In least squares migration (LSM), multiples are usually a type of noise. Although they contain information about underground structures, they also cause artifacts in imaging. Therefore, multiple attenuation is an important way to reduce these artifacts in LSM images. Reweighted least squares reverse time migration (RWLSRTM) can use the weighting matrix and the predicted multiples to eliminate artifacts. Because the LSM provides a high resolution model, we can predict the internal multiples by using high-order Born modeling. The method is based on the inverse scattering series (ISS), and the difference is that it forwards the modeling of the internal multiples in the time domain; the model is constructed by the RWLSRTM. Because this method does not require performing as many Fourier transforms as the ISS method, it requires less calculation. We have applied the predicted multiples in the RWLSRTM to remove the artifacts caused by the multiples. The RWLSRTM image can also serve as a parameter of multiple predictions and can make the results of multiple predictions more accurate. The results of numerical tests using synthetic data show that this method can remove artifacts of internal multiples well. A comparison with the ISS method shows that our method can reduce the calculation.
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The data-driven internal multiple elimination (IME) method based on feedback model, which includes CFP-based, surface-based and inversion-based methods, are successfully applied to marine datasets. However, these methods are computationally expensive and not always straightforward on land datasets. In this paper, we first proved that the surface-based IME method, which is the most computationally efficient method among the three methods, can be derived from the CFP theory. Then we extend it to CMP domain under the assumption of locally lateral invariance of the earth, which makes it more computationally efficient. In addition, we proposed applying a time-variant taper based on the first Fresnel zone to predict the multiples more percisely. Besides, the improved S/N ratio and dense offset distribution can be obtained by using the CMP supergather, which makes the CMP-oriented method more suitable for land data. Some practical processing strategies are proposed via case study. The effectiveness of the proposed method is demonstrated with the application to synthetic and field data.
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