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Pure and Applied Geophysics

, Volume 175, Issue 10, pp 3591–3604 | Cite as

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

  • Pingping Wu
  • Handong Tan
  • Miao Peng
  • Huan Ma
  • Mao Wang
Article
  • 86 Downloads

Abstract

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.

Keywords

Magnetotellurics surface wave joint inversion NSGA–SBX 

Notes

Acknowledgements

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).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Key Laboratory of Geo-DetectionChina University of Geosciences, Ministry of EducationBeijingChina
  2. 2.School of Geophysics and Information TechnologyChina University of GeosciencesBeijingChina
  3. 3.Institute of Disaster PreventionHebeiChina

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