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Thursday, March 16 • 4:00pm - 5:30pm
Poster: Applying Deep Learning on a GPU-based Cluster for Seismic Volume Interpretation with Spark and TensorFlow

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In bringing data analytics technology to bear on the problem of computer-assisted interpretation of seismic volumes, the Seismic Analytics Cloud project conducted at Prairie View A&M University has employed the deep learning models in seismic interpretation. In our initial use cases, we train our deep learning models for geological fault identification using labeled fault and non-fault regions in both synthetic and field-recorded data volumes. The computational demands of techniques such as Convolutional Neural Networks (CNN) extend to multiple days on our small-sized clusters for even modest-sized volume analytics problems. This excessive turnaround is mitigated through the use of GPU-based systems using Apache Spark and Google TensorFlow deep learning software. We will show our performance improvement in applying deep learning models on GPU-based clusters. Moreover, we will present how we speed up the Apache Spark performance by bringing two parallel programming models Spark and OpenMP together to perform the deep learning based solutions for seismic interpretation.

We acknowledge the support of the National Science Foundation for this project through a number of research and innovation grant programs.


Lei Huang

Assistant Professor, Prairie View A&M University

Thursday March 16, 2017 4:00pm - 5:30pm
Exhibit Hall BRC

Attendees (2)