3D Gravity Inversion with Correlation Image in Space Domain and Application to the Northern Sinai Peninsula

Authors

  • Xu Zhang Shanghai Shannan Investigation & Design Co.,Ltd. , Shanghai, 201206, China
  • Peng Yu State Key Laboratory of Marine Geology, Tongji University, Shanghai, 200092, China
  • Jian Wang Key Laboratory of Crustal Dynamics, Institute of Crustal Dynamics, China Earthquake Administration, Beijing, 100085, China

DOI:

https://doi.org/10.30564/jgr.v1i2.845

Abstract

We present a 3D inversion method to recover density distribution from gravity data in space domain. Our method firstly employs 3D correlation image of the vertical gradient of gravity data as a starting model to generate a higher resolution image for inversion. The 3D density distribution is then obtained by inverting the correlation image of gravity data to fit the observed data based on classical inversion method of the steepest descent method. We also perform the effective equivalent storage and subdomain techniques in the starting model calculation, the forward modeling and the inversion procedures, which allow fast computation in space domain with reducing memory consumption but maintaining accuracy. The efficiency and stability of our method is demonstrated on two sets of synthetic data and one set of the Northern Sinai Peninsula gravity data. The inverted 3D density distributions show that high density bodies beneath Risan Aniza and low density bodies exist to the southeast of Risan Aniza at depths between 1~10 and 20 km, which may be originated from hot anomalies in the lower crust. The results show that our inversion method is useful for 3D quantitative interpretation.

Keywords:

3D gravity inversion; Space domain; Correlation image; Effective equivalent storage; Subdomain technique; Northern Sinai Peninsula

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How to Cite

Zhang, X., Yu, P., & Wang, J. (2019). 3D Gravity Inversion with Correlation Image in Space Domain and Application to the Northern Sinai Peninsula. Journal of Geological Research, 1(2), 9–18. https://doi.org/10.30564/jgr.v1i2.845

Issue

Vol. 1 , Iss. 2 (July 2019)

Article Type

Articles

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Copyright © 2019 Xu Zhang, Peng Yu, Jian Wang

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