[an error occurred while processing this directive] Global Geology 2019, 22(3) 179-187 DOI:   10.3969/j.issn.1673-9736.2019.03.05  ISSN: 1673-9736 CN: 22-1371/P
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large-scale gravity data
GPU parallel computing
CUDA
equivalent geometric trellis
focusing inversion
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PubMed
Regularized focusing inversion for large-scale gravity data based on GPU parallel computing
WANG Haoran1, DING Yidan1, LI Feida2, LI Jing1
1. College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China;
2. Jilin Geophysics Prospecting Institude, Changchun 130062, China
ժҪ�� Processing large-scale 3-D gravity data is an important topic in geophysics field. Many existing inversion methods lack the competence of processing massive data and practical application capacity. This study proposes the application of GPU parallel processing technology to the focusing inversion method, aiming at improving the inversion accuracy while speeding up calculation and reducing the memory consumption, thus obtaining the fast and reliable inversion results for large complex model. In this paper, equivalent storage of geometric trellis is used to calculate the sensitivity matrix, and the inversion is based on GPU parallel computing technology. The parallel computing program that is optimized by reducing data transfer, access restrictions and instruction restrictions as well as latency hiding greatly reduces the memory usage, speeds up the calculation, and makes the fast inversion of large models possible. By comparing and analyzing the computing speed of traditional single thread CPU method and CUDA-based GPU parallel technology, the excellent acceleration performance of GPU parallel computing is verified, which provides ideas for practical application of some theoretical inversion methods restricted by computing speed and computer memory. The model test verifies that the focusing inversion method can overcome the problem of severe skin effect and ambiguity of geological body boundary. Moreover, the increase of the model cells and inversion data can more clearly depict the boundary position of the abnormal body and delineate its specific shape.
�ؼ����� large-scale gravity data   GPU parallel computing   CUDA   equivalent geometric trellis   focusing inversion  
Regularized focusing inversion for large-scale gravity data based on GPU parallel computing
WANG Haoran1, DING Yidan1, LI Feida2, LI Jing1
1. College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China;
2. Jilin Geophysics Prospecting Institude, Changchun 130062, China
Abstract: Processing large-scale 3-D gravity data is an important topic in geophysics field. Many existing inversion methods lack the competence of processing massive data and practical application capacity. This study proposes the application of GPU parallel processing technology to the focusing inversion method, aiming at improving the inversion accuracy while speeding up calculation and reducing the memory consumption, thus obtaining the fast and reliable inversion results for large complex model. In this paper, equivalent storage of geometric trellis is used to calculate the sensitivity matrix, and the inversion is based on GPU parallel computing technology. The parallel computing program that is optimized by reducing data transfer, access restrictions and instruction restrictions as well as latency hiding greatly reduces the memory usage, speeds up the calculation, and makes the fast inversion of large models possible. By comparing and analyzing the computing speed of traditional single thread CPU method and CUDA-based GPU parallel technology, the excellent acceleration performance of GPU parallel computing is verified, which provides ideas for practical application of some theoretical inversion methods restricted by computing speed and computer memory. The model test verifies that the focusing inversion method can overcome the problem of severe skin effect and ambiguity of geological body boundary. Moreover, the increase of the model cells and inversion data can more clearly depict the boundary position of the abnormal body and delineate its specific shape.
Keywords: large-scale gravity data   GPU parallel computing   CUDA   equivalent geometric trellis   focusing inversion  
�ո����� 2019-06-03 �޻����� 2019-07-02 ����淢������  
DOI: 10.3969/j.issn.1673-9736.2019.03.05
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Supported by Project of National Natural Science Foundation (No.41874134)

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1��WANG Xusheng, ZENG Zhaofa.3D fast inversion of gravity data based on GPU[J]. Global Geology, 2018,21(2): 114-119
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