Large-scale seismic data recovery by the parallel windowed curvelet transform

We propose using overlapping, tapered windows to process seismic data in parallel. This method consists of numerically tight linear operators and adjoints that are suitable for use in iterative algorithms. This method is also highly scalable and makes parallelprocessing of large seismic data sets feasible. We use this scheme to define the Parallel Windowed Fast Discrete Curvelet Transform (PWFDCT), which we have applied to a seismic data interpolation algorithm. Some preliminary results will be shown. Henryk Modzeleweski: Design and specifications for SLIMPy's software framework The SLIM group is actively developing software for seismic imaging. This talk will give a general overview of the software development philosophy adopted by SLIM. The covered topics will include: 1) adopting Python for object-oriented programming, 2) including parallelism into the algorithms used in seismic imaging/modeling, 3) in-house algorithms for seismic imaging, and 4) contributions to Madagascar (RSF). The talk will serve as an introduction to the other presentations in the session "SINBAD Software releases".