Nov 8th-Geoscience Computing SIG: Deep Learning Platform for Seismic Data Analytics*
We have revamped our Geoscience Computing SIG!
Now meeting every month during lunch.
Sponsored by Unique Digital, Inc.
Meeting Location:
UDI Houston Banquet Room
10595 Westoffice Dr.
Houston, TX 77042
NOTE: You Must Be Logged In to Register
Registration is Free - A Lunch will be served.
Speaker: Dr. Lei Huang, Asst. Professor, Prairie View A&M
The Cloud Computing Research Lab at Prairie View A&M University has been working on developing a scalable cloud platform for seismic data analytics to facilitate a variety of interpretation scenarios. The recognition of 3D complex geological features in noisy seismic datasets is a daunting challenge in pattern recognition. Early experience shows that traditional machine learning techniques such as Support Vector Machines (SVMs) and Logistic Regression may be insufficient for this task. We will present our experience of building a deep learning based seismic data analytics platform on top of Apache Spark and Google TensorFlow, and demonstrate the early results of applying Convolutional Neural Networks (CNN) on a geological fault detection case study. Moreover, we will also present the collaborative work with FEI on applying remote visualization technology to large seismic datasets that are distributed in the Spark environment. The work is sponsored by National Science Foundation.
Speaker Biography: Dr. Lei Huang, Asst. Professor, Prairie View A&M
Dr. Lei Huang is an Assistant Professor at Prairie View A&M University (PVAMU), where he is leading research at the Cloud Computing Research Lab. He also serves as Associate Director of Research in the Center of Excellence in Research and Education for Big Military Data Intelligence at PVAMU. He currently has several active research projects sponsored by NSF and DOD in Big Data Analytics, Cloud Computing and High Performance Computing areas. He joined the university in 2011 having previous experience in HPC research at the University of Houston. Huang has a Ph.D. from the Computer Science department at University of Houston in 2006.
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