Sparse-spike AVO/AVA attributes from prestack data
Daniel Omar Pérez, Danilo R. Velis · 2011
We present a new inversion method for obtaining sparse-spike AVO/AVA attributes from prestack seismic data. The proposed method aims to find the smallest number of reflectors that, when convolved with the source wavelet, fit the data. It is an extension to prestack data of an earlier work on sparse-spike deconvolution that was applied to poststack data. The proposed strategy provides high-resolution AVO attributes such as intercept and gradient. In this work, and for the sake of simplicity, we consider the classical Shuey's two- and three-terms approaches, but the use of other approximations (e.g. Aki & Richards) is immediate. Due to the high nonlinearity of the inverse problem, which includes the determination of the time location of a given number of reflectors, we use the global optimization algorithm known as simulated annealing. The coefficients of the Shuey's approximations are obtained after solving a small system of linear equations, a step that guarantees optimal least-squares solutions at each annealing iteration. Results using synthetic and field data show that the proposed method is very robust under noisy conditions even when the number of reflectors is not known a priori and the utilized wavelet is inaccurate. One advantage of the method is that the uncertainty of the solutions can be estimated stochastically, taking advantage of the large number of solutions that are tested during the inversion process.