In the oil and gas industry, there is always an interest in investigating potential oil and gas reservoirs. There are several ways in which to test for this, and one of these is seismic data collection. Seismic data is gathered by recording seismic waves (waves of force that travel through the earth). This data is used in the field of petroleum to discover the geological structures of the earth and find natural resources such as oil and gas. To help in this search seismic data is processed by many filters and filtering methods to get a clearer subsurface image and to view more relevant information such as faults and reservoirs, see FIgure \ref{seis} for an example of seismic data. These filters are like other image filtering processes very adaptable to the graphical processing unit (GPU), but are per today run on the central processing unit (CPU).

\begin{figure}[H]
	\centering
	\includegraphics[width=10cm]{report/intro/seismic.PNG}
	\caption{Figure illustrating seismic data from \cite{victor}, with permission from Schlumberger}
	\label{seis}
\end{figure}

In recent years, it has been shown that the performance capabilities of the GPU, in some cases, has exceeded that of the CPU. Which in turn motivated the development of the general purpose graphical processing unit (GPGPU). This has lead to the use of the GPU not only in graphic applications, but also in scientific calculations. These trends have created a boom in the graphical processing architectures and manufacturers have started introducing new product lines specific for scientific calculations. In Figure \ref{cpugpu} one can see the trend of computational power measured in floating point operations pr. second (FLOPS) for the past 5 years. Another aspect worth noting is that the use of the GPU gives room to use the CPU for other tasks in parallel, functioning as an accelerator.

Given these advancements one is now often interested to see if it is possible to utilize the GPU for calculations and gain some increased performance for certain tasks. Such tasks as image processing, seismic processing and other physical modeling as well as linear programming applications have proven to be well parallelizable on the GPU. This gives the foundation of this projects existence in that we are to perform an image enhancement task on seismic data on the GPU.

\begin{figure}[H]
	\centering
	\includegraphics[width=10cm]{report/intro/cpu_gpu_graph.PNG}
	\caption{Figure illustrating CPU and GPU performance trends from \cite{nvidia_cuda_prog_guide}, with permission from NVIDIA}
	\label{cpugpu}
\end{figure}
