Performance Evaluation Model based on Precision Reduction and FPGAs Applied to Seismic Modeling

Summary form only given. The recent increase in computing power of FPGAs has allowed its use in areas such as seismic data processing. Additionally, besides the capability of performing computations in parallel way, FPGAs also support application-specific number representations. In this type of application, in order to achieve better performance, instead of using the floating-point standard, usually the processing and storage of data is done using the fixed point standard. However, the change of representation can cause a degradation in the quality of the results. In the petroleum industry, a seismic image of poor quality can represent an erroneous interpretation of the subsurface, resulting in catastrophic losses. For this reason, it is essential that the quality of data obtained from the seismic data processing for low precision can be evaluated within reliable technical criteria. In this paper, a real case study was used in order to evaluate the efficiency of two different metrics applied to this seismic application based on RTM algorithm. The main strategy is to explore the precision reduction in terms of SNR (Signal-to-Noise Ratio) and UIQI (Universal Image Quality Index) metrics, in order to improve the performance of the system. Results show a performance gain of 50% compared with the architecture implemented in hardware using floating point standart IEE754.