Abstract
The paper describes a method of gravity data inversion, which is based on parallel algorithms. The choice of the density model of the initial approximation and the set on which the solution is sought guarantees the stability of the algorithms. We offer a new upward and downward continuation algorithm for separating the effects of shallow and deep sources. Using separated field of layers, the density distribution is restored in a form of 3D grid. We use the iterative parallel algorithms for the downward continuation and restoration of the density values (by solving the inverse linear gravity problem). The algorithms are based on the ideas of local minimization; they do not require a nonlinear minimization; they are easier to implement and have better stability. We also suggest an optimization of the gravity field calculation, which speeds up the inversion. A practical example of interpretation is presented for the gravity data of the Urals region, Russia.
Highlights
Gravity data inversion is the main tool for obtaining the Earth’s crust density model
The method was applied to real potential field data, and the results show that the proposed method accomplishes the downward continuation of the real data, stably
The paper is organized as follows: we present new mathematical algorithms for downward continuation of the gravity field and the 3D inverse problem, and both algorithms are applied to the gravity anomalies of the Middle Urals region, Russia
Summary
Gravity data inversion is the main tool for obtaining the Earth’s crust density model. The process of constructing density models based on gravity field anomalies proposed here contains five major steps: the fast forward modeling algorithm; the initial 3D density model creation; the upward and downward continuation (the extraction of the gravity fields of layers); the choice of the density as multiplicative function; and the stable adaptive algorithm for the inverse problem solving. The optimum regularization parameter value is selected as a local minimum of constructed Lp-norms functions-in the majority of cases They demonstrate excellent stabilizing properties of this method on several synthetic models and one real-world example from high-definition magnetometry. The spectral combination method requires no matrix inversion Another example of applying regularization to inversion of potential field data is presented in [8]. The paper is organized as follows: we present new mathematical algorithms for downward continuation of the gravity field and the 3D inverse problem, and both algorithms are applied to the gravity anomalies of the Middle Urals region, Russia
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