Skip to main content
Earth Science Week
Maps in Schools Project
Geoscience Center & Museum
Nugget Book Purchase
Tutorial Nuggets Purchase
Geoscience Center and Museum
Honorary and Life Members
Outstanding GSH Volunteer
Special Interest Groups
Data Proc. & Acquisition
Share this page
Share on Facebook
Share on Twitter
Share on LinkedIn
Data Proc & Acqui SIG: Seismic Imaging and Multiple Removal via Model Order Reduction* - Jan 15th
Sponsored by Schlumberger
10001 Richmond Avenue
Houston TX 77042 USA
You Must be Logged in to Register
Speaker: Prof. Alexander Mamonov, University of Houston
We introduce a novel framework for imaging and removal of multiples from seismic data based on model order reduction. The reduced order model (ROM) is an orthogonal projection of the wave equation propagator (Green's function) on the subspace of discretely sampled time domain wavefield snapshots. Even though neither the propagator nor the wavefields are known in the bulk, the projection can be computed just from the knowledge of the seismic data using the block Cholesky factorization. Once the ROM is found, its use is twofold.
First, the projected propagator can be backprojected to obtain an image. ROM computation implicitly orthogonalizes the wavefield snapshots. This highly nonlinear procedure differentiates our approach from the conventional linear migration methods (Kirchhoff, RTM). It allows to resolve the reflectors independently of the knowledge of the kinematics and to untangle the nonlinear interactions between the reflectors. As a consequence, the resulting images are almost completely free from the multiple reflection artifacts.
Second, the ROM computed from the original seismic data can be used to generate the Born data set, i.e. the data that the measurements would produce if the propagation of waves in the unknown medium obeyed Born approximation instead of the full wave equation. Obviously, such data only contains primary reflections and the multiples are removed. Consecutively, existing linear imaging and inversion techniques can be applied to Born data to obtain reconstructions in a direct, non-iterative manner.
Speaker Biography: Prof. Alexander Mamonov, University of Houston
Prof. Mamonov received his formal education with an undergraduate degree in Mechanics at Lomonosov Moscow State University in 2003 and a Ph.D. in Computational and Applied Mathematics at Rice University in 2010.
His career spans both the oil industry and the academia. As a graduate student he completed four internships with Schlumberger at SDR and AbTC centers working on optimal grid coarsening for reservoir optimization. He completed two postdoctoral fellowships at the Mathematical Sciences Research Institute at Berkeley, CA and at the University of Texas at Austin in 2010-2013. In 2013-2014 he joined Schlumberger as a full time research scientist, first in Moscow and later at the FWI group at WesternGeco in Houston, TX. While at Schlumberger he began his work on using data-driven model order reduction for inversion and imaging with seismic waves.
Currently Prof. Mamonov continues his research at the Department of Mathematics at the University of Houston where he holds a position of an Assistant Professor since 2015.
4:30 PM – sign-in, social time
5:00 PM – start of presentation
6:00 PM - close of meeting
THANK YOU TO OUR GENEROUS SPONSOR:
1/15/2018 4:30 PM - 6:00 PM
Keep me signed in
I don't know my
Create a new account