Three-dimensional gravity inversion plays a vital role in the quantitative interpretation of practical gravity data. One of the key issues with 3D inversion of gravity data is the computational efficiency. Here, based on the proximal objective function optimization (POFO) algorithm, we have developed an efficient 3D inversion method of gravity data to enhance the computational efficiency. Our proposed inversion method calculates each unknown parameter in the model vector one by one during iteration, so our method has low computational complexity and high efficiency. In addition, a long-tailed Cauchy distribution is incorporated into the inversion objective function to obtain sparse solution. Both synthetic example and field data demonstrate the feasibility and reliability of our method.


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