Parallel computation of optimized arrays for 2D electrical imaging surveys
Loke, M.H.; Wilkinson, P.B.; Chambers, J.E.. 2010 Parallel computation of optimized arrays for 2D electrical imaging surveys. Geophysical Journal International, 183 (3). 13021315. 10.1111/j.1365246X.2010.04796.x
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Abstract/Summary
Modern automatic multielectrode survey instruments have made it possible to use nontraditional arrays to maximize the subsurface resolution from electrical imaging surveys. Previous studies have shown that one of the best methods for generating optimized arrays is to select the set of array configurations that maximizes the model resolution for a homogeneous earth model. The Sherman–Morrison Rank1 update is used to calculate the change in the model resolution when a new array is added to a selected set of array configurations. This method had the disadvantage that it required several hours of computer time even for short 2D survey lines. The algorithm was modified to calculate the change in the model resolution rather than the entire resolution matrix. This reduces the computer time and memory required as well as the computational roundoff errors. The matrix–vector multiplications for a single addon array were replaced with matrix–matrix multiplications for 28 addon arrays to further reduce the computer time. The temporary variables were stored in the doubleprecision Single Instruction Multiple Data (SIMD) registers within the CPU to minimize computer memory access. A further reduction in the computer time is achieved by using the computer graphics card Graphics Processor Unit (GPU) as a highly parallel mathematical coprocessor. This makes it possible to carry out the calculations for 512 addon arrays in parallel using the GPU. The changes reduce the computer time by more than two orders of magnitude. The algorithm used to generate an optimized data set adds a specified number of new array configurations after each iteration to the existing set. The resolution of the optimized data set can be increased by adding a smaller number of new array configurations after each iteration. Although this increases the computer time required to generate an optimized data set with the same number of data points, the new fast numerical routines has made this practical on commonly available microcomputers.
Item Type:  Publication  Article 

Digital Object Identifier (DOI):  10.1111/j.1365246X.2010.04796.x 
Programmes:  BGS Programmes 2010 > Geoscience Technologies 
NORA Subject Terms:  Computer Science Physics Earth Sciences Mathematics 
Date made live:  04 Jan 2011 09:49 
URI:  http://nora.nerc.ac.uk/id/eprint/12869 
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