Яндекс.Метрика

AndreyBakulin,AhmadRamdani, Dmitry Neklyudov, Ilya Silvestrov

Выпуск: 10 , Том: 42 , Год издания: 2023
Сериальное издание: Leading Edge
Страницы: 683-694

Аннотация

Complex scattering in the near surface can introduce significant distortions in deep reflection data. To model and explain these effects, a multiplicative random noise model based on the speckle mechanism of small-scale scattering has been proposed. While this model effectively captures the observed phenomena in field data, it has been considered rather abstract as it relies on random mathematical clutter to replicate the distortions. This study goes beyond by delving into the analysis of the actual meter-scale geologic heterogeneity found in carbonate formations from desert environments. By employing elastic wave propagation simulations, we show that geologic heterogeneity is equally capable of generating the observed speckle noise in field data when compared to idealized mathematical clutter. Our simulations reveal that the phase perturbations exhibit a quasi-random nature and follow a symmetric near-normal distribution, thereby supporting the validity of the multiplicative noise model and aligning with field observations. Furthermore, we discover that the spread or standard deviation of phase perturbations increases with frequency. This finding provides a plausible explanation for the loss of higher frequencies commonly seen in our data. By considering the complex waveform distortions induced by near-surface heterogeneity, our new noise model represents a significant advancement over residual statics that only account for the kinematic aspect. In summary, our study shows that geologic heterogeneity can easily generate the speckle noise observed in field data. The complex waveform distortions can be captured using quasi-random phase perturbations, as the multiplicative noise model outlines. This advancement leads to a more comprehensive understanding of the influence of near-surface heterogeneity on seismic data. Consequently, this understanding serves as a foundation for despeckling deep reflection data and enhancing the resolution of seismic imaging. These findings have significant implications for improving the quality and accuracy of seismic imaging in areas where speckle noise dominates.
индекс в базе ИАЦ: 038580