Three-Dimensional Induced Polarization Parallel Inversion Using Nonlinear Conjugate Gradients Method
Four kinds of array of induced polarization (IP) methods (surface, borehole-surface, surface-borehole, and borehole-borehole) are widely used in resource exploration. However, due to the presence of large amounts of the sources, it will take much time to complete the inversion. In the paper, a new p...
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doaj-d58a4ea8d4a84da4ae70af35131c83b62020-11-24T20:59:59ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/464793464793Three-Dimensional Induced Polarization Parallel Inversion Using Nonlinear Conjugate Gradients MethodHuan Ma0Handong Tan1Yue Guo2Key Laboratory of Geo-Detection, China University of Geosciences, Ministry of Education, Beijing 100083, ChinaKey Laboratory of Geo-Detection, China University of Geosciences, Ministry of Education, Beijing 100083, ChinaExploratory Drilling Corporation Well Logging Company, Daqing, Heilongjiang 163000, ChinaFour kinds of array of induced polarization (IP) methods (surface, borehole-surface, surface-borehole, and borehole-borehole) are widely used in resource exploration. However, due to the presence of large amounts of the sources, it will take much time to complete the inversion. In the paper, a new parallel algorithm is described which uses message passing interface (MPI) and graphics processing unit (GPU) to accelerate 3D inversion of these four methods. The forward finite differential equation is solved by ILU0 preconditioner and the conjugate gradient (CG) solver. The inverse problem is solved by nonlinear conjugate gradients (NLCG) iteration which is used to calculate one forward and two “pseudo-forward” modelings and update the direction, space, and model in turn. Because each source is independent in forward and “pseudo-forward” modelings, multiprocess modes are opened by calling MPI library. The iterative matrix solver within CULA is called in each process. Some tables and synthetic data examples illustrate that this parallel inversion algorithm is effective. Furthermore, we demonstrate that the joint inversion of surface and borehole data produces resistivity and chargeability results are superior to those obtained from inversions of individual surface data.http://dx.doi.org/10.1155/2015/464793 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huan Ma Handong Tan Yue Guo |
spellingShingle |
Huan Ma Handong Tan Yue Guo Three-Dimensional Induced Polarization Parallel Inversion Using Nonlinear Conjugate Gradients Method Mathematical Problems in Engineering |
author_facet |
Huan Ma Handong Tan Yue Guo |
author_sort |
Huan Ma |
title |
Three-Dimensional Induced Polarization Parallel Inversion Using Nonlinear Conjugate Gradients Method |
title_short |
Three-Dimensional Induced Polarization Parallel Inversion Using Nonlinear Conjugate Gradients Method |
title_full |
Three-Dimensional Induced Polarization Parallel Inversion Using Nonlinear Conjugate Gradients Method |
title_fullStr |
Three-Dimensional Induced Polarization Parallel Inversion Using Nonlinear Conjugate Gradients Method |
title_full_unstemmed |
Three-Dimensional Induced Polarization Parallel Inversion Using Nonlinear Conjugate Gradients Method |
title_sort |
three-dimensional induced polarization parallel inversion using nonlinear conjugate gradients method |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2015-01-01 |
description |
Four kinds of array of induced polarization (IP) methods (surface, borehole-surface, surface-borehole, and borehole-borehole) are widely used in resource exploration. However, due to the presence of large amounts of the sources, it will take much time to complete the inversion. In the paper, a new parallel algorithm is described which uses message passing interface (MPI) and graphics processing unit (GPU) to accelerate 3D inversion of these four methods. The forward finite differential equation is solved by ILU0 preconditioner and the conjugate gradient (CG) solver. The inverse problem is solved by nonlinear conjugate gradients (NLCG) iteration which is used to calculate one forward and two “pseudo-forward” modelings and update the direction, space, and model in turn. Because each source is independent in forward and “pseudo-forward” modelings, multiprocess modes are opened by calling MPI library. The iterative matrix solver within CULA is called in each process. Some tables and synthetic data examples illustrate that this parallel inversion algorithm is effective. Furthermore, we demonstrate that the joint inversion of surface and borehole data produces resistivity and chargeability results are superior to those obtained from inversions of individual surface data. |
url |
http://dx.doi.org/10.1155/2015/464793 |
work_keys_str_mv |
AT huanma threedimensionalinducedpolarizationparallelinversionusingnonlinearconjugategradientsmethod AT handongtan threedimensionalinducedpolarizationparallelinversionusingnonlinearconjugategradientsmethod AT yueguo threedimensionalinducedpolarizationparallelinversionusingnonlinearconjugategradientsmethod |
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1716780738532081664 |