Abstract
We propose a new 3D inversion scheme to invert the near- and transition-zone data of CSAMT with topography accurately. In this new method, the earth was discretized into unstructured tetrahedra to fit the ragged topography and the vector finite-element method was adopted to obtain precise responses and good sensitivity. To simulate the attitude and shape of the transmitter, we divided a long-grounded transmitter into dipoles and integrated these dipoles to obtain good responses in the near- and transition-field zones. Next, we designed an L2 norm-based objective functional and applied a standard quasi-Newton method as the optimization method to solve the inverse problem and guarantee steady convergence. We tested our 3D inversion method first on synthetic data and then on a field dataset acquired from select sites near Changbai Mountain, China. In both tests, the new inversion algorithm achieved excellent fitting between the predicted and observed data, even in near- and transition-field zones, and the inversion results agreed well with the true model. These findings reveal that the proposed algorithm is effective for 3D inversion of CSAMT data.
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We thank the reviewers for their constructive comments.
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The research is financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA14020102), the National Natural Science Foundation of China (Nos. 41774125, 41530320, 41904104), the Key National Research Project of China (No. 2018YFC0603300), and the S&T Program of Beijing (No. Z181100005718001).
Chen Xiang-Zhong received bachelor’s and master’s degree from the College of Geo-Exploration Science and Technology of Jilin University in 2006 and 2016, respectively, he is currently studying for a doctoral degree from the College of Geo-Exploration Science and Technology of Jilin University. Mainly engaged in geophysical electromagnetic theory and application.
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Chen, XZ., Liu, YH., Yin, CC. et al. Three-dimensional inversion of controlled-source audio-frequency magnetotelluric data based on unstructured finite-element method. Appl. Geophys. 17, 349–360 (2020). https://doi.org/10.1007/s11770-020-0812-z
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DOI: https://doi.org/10.1007/s11770-020-0812-z