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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Main Authors: Huan Ma, Handong Tan, Yue Guo
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2015/464793
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spelling 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
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AT handongtan threedimensionalinducedpolarizationparallelinversionusingnonlinearconjugategradientsmethod
AT yueguo threedimensionalinducedpolarizationparallelinversionusingnonlinearconjugategradientsmethod
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