Joint Inversion of 1-D Magnetotelluric and Surface-Wave Dispersion Data with an Improved Multi-Objective Genetic Algorithm and Application to the Data of the Longmenshan Fault Zone
- 86 Downloads
Magnetotellurics and seismic surface waves are two prominent geophysical methods for deep underground exploration. Joint inversion of these two datasets can help enhance the accuracy of inversion. In this paper, we describe a method for developing an improved multi-objective genetic algorithm (NSGA–SBX) and applying it to two numerical tests to verify the advantages of the algorithm. Our findings show that joint inversion with the NSGA–SBX method can improve the inversion results by strengthening structural coupling when the discontinuities of the electrical and velocity models are consistent, and in case of inconsistent discontinuities between these models, joint inversion can retain the advantages of individual inversions. By applying the algorithm to four detection points along the Longmenshan fault zone, we observe several features. The Sichuan Basin demonstrates low S-wave velocity and high conductivity in the shallow crust probably due to thick sedimentary layers. The eastern margin of the Tibetan Plateau shows high velocity and high resistivity in the shallow crust, while two low velocity layers and a high conductivity layer are observed in the middle lower crust, probably indicating the mid-crustal channel flow. Along the Longmenshan fault zone, a high conductivity layer from ~ 8 to ~ 20 km is observed beneath the northern segment and decreases with depth beneath the middle segment, which might be caused by the elevated fluid content of the fault zone.
KeywordsMagnetotellurics surface wave joint inversion NSGA–SBX
We would like to thank Dr. Li Hongyi for proving the group velocity dispersion data for this study. We also thank the editor and the anonymous reviewers whose comments and suggestions have contributed greatly to the improvement of the original manuscript. This study is supported by the National Science Foundation (NSF) of China (Grants 41704055).
- Kalyanmoy, D., & Agarwal, R. B. (1995). Simulated binary crossover for continuous search space. Complex Systems, 9, 115–148.Google Scholar
- Knowles J, Corne D (1999) The Pareto archived evolution strategy: A new baseline algorithm for multiobjective optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, pp. 98–105.Google Scholar
- Moorkamp, M., Jones, A., & Fishwick, S. (2011). Joint inversion of receiver functions, surface wave dispersion and magnetotelluric data. Journal of Geophysical Research Solid Earth, 115(B4), B04318.Google Scholar
- Peng, M., Jiang, M., Tan, H. D., Li, Q. Q., Zhang, L. S., Xu, L. H., et al. (2015). Electrical structure of the crust beneath the central-northern segment of the LongMen Shan fault one and its geodynamic model. Seismology and Geology, 37(1), 162–175.Google Scholar
- Tikhonov, A. N. (1950). On the determination of electric characteristics of deep layers of the earth’s crust. Doklady Akademii Nauk, 73(2), 295–297.Google Scholar
- Wang, X. B., Zhu, Y. T., Zhao, X. K., et al. (2009). Deep conductivity characteristics of the Longmen Shan, Eastern Qinghai–Tibet Plateau. Chinese Journal of Geophysics, 52(2), 564–571. (in Chinese).Google Scholar
- Wu, J. P., Huang, Y., Zhang, T. Z., Ming, Y. H., & Fang, L. H. (2009). After shock distribution of the Ms 8.0 Wenchuan earthquake and 3-D P wave velocity structure in and around source region. Journal of Geophysics, 52(1), 102–111. (in Chinese).Google Scholar
- Zitzler, E. (1999). Evolutionary algorithms for multiobjective optimization: Methods and applications, Doctoral dissertation ETH 13398. Zurich: Swiss Federal Institute of Technology (ETH).Google Scholar