge Bowl finals at the SEG annual symposium in the fall! The second event will be the announcement of the winners of the Machine Learning Competition being run by the Data Science & Machine Learning SIG and will include an overview of the competition challenge, the announcement of the winner and a presentation by the winning entry.

Key Note Speaker: Jake Umbriaco, Digital Platform and Services Manager, Subsurface - Chevron
"The Future of Petroleum Geoscience in a Digital World"

Confirmed Speakers and Topics include:

• Aria Abubaker, Schlumberger - Deep Learning Applications for Automating Evergreen Subsurface Model Building
• Satinder Chopra, SamiGeo - Some Machine Learning Applications for Seismic Facies Classification
• Hugo Garcia, Geoteric - Automated Fault Detection from 3D Seismic using Artificial Intelligence
• Elive Menyoli, Emerson - Wavefield Separation via Principle Component Analysis and Deep learning in the Local Angle Domain
• Tom Smith, Geophysical Insights - The Scientific Universe from Square One to SOM
• Yuting Duan, Shell - Estimation of Time-lapse Timeshifts using Machine Learning
• Jon Downton, CGG - Predicting Reservoir Properties from Seismic and Well Data using Deep Neural Networks
• Wenyi Hu, AGT & UH - Progressive Transfer Learning for Low Frequency Prediction in FWI
  
  

Symposium Proceedings Booklet CLICK HERE

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