  The primary use of technical computing in the oil and gas industries is for
seismic imaging of the earth's subsurface, driven by the business need for
making well-informed drilling decisions during petroleum exploration and
production. Since each oil/gas well in exploration areas costs several tens of
millions of dollars, producing high-quality seismic images in a reasonable time
can significantly reduce the risk of drilling a "dry hole". Similarly, these
images are important as they can improve the position of wells in a
billion-dollar producing oil field. However seismic imaging is very data- and
compute-intensive which needs to process terabytes of data and require
Gflop-years of computation (using "flop" to mean floating point operation per
second). Due to the data/computing intensive nature of seismic imaging,
parallel computing are used to process data to reduce the time compilation.
  With introducing of Cloud computing, MapReduce programming model has been
attracted a lot of attention in parallel and distributed systems [1, 2] to
execute massive processing algorithms such as Bioinformatics[3], Astronomy[4],
Geology[5] and so on. In this report, we will investigate and discuss current
approaches to fit seismic algorithms to MapReduce programming model.
