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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This work is jointly sponsored by the National Natural Science Foundation of China (Grant No. 41374078), the Geological Survey Projects of the Ministry of Land and Resources of China (Grant Nos. 12120113086100 and 12120113101300), and Beijing Higher Education Young Elite Teacher Project.
Zhang Zhi-Yong, He received his M.S. in 2013 from China University of Geosciences, Beijing. Currently, he is a Ph.D. student at the School of Geophysics and Information Technology, China University of Geosciences, Beijing. His main research interests are electromagnetic forward modeling and inversion.
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Zhang, ZY., Tan, HD., Wang, KP. et al. Two-dimensional inversion of spectral induced polarization data using MPI parallel algorithm in data space. Appl. Geophys. 13, 13–24 (2016). https://doi.org/10.1007/s11770-016-0530-8
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DOI: https://doi.org/10.1007/s11770-016-0530-8