Two-dimensional inversion of spectral induced polarization data using MPI parallel algorithm in data space
Traditional two-dimensional (2D) complex resistivity forward modeling is based on Poisson’s equation but spectral induced polarization (SIP) data are the coproducts of the induced polarization (IP) and the electromagnetic induction (EMI) effects. This is especially true under high frequencies, where...
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| Vydáno v: | Applied geophysics Ročník 13; číslo 1; s. 13 - 24 |
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Beijing
Chinese Geophysical Society
01.03.2016
Springer Nature B.V School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China%China Non-ferrous Metals Resource Geological Survey, Beijing 100012, China |
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| ISSN: | 1672-7975, 1993-0658 |
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| Abstract | Traditional two-dimensional (2D) complex resistivity forward modeling is based on Poisson’s equation but spectral induced polarization (SIP) data are the coproducts of the induced polarization (IP) and the electromagnetic induction (EMI) effects. This is especially true under high frequencies, where the EMI effect can exceed the IP effect. 2D inversion that only considers the IP effect reduces the reliability of the inversion data. In this paper, we derive differential equations using Maxwell’s equations. With the introduction of the Cole–Cole model, we use the finite-element method to conduct 2D SIP forward modeling that considers the EMI and IP effects simultaneously. The data-space Occam method, in which different constraints to the model smoothness and parametric boundaries are introduced, is then used to simultaneously obtain the four parameters of the Cole—Cole model using multi-array electric field data. This approach not only improves the stability of the inversion but also significantly reduces the solution ambiguity. To improve the computational efficiency, message passing interface programming was used to accelerate the 2D SIP forward modeling and inversion. Synthetic datasets were tested using both serial and parallel algorithms, and the tests suggest that the proposed parallel algorithm is robust and efficient. |
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| AbstractList | Traditional two-dimensional (2D) complex resistivity forward modeling is based on Poisson's equation but spectral induced polarization (SIP) data are the coproducts of the induced polarization (IP) and the electromagnetic induction (EMI) effects. This is especially true under high frequencies, where the EMI effect can exceed the IP effect. 2D inversion that only considers the IP effect reduces the reliability of the inversion data. In this paper, we derive differential equations using Maxwell's equations. With the introduction of the Cole-Cole model, we use the finite-element method to conduct 2D SIP forward modeling that considers the EMI and IP effects simultaneously. The data-space Occam method, in which different constraints to the model smoothness and parametric boundaries are introduced, is then used to simultaneously obtain the four parameters of the Cole--Cole model using multi-array electric field data. This approach not only improves the stability of the inversion but also significantly reduces the solution ambiguity. To improve the computational efficiency, message passing interface programming was used to accelerate the 2D SIP forward modeling and inversion. Synthetic datasets were tested using both serial and parallel algorithms, and the tests suggest that the proposed parallel algorithm is robust and efficient. Traditional two-dimensional (2D) complex resistivity forward modeling is based on Poisson’s equation but spectral induced polarization (SIP) data are the coprod-ucts of the induced polarization (IP) and the electromagnetic induction (EMI) effects. This is especially true under high frequencies, where the EMI effect can exceed the IP effect. 2D inversion that only considers the IP effect reduces the reliability of the inver-sion data. In this paper, we derive differential equations using Maxwell’s equations. With the introduction of the Cole–Cole model, we use thefi nite-element method to conduct 2D SIP forward modeling that considers the EMI and IP effects simultaneously. The data-space Occam method, in which different constraints to the model smoothness and parametric boundaries are introduced, is then used to simultaneously obtain the four parameters of the Cole–Cole model using multi-array electricfi eld data. This approach not only improves the stability of the inversion but also signifi cantly reduces the solution ambiguity. To improve the computational effi ciency, message passing interface program-ming was used to accelerate the 2D SIP forward modeling and inversion. Synthetic da-tasets were tested using both serial and parallel algorithms, and the tests suggest that the proposed parallel algorithm is robust and effi cient. |
| Author | Tan, Han-Dong Zhang, Zhi-Yong Zhang, Bin Lin, Chang-Hong Xie, Mao-Bi Wang, Kun-Peng |
| AuthorAffiliation | School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China%China Non-ferrous Metals Resource Geological Survey, Beijing 100012, China |
| AuthorAffiliation_xml | – name: School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China%China Non-ferrous Metals Resource Geological Survey, Beijing 100012, China |
| Author_xml | – sequence: 1 givenname: Zhi-Yong surname: Zhang fullname: Zhang, Zhi-Yong organization: School of Geophysics and Information Technology, China University of Geosciences, Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of Education – sequence: 2 givenname: Han-Dong surname: Tan fullname: Tan, Han-Dong email: thd@cugb.edu.cn organization: School of Geophysics and Information Technology, China University of Geosciences, Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of Education – sequence: 3 givenname: Kun-Peng surname: Wang fullname: Wang, Kun-Peng organization: School of Geophysics and Information Technology, China University of Geosciences, Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of Education – sequence: 4 givenname: Chang-Hong surname: Lin fullname: Lin, Chang-Hong organization: School of Geophysics and Information Technology, China University of Geosciences, Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of Education – sequence: 5 givenname: Bin surname: Zhang fullname: Zhang, Bin organization: China Non-ferrous Metals Resource Geological Survey – sequence: 6 givenname: Mao-Bi surname: Xie fullname: Xie, Mao-Bi organization: School of Geophysics and Information Technology, China University of Geosciences, Key Laboratory of Geo-detection (China University of Geosciences, Beijing), Ministry of Education |
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| CitedBy_id | crossref_primary_10_1016_j_pce_2025_103891 crossref_primary_10_1016_j_compag_2019_105058 crossref_primary_10_1007_s11770_017_0627_8 crossref_primary_10_1007_s11770_016_0581_x |
| Cites_doi | 10.1190/1.1444740 10.1016/S1385-8947(99)00135-7 10.1190/1.1442303 10.1111/j.1365-246X.1994.tb02121.x 10.1190/1.1440839 10.1007/s10040-011-0819-x 10.1186/BF03352229 10.1190/1.2431635 10.1111/j.1365-246X.2007.03663.x 10.1029/94JC01894 10.1111/j.1365-2478.2006.00537.x 10.1111/j.1365-246X.2011.05025.x 10.1016/j.pepi.2004.08.023 10.5194/hessd-10-5315-2013 10.1007/s10712-005-1836-x 10.1190/1.1444778 10.1007/s12665-014-3993-z 10.1029/2012JB009543 |
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| DOI | 10.1007/s11770-016-0530-8 |
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| Keywords | MPI parallel computation data-space method 2D inversion Spectral induced polarization Cole—Cole model Cole-Cole model |
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| SubjectTerms | Algorithms Differential equations Earth and Environmental Science Earth Sciences Electric resistance Electromagnetic interference Finite element analysis Geophysics Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Induced polarization Inversions Mathematical models Modelling Polarization Spectra Two dimensional |
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| Title | Two-dimensional inversion of spectral induced polarization data using MPI parallel algorithm in data space |
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