其他摘要：In this work we present a new inversion method to obtain AVA high-resolution attributes from prestack seismic data. The method aims to find a series of sparse reflectors that, when convolved with the source wavelet, fit the observed data. To perform the inversion, we propose the use of the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). FISTA, which can be viewed as an extension of the classical gradient algorithm, provides sparse solutions minimizing both the misfit between the modeled and the observed data, and the l1-norm of the solution. The advantage of FISTA over other methods is that no inversion over any matrix is needed, making it numerically stable, easy to apply, economic in computational terms, and adequate for solving large-scale problems even with dense matrix data. Results on synthetic and field data show that the proposed method is capable to provide high-resolution AVA attributes that honor the observed data under noisy conditions, making it an interesting alternative to other known methods.
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