LAUSR

In this article, we proposed a weighted stacking algorithm for obtaining high-resolution data and constructed an optimization objective function using the similarity of the stacked amplitude spectrum and constant (amplitude spectrum of impulse function). The optimization problem is solved to obtain the stacking weights involved in the common midpoint (CMP) gathers by using the alternating iterative method of gradient descent and subgradient descent. Then, the weighted stacking is performed to obtain resolution-enhanced data. A traditional poststack deconvolution algorithm decomposes the data and performs frequency-weighted recombination, which alters the original frequency composition. Furthermore, most existing methods require wavelet estimation, which may be inaccurate. The amplitude-spectrum shape of CMP gather controls the resolution-enhanced data using the proposed method, which is a stacking scheme that does not require wavelet estimation. On the other hand, our proposed stacking algorithm can handle data containing white noise, and we introduce a penalty term to avoid the mutual offset between the effective signals caused by negative weight, which improves the stacked signal-to-noise ratio. Applications to synthetic and field seismic datasets demonstrate that data stacked using the proposed method have higher resolution and can be more easily interpretated compared to the traditional equal-weight stacking.