Method for wavelet denoising of multi-angle prestack seismic data

When processing seismic survey data, it is desirable to reduce random noise without lowering the seismic resolution or lessening the number of seismic stacking folds. To this end, here we introduce a wavelet denoising of multi-angle (WDMA) method for prestack seismic data. Unlike the traditional method of directly stacking multi-angle gathers, our proposed WDMA method does not rely on the simple averaging of multi-angle prestack data, and when denoising the final result does not require averaging of direct stacking. Instead, WDMA first decomposes single-angle gathers through wavelet decomposition to determine local noise and obtain a detailed estimation, following which the average weighting coefficient is calculated and the data are reconstructed. Based on the above steps, we mainly studied the wavelet decomposition, optimization of the coefficient weight and weight mode, and the number of angle gathers. We used synthetic prestack angle gathers and field seismic data to validate the effectiveness of the proposed method. Even when the ratio of maximum noise amplitude to maximum effective signal amplitude was 60%, our proposed method could still achieve a good denoising effect by conducting WDMA two or more times. Compared with soft threshold wavelet denoising or the traditional stacking method, our results demonstrate that the proposed WDMA method can obtain high-quality seismic data using fewer frames of angle gathers and can simultaneously perform denoising and stacking.