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12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'04)
Accelerating Seismic Migration Using FPGA-Based Coprocessor Platform
Napa, California
April 20-April 23
ISBN: 0-7695-2230-0
Chuan He, Texas A&M University, College Station, TX
Mi Lu, Texas A&M University, College Station, TX
Chuanwen Sun, Texas A&M University, College Station, TX
Migration is the most important seismic data processing method that recovers subsurface images of the Earth's interior using surface-recorded data volumes obtained from seismic reflection surveys. A reconfigurable coprocessor platform called SPACE (Seismic data Processing Accelerator with reConfigurable Engine) using Field Programmable Gate Array (FPGA) technology is proposed in this paper to speed up these computationally demanding and data-intensive seismic migration applications. The proposed SPACE platform is characterized by its simple architecture and abundant on-board memory resources along with ultra-wide memory bandwidth, which also makes the platform suitable for other seismic data processing methods or some large-scale scientific computing applications. The time-consuming kernel part of the Pre-Stack Kirchhoff Time Migration (PSTM) algorithm is programmed into the FPGA-based coprocessor platform, which acts as a hardware accelerator attached to an Intel-based workstation through the local Peripheral Controller Interface (PCI) bus. Improved performance can be achieved by integrating a number of parallel running fully pipelined arithmetic modules into a single FPGA chip. Our simulation results show that the proposed coprocessor platform operating at a conservative speed of 50 MHz can calculate the Kirchhoff summations for 50 million points per second, which is about 15.6 times faster than a referential 2.4 GHz Pentium 4 workstation. The impressive performance of the proposed platform implies its broad applications in seismic data processing industry.
Citation:
Chuan He, Mi Lu, Chuanwen Sun, "Accelerating Seismic Migration Using FPGA-Based Coprocessor Platform," fccm, pp.207-216, 12th Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM'04), 2004
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