Global optimization with model-space preconditioning: Application to AVO inversion

Abstract

Linearized-inversion methods often have the disadvantage of dependence on the initial model. When the initial model is far from the global minimum, optimization is likely to converge to a local minimum. Optimization problems involving nonlinear relationships between data and model are likely to have more than one local minimum. Such problems are solved effectively by using global-optimization methods, which are exhaustive search techniques and hence are computationally expensive. As model dimensionality increases, the search space becomes large, making the algorithm very slow in convergence. We propose a new approach to the global-optimization scheme that incorporates a priori knowledge in the algorithm by preconditioning the model space using edge-preserving smoothing operators. Such nonlinear operators acting on the model space favorably precondition or bias the model space for blocky solutions. This approach not only speeds convergence but also retrieves blocky solutions. We apply the algorithm to estimate the layer parameters from the amplitude-variation-with-offset data. The results indicate that global optimization with model-space-preconditioning operators provides faster convergence and yields a more accurate blocky-model solution that is consistent with a priori information.

Simulated Annealing for CSAMT 1 Dimension Inversion

“…In this paper, fast simulated annealing was adopted [7][8][9]. The number of parameters need to calculate is X , and the parameters are expressed by …”

“…Simulated annealing was used in mathematical optimization by Kirkpatrick in 1983, which needn't calculate Jacobian matrix, is less dependent on starting model, easily jump out of local minima and converge to a global minimum [7][8][9]. In this paper, we try to use simulated annealing for electric field 1D inversion CSAMT data.…”

Non‐linear prestack seismic inversion with global optimization using an edge‐preserving smoothing filter

“…We followed the over‐parametrization approach (Sen and Stoffa 1991; Misra and Sacchi 2008) to parametrize the model space. As a simple example, we represent a three‐layer model in terms of a 30‐microlayer model.…”

An improved particle swarm optimization algorithm for AVO elastic parameter inversion problem

“…Mallick applied GA to perform AVO inversion . Misra and Sacchi solved a boundary‐protection‐based prestack AVO inversion problem using the fast simulated annealing (FSA) algorithm with favorable results . Lu et al also conducted an inversion of some key parameters using an improved Simulated Annealing (SA) algorithm, making the inversion precision effectively enhanced .…”