Constrained inversion of gravity fields for complex 3-D structures

Geophysics ◽  
2001 ◽  
Vol 66 (2) ◽  
pp. 501-510 ◽  
Author(s):  
Roberto A. V. Moraes ◽  
R. O. Hansen

As part of a research program to develop gravity interpretation tools that can be merged with seismic techniques, a full 3-D complex structural inversion scheme for (possibly multibody) polyhedral models has been developed. The forward modeling algorithm was adopted from previous work. Because the inverse problem is generally very ill posed, several methods of regularizing the inversion were investigated and a combination of the most useful was adopted. The combination includes (i) a structured matrix formulation for the system equations, (ii) an analytical expression for the Jacobian calculation, (iii) first‐derivative damping, (iv) a choice of damping parameter based on a variation of the trust region method, (v) a weighted scheme for parameter correction, and (vi) complete freezing of degrees of freedom found not to influence the gravity field significantly. This combination yields a robust inversion which was successfully demonstrated on data over the Galveston Island salt dome, offshore Texas. Variations of the technique should be applicable to magnetic data, which would make the method useful for mining problems and petroleum exploration settings involving volcanic structures.

2019 ◽  
Author(s):  
S Crisci ◽  
M Piana ◽  
V Ruggiero ◽  
M Scussolini

AbstractParametric imaging of nuclear medicine data exploits dynamic functional images in order to reconstruct maps of kinetic parameters related to the metabolism of a specific tracer injected in the biological tissue. From a computational viewpoint, the realization of parametric images requires the pixel-wise numerical solution of compartmental inverse problems that are typically ill-posed and nonlinear. In the present paper we introduce a fast numerical optimization scheme for parametric imaging relying on a regularized version of the standard affine-scaling Trust Region method. The validation of this approach is realized in a simulation framework for brain imaging and comparison of performances is made with respect to a regularized Gauss-Newton scheme and a standard nonlinear least-squares algorithm.


Author(s):  
Morteza Kimiaei

AbstractThis paper discusses an active set trust-region algorithm for bound-constrained optimization problems. A sufficient descent condition is used as a computational measure to identify whether the function value is reduced or not. To get our complexity result, a critical measure is used which is computationally better than the other known critical measures. Under the positive definiteness of approximated Hessian matrices restricted to the subspace of non-active variables, it will be shown that unlimited zigzagging cannot occur. It is shown that our algorithm is competitive in comparison with the state-of-the-art solvers for solving an ill-conditioned bound-constrained least-squares problem.


2011 ◽  
Vol 141 ◽  
pp. 92-97
Author(s):  
Miao Hu ◽  
Tai Yong Wang ◽  
Bo Geng ◽  
Qi Chen Wang ◽  
Dian Peng Li

Nonlinear least square is one of the unconstrained optimization problems. In order to solve the least square trust region sub-problem, a genetic algorithm (GA) of global convergence was applied, and the premature convergence of genetic algorithms was also overcome through optimizing the search range of GA with trust region method (TRM), and the convergence rate of genetic algorithm was increased by the randomness of the genetic search. Finally, an example of banana function was established to verify the GA, and the results show the practicability and precision of this algorithm.


Computing ◽  
2011 ◽  
Vol 92 (4) ◽  
pp. 317-333 ◽  
Author(s):  
Gonglin Yuan ◽  
Zengxin Wei ◽  
Xiwen Lu

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Yunlong Lu ◽  
Weiwei Yang ◽  
Wenyu Li ◽  
Xiaowei Jiang ◽  
Yueting Yang

A new trust region method is presented, which combines nonmonotone line search technique, a self-adaptive update rule for the trust region radius, and the weighting technique for the ratio between the actual reduction and the predicted reduction. Under reasonable assumptions, the global convergence of the method is established for unconstrained nonconvex optimization. Numerical results show that the new method is efficient and robust for solving unconstrained optimization problems.


2021 ◽  
Vol 14 (1) ◽  
pp. 418-439
Author(s):  
S. Crisci ◽  
M. Piana ◽  
V. Ruggiero ◽  
M. Scussolini

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