Mar 7th-Data Proc. & Acquisn. SIG: Sparse Radon Transform in Extended Domain*
Sponsored by Schlumberger
Event Location:
Schlumberger
Q Auditorium
10001 Richmond Avenue
Houston TX 77042 USA
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Speaker: Madhav Vyas, BP
Co-Authors: John Etgen, Qingqing Liao
The problem of signal and noise separation has been worked on for the last few decades and it still continues to remain very relevant. One way to distinguish signal from noise is on the basis of curvature. Radon transforms enable us to do that by mapping the input data as a function of moveout. In order for the process of signal and noise separation to be effective we require high resolution in the Radon domain. Although we have access to methods that can increase the resolution by imposing sparsity constraints; these methods seldom preserve the amplitude nuances present in real data. In this abstract we propose an algorithm that can deliver sparse high resolution Radon domain representation without compromising the amplitude information. We also share a field data example to demonstrate the effectiveness of the process.
Speaker Biography: Madhav Vyas, BP
Madhav Vyas received his BS (2003) and MS (2005) degree in Exploration Geophysics from Indian Institute of Technology, Kharagpur, INDIA. He then pursued an MS degree (2007) from Stanford University. During his stay at Stanford he was advised by Biondo Biondi and was part of the Stanford Exploration Project (SEP). Madhav then worked as a Research Scientist with WesternGeco, Schlumberger from 2007 to 2011. Since 2011 he is employed with BP as a Research Geophysicist in the Complex Imaging team. He has worked on different topics including multiple elimination, wave-equation tomography, wave-equation based angle gathers, optimized stacking and signal enhancement. His research interests include signal processing algorithms, inversion, imaging codes and velocity model building.
Time Schedule:
4:30 PM – sign-in, social time
5:00 PM – start of presentation
6:00 PM - close of meeting
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