Improved preconditioned conjugate gradient algorithm and application in 3D inversion of gravity-gradiometry data
Improved preconditioned conjugate gradient algorithm and application in 3D inversion of gravity-gradiometry data
复制标题
改进的预处理共轭梯度算法及其在重力梯度数据三维反演中的应用
DOI：
10.1007/s11770-017-0625-x
复制
发表时间：
2017
影响因子：
0.7
通讯作者：
Li Ye
中科院分区：
文献类型：
--
作者：
Wang Tai-Han;Huang Da-Nian;Ma Guo-Qing;Meng Zhao-Hai;Li Ye
随着全张量梯度仪（FTG）测量技术的不断发展，FTG数据的三维反演在油气勘探中的应用越来越广泛。在大规模高精度数据的快速处理和解释中，图形处理单元（GPU）的使用和预处理方法在数据反演中非常重要。本文将对称连续过松弛（SSOR）技术与不完全Choleksy分解共轭梯度算法（ICCG）相结合，提出了一种改进的预条件共轭梯度算法。由于预处理需要额外的时间，提出了一种基于GPU的并行实现方法。将改进后的方法应用于噪声污染合成数据的反演，验证了该方法在三维FTG数据反演中的适应性。结果表明，基于NVIDIA Tesla C2050 GPU的并行SSOR-ICCG算法的加速速度是使用2.0 GHz中央处理器（CPU）串行程序的25倍左右。本文还考虑了来自美国路易斯安那州西南部温顿盐丘的实际机载重力梯度测量数据。得到了较好的结果，验证了该方法在三维FTG数据快速反演中的有效性和可行性。
With the continuous development of full tensor gradiometer (FTG) measurement techniques, three-dimensional (3D) inversion of FTG data is becoming increasingly used in oil and gas exploration. In the fast processing and interpretation of large-scale high-precision data, the use of the graphics processing unit process unit (GPU) and preconditioning methods are very important in the data inversion. In this paper, an improved preconditioned conjugate gradient algorithm is proposed by combining the symmetric successive over-relaxation (SSOR) technique and the incomplete Choleksy decomposition conjugate gradient algorithm (ICCG). Since preparing the preconditioner requires extra time, a parallel implement based on GPU is proposed. The improved method is then applied in the inversion of noisecontaminated synthetic data to prove its adaptability in the inversion of 3D FTG data. Results show that the parallel SSOR-ICCG algorithm based on NVIDIA Tesla C2050 GPU achieves a speedup of approximately 25 times that of a serial program using a 2.0 GHz Central Processing Unit (CPU). Real airborne gravity-gradiometry data from Vinton salt dome (southwest Louisiana, USA) are also considered. Good results are obtained, which verifies the efficiency and feasibility of the proposed parallel method in fast inversion of 3D FTG data.